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.state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}a .nodeLabel{text-decoration:underline}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.entityTitleText{fill:var(--md-mermaid-label-fg-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}text:not([class]):last-child{fill:var(--md-mermaid-label-fg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}";var Gr,Qa=0;function Ka(){return typeof mermaid=="undefined"||mermaid instanceof Element?Tt("https://unpkg.com/mermaid@11/dist/mermaid.min.js"):I(void 0)}function Wn(e){return e.classList.remove("mermaid"),Gr||(Gr=Ka().pipe(w(()=>mermaid.initialize({startOnLoad:!1,themeCSS:Un,sequence:{actorFontSize:"16px",messageFontSize:"16px",noteFontSize:"16px"}})),m(()=>{}),G(1))),Gr.subscribe(()=>co(this,null,function*(){e.classList.add("mermaid");let t=`__mermaid_${Qa++}`,r=x("div",{class:"mermaid"}),o=e.textContent,{svg:n,fn:i}=yield mermaid.render(t,o),a=r.attachShadow({mode:"closed"});a.innerHTML=n,e.replaceWith(r),i==null||i(a)})),Gr.pipe(m(()=>({ref:e})))}var Dn=x("table");function Vn(e){return e.replaceWith(Dn),Dn.replaceWith(An(e)),I({ref:e})}function Ya(e){let t=e.find(r=>r.checked)||e[0];return O(...e.map(r=>h(r,"change").pipe(m(()=>R(`label[for="${r.id}"]`))))).pipe(Q(R(`label[for="${t.id}"]`)),m(r=>({active:r})))}function Nn(e,{viewport$:t,target$:r}){let o=R(".tabbed-labels",e),n=P(":scope > input",e),i=Kr("prev");e.append(i);let a=Kr("next");return e.append(a),C(()=>{let s=new g,p=s.pipe(Z(),ie(!0));z([s,ge(e),tt(e)]).pipe(W(p),Me(1,me)).subscribe({next([{active:c},l]){let f=Ve(c),{width:u}=ce(c);e.style.setProperty("--md-indicator-x",`${f.x}px`),e.style.setProperty("--md-indicator-width",`${u}px`);let d=pr(o);(f.xd.x+l.width)&&o.scrollTo({left:Math.max(0,f.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),z([Ne(o),ge(o)]).pipe(W(p)).subscribe(([c,l])=>{let f=St(o);i.hidden=c.x<16,a.hidden=c.x>f.width-l.width-16}),O(h(i,"click").pipe(m(()=>-1)),h(a,"click").pipe(m(()=>1))).pipe(W(p)).subscribe(c=>{let{width:l}=ce(o);o.scrollBy({left:l*c,behavior:"smooth"})}),r.pipe(W(p),b(c=>n.includes(c))).subscribe(c=>c.click()),o.classList.add("tabbed-labels--linked");for(let c of n){let l=R(`label[for="${c.id}"]`);l.replaceChildren(x("a",{href:`#${l.htmlFor}`,tabIndex:-1},...Array.from(l.childNodes))),h(l.firstElementChild,"click").pipe(W(p),b(f=>!(f.metaKey||f.ctrlKey)),w(f=>{f.preventDefault(),f.stopPropagation()})).subscribe(()=>{history.replaceState({},"",`#${l.htmlFor}`),l.click()})}return B("content.tabs.link")&&s.pipe(Ce(1),re(t)).subscribe(([{active:c},{offset:l}])=>{let f=c.innerText.trim();if(c.hasAttribute("data-md-switching"))c.removeAttribute("data-md-switching");else{let u=e.offsetTop-l.y;for(let y of P("[data-tabs]"))for(let L of P(":scope > input",y)){let X=R(`label[for="${L.id}"]`);if(X!==c&&X.innerText.trim()===f){X.setAttribute("data-md-switching",""),L.click();break}}window.scrollTo({top:e.offsetTop-u});let d=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([f,...d])])}}),s.pipe(W(p)).subscribe(()=>{for(let c of P("audio, video",e))c.pause()}),Ya(n).pipe(w(c=>s.next(c)),_(()=>s.complete()),m(c=>$({ref:e},c)))}).pipe(Ke(se))}function zn(e,{viewport$:t,target$:r,print$:o}){return O(...P(".annotate:not(.highlight)",e).map(n=>Pn(n,{target$:r,print$:o})),...P("pre:not(.mermaid) > code",e).map(n=>jn(n,{target$:r,print$:o})),...P("pre.mermaid",e).map(n=>Wn(n)),...P("table:not([class])",e).map(n=>Vn(n)),...P("details",e).map(n=>Fn(n,{target$:r,print$:o})),...P("[data-tabs]",e).map(n=>Nn(n,{viewport$:t,target$:r})),...P("[title]",e).filter(()=>B("content.tooltips")).map(n=>mt(n,{viewport$:t})))}function Ba(e,{alert$:t}){return t.pipe(v(r=>O(I(!0),I(!1).pipe(Ge(2e3))).pipe(m(o=>({message:r,active:o})))))}function qn(e,t){let r=R(".md-typeset",e);return C(()=>{let o=new g;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),Ba(e,t).pipe(w(n=>o.next(n)),_(()=>o.complete()),m(n=>$({ref:e},n)))})}var Ga=0;function Ja(e,t){document.body.append(e);let{width:r}=ce(e);e.style.setProperty("--md-tooltip-width",`${r}px`),e.remove();let o=cr(t),n=typeof o!="undefined"?Ne(o):I({x:0,y:0}),i=O(et(t),$t(t)).pipe(K());return z([i,n]).pipe(m(([a,s])=>{let{x:p,y:c}=Ve(t),l=ce(t),f=t.closest("table");return f&&t.parentElement&&(p+=f.offsetLeft+t.parentElement.offsetLeft,c+=f.offsetTop+t.parentElement.offsetTop),{active:a,offset:{x:p-s.x+l.width/2-r/2,y:c-s.y+l.height+8}}}))}function Qn(e){let t=e.title;if(!t.length)return S;let r=`__tooltip_${Ga++}`,o=Rt(r,"inline"),n=R(".md-typeset",o);return n.innerHTML=t,C(()=>{let i=new g;return i.subscribe({next({offset:a}){o.style.setProperty("--md-tooltip-x",`${a.x}px`),o.style.setProperty("--md-tooltip-y",`${a.y}px`)},complete(){o.style.removeProperty("--md-tooltip-x"),o.style.removeProperty("--md-tooltip-y")}}),O(i.pipe(b(({active:a})=>a)),i.pipe(_e(250),b(({active:a})=>!a))).subscribe({next({active:a}){a?(e.insertAdjacentElement("afterend",o),e.setAttribute("aria-describedby",r),e.removeAttribute("title")):(o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t))},complete(){o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t)}}),i.pipe(Me(16,me)).subscribe(({active:a})=>{o.classList.toggle("md-tooltip--active",a)}),i.pipe(pt(125,me),b(()=>!!e.offsetParent),m(()=>e.offsetParent.getBoundingClientRect()),m(({x:a})=>a)).subscribe({next(a){a?o.style.setProperty("--md-tooltip-0",`${-a}px`):o.style.removeProperty("--md-tooltip-0")},complete(){o.style.removeProperty("--md-tooltip-0")}}),Ja(o,e).pipe(w(a=>i.next(a)),_(()=>i.complete()),m(a=>$({ref:e},a)))}).pipe(Ke(se))}function Xa({viewport$:e}){if(!B("header.autohide"))return I(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Be(2,1),m(([n,i])=>[nMath.abs(i-n.y)>100),m(([,[n]])=>n),K()),o=ze("search");return z([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),K(),v(n=>n?r:I(!1)),Q(!1))}function Kn(e,t){return C(()=>z([ge(e),Xa(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),K((r,o)=>r.height===o.height&&r.hidden===o.hidden),G(1))}function Yn(e,{header$:t,main$:r}){return C(()=>{let o=new g,n=o.pipe(Z(),ie(!0));o.pipe(ee("active"),He(t)).subscribe(([{active:a},{hidden:s}])=>{e.classList.toggle("md-header--shadow",a&&!s),e.hidden=s});let i=ue(P("[title]",e)).pipe(b(()=>B("content.tooltips")),ne(a=>Qn(a)));return r.subscribe(o),t.pipe(W(n),m(a=>$({ref:e},a)),Re(i.pipe(W(n))))})}function Za(e,{viewport$:t,header$:r}){return mr(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=ce(e);return{active:o>=n}}),ee("active"))}function Bn(e,t){return C(()=>{let r=new g;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=fe(".md-content h1");return typeof o=="undefined"?S:Za(o,t).pipe(w(n=>r.next(n)),_(()=>r.complete()),m(n=>$({ref:e},n)))})}function Gn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),K()),n=o.pipe(v(()=>ge(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),ee("bottom"))));return z([o,n,t]).pipe(m(([i,{top:a,bottom:s},{offset:{y:p},size:{height:c}}])=>(c=Math.max(0,c-Math.max(0,a-p,i)-Math.max(0,c+p-s)),{offset:a-i,height:c,active:a-i<=p})),K((i,a)=>i.offset===a.offset&&i.height===a.height&&i.active===a.active))}function es(e){let t=__md_get("__palette")||{index:e.findIndex(o=>matchMedia(o.getAttribute("data-md-color-media")).matches)},r=Math.max(0,Math.min(t.index,e.length-1));return I(...e).pipe(ne(o=>h(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),G(1))}function Jn(e){let t=P("input",e),r=x("meta",{name:"theme-color"});document.head.appendChild(r);let o=x("meta",{name:"color-scheme"});document.head.appendChild(o);let n=Pt("(prefers-color-scheme: light)");return C(()=>{let i=new g;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),p=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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Jr=Mt(Br());function ts(e){e.setAttribute("data-md-copying","");let t=e.closest("[data-copy]"),r=t?t.getAttribute("data-copy"):e.innerText;return e.removeAttribute("data-md-copying"),r.trimEnd()}function Zn({alert$:e}){Jr.default.isSupported()&&new j(t=>{new Jr.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||ts(R(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(w(t=>{t.trigger.focus()}),m(()=>Ee("clipboard.copied"))).subscribe(e)}function ei(e,t){return e.protocol=t.protocol,e.hostname=t.hostname,e}function rs(e,t){let r=new Map;for(let o of P("url",e)){let n=R("loc",o),i=[ei(new URL(n.textContent),t)];r.set(`${i[0]}`,i);for(let a of P("[rel=alternate]",o)){let s=a.getAttribute("href");s!=null&&i.push(ei(new URL(s),t))}}return r}function ur(e){return un(new URL("sitemap.xml",e)).pipe(m(t=>rs(t,new URL(e))),de(()=>I(new Map)))}function os(e,t){if(!(e.target instanceof Element))return S;let r=e.target.closest("a");if(r===null)return S;if(r.target||e.metaKey||e.ctrlKey)return S;let o=new URL(r.href);return o.search=o.hash="",t.has(`${o}`)?(e.preventDefault(),I(new URL(r.href))):S}function ti(e){let t=new Map;for(let r of P(":scope > *",e.head))t.set(r.outerHTML,r);return t}function ri(e){for(let t of P("[href], [src]",e))for(let r of["href","src"]){let o=t.getAttribute(r);if(o&&!/^(?:[a-z]+:)?\/\//i.test(o)){t[r]=t[r];break}}return I(e)}function ns(e){for(let o of["[data-md-component=announce]","[data-md-component=container]","[data-md-component=header-topic]","[data-md-component=outdated]","[data-md-component=logo]","[data-md-component=skip]",...B("navigation.tabs.sticky")?["[data-md-component=tabs]"]:[]]){let n=fe(o),i=fe(o,e);typeof n!="undefined"&&typeof i!="undefined"&&n.replaceWith(i)}let t=ti(document);for(let[o,n]of ti(e))t.has(o)?t.delete(o):document.head.appendChild(n);for(let o of t.values()){let 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+//# sourceMappingURL=bundle.88dd0f4e.min.js.map + diff --git a/assets/javascripts/bundle.88dd0f4e.min.js.map b/assets/javascripts/bundle.88dd0f4e.min.js.map new file mode 100644 index 0000000000..dab2a8754b --- /dev/null +++ b/assets/javascripts/bundle.88dd0f4e.min.js.map @@ -0,0 +1,7 @@ +{ + "version": 3, + "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/escape-html/index.js", "node_modules/clipboard/dist/clipboard.js", "src/templates/assets/javascripts/bundle.ts", "node_modules/tslib/tslib.es6.mjs", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", "node_modules/rxjs/src/internal/util/errorContext.ts", "node_modules/rxjs/src/internal/Subscriber.ts", "node_modules/rxjs/src/internal/symbol/observable.ts", "node_modules/rxjs/src/internal/util/identity.ts", "node_modules/rxjs/src/internal/util/pipe.ts", "node_modules/rxjs/src/internal/Observable.ts", "node_modules/rxjs/src/internal/util/lift.ts", "node_modules/rxjs/src/internal/operators/OperatorSubscriber.ts", "node_modules/rxjs/src/internal/scheduler/animationFrameProvider.ts", "node_modules/rxjs/src/internal/util/ObjectUnsubscribedError.ts", "node_modules/rxjs/src/internal/Subject.ts", "node_modules/rxjs/src/internal/BehaviorSubject.ts", "node_modules/rxjs/src/internal/scheduler/dateTimestampProvider.ts", "node_modules/rxjs/src/internal/ReplaySubject.ts", "node_modules/rxjs/src/internal/scheduler/Action.ts", "node_modules/rxjs/src/internal/scheduler/intervalProvider.ts", "node_modules/rxjs/src/internal/scheduler/AsyncAction.ts", "node_modules/rxjs/src/internal/Scheduler.ts", "node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/QueueAction.ts", "node_modules/rxjs/src/internal/scheduler/QueueScheduler.ts", "node_modules/rxjs/src/internal/scheduler/queue.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", 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"node_modules/rxjs/src/internal/operators/ignoreElements.ts", "node_modules/rxjs/src/internal/operators/mapTo.ts", "node_modules/rxjs/src/internal/operators/delayWhen.ts", "node_modules/rxjs/src/internal/operators/delay.ts", "node_modules/rxjs/src/internal/operators/distinctUntilChanged.ts", "node_modules/rxjs/src/internal/operators/distinctUntilKeyChanged.ts", "node_modules/rxjs/src/internal/operators/throwIfEmpty.ts", "node_modules/rxjs/src/internal/operators/endWith.ts", "node_modules/rxjs/src/internal/operators/finalize.ts", "node_modules/rxjs/src/internal/operators/first.ts", "node_modules/rxjs/src/internal/operators/takeLast.ts", "node_modules/rxjs/src/internal/operators/merge.ts", "node_modules/rxjs/src/internal/operators/mergeWith.ts", "node_modules/rxjs/src/internal/operators/repeat.ts", "node_modules/rxjs/src/internal/operators/scan.ts", "node_modules/rxjs/src/internal/operators/share.ts", "node_modules/rxjs/src/internal/operators/shareReplay.ts", 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"src/templates/assets/javascripts/browser/viewport/_/index.ts", "src/templates/assets/javascripts/browser/viewport/at/index.ts", "src/templates/assets/javascripts/browser/worker/index.ts", "src/templates/assets/javascripts/_/index.ts", "src/templates/assets/javascripts/components/_/index.ts", "src/templates/assets/javascripts/components/announce/index.ts", "src/templates/assets/javascripts/components/consent/index.ts", "src/templates/assets/javascripts/templates/tooltip/index.tsx", "src/templates/assets/javascripts/templates/annotation/index.tsx", "src/templates/assets/javascripts/templates/clipboard/index.tsx", "src/templates/assets/javascripts/templates/search/index.tsx", "src/templates/assets/javascripts/templates/source/index.tsx", "src/templates/assets/javascripts/templates/tabbed/index.tsx", "src/templates/assets/javascripts/templates/table/index.tsx", "src/templates/assets/javascripts/templates/version/index.tsx", "src/templates/assets/javascripts/components/tooltip2/index.ts", "src/templates/assets/javascripts/components/content/annotation/_/index.ts", "src/templates/assets/javascripts/components/content/annotation/list/index.ts", "src/templates/assets/javascripts/components/content/annotation/block/index.ts", "src/templates/assets/javascripts/components/content/code/_/index.ts", "src/templates/assets/javascripts/components/content/details/index.ts", "src/templates/assets/javascripts/components/content/mermaid/index.css", "src/templates/assets/javascripts/components/content/mermaid/index.ts", "src/templates/assets/javascripts/components/content/table/index.ts", "src/templates/assets/javascripts/components/content/tabs/index.ts", "src/templates/assets/javascripts/components/content/_/index.ts", "src/templates/assets/javascripts/components/dialog/index.ts", "src/templates/assets/javascripts/components/tooltip/index.ts", "src/templates/assets/javascripts/components/header/_/index.ts", "src/templates/assets/javascripts/components/header/title/index.ts", "src/templates/assets/javascripts/components/main/index.ts", "src/templates/assets/javascripts/components/palette/index.ts", "src/templates/assets/javascripts/components/progress/index.ts", "src/templates/assets/javascripts/integrations/clipboard/index.ts", "src/templates/assets/javascripts/integrations/sitemap/index.ts", "src/templates/assets/javascripts/integrations/instant/index.ts", "src/templates/assets/javascripts/integrations/search/highlighter/index.ts", "src/templates/assets/javascripts/integrations/search/worker/message/index.ts", "src/templates/assets/javascripts/integrations/search/worker/_/index.ts", "src/templates/assets/javascripts/integrations/version/findurl/index.ts", "src/templates/assets/javascripts/integrations/version/index.ts", "src/templates/assets/javascripts/components/search/query/index.ts", "src/templates/assets/javascripts/components/search/result/index.ts", "src/templates/assets/javascripts/components/search/share/index.ts", "src/templates/assets/javascripts/components/search/suggest/index.ts", "src/templates/assets/javascripts/components/search/_/index.ts", "src/templates/assets/javascripts/components/search/highlight/index.ts", "src/templates/assets/javascripts/components/sidebar/index.ts", "src/templates/assets/javascripts/components/source/facts/github/index.ts", "src/templates/assets/javascripts/components/source/facts/gitlab/index.ts", "src/templates/assets/javascripts/components/source/facts/_/index.ts", "src/templates/assets/javascripts/components/source/_/index.ts", "src/templates/assets/javascripts/components/tabs/index.ts", "src/templates/assets/javascripts/components/toc/index.ts", "src/templates/assets/javascripts/components/top/index.ts", "src/templates/assets/javascripts/patches/ellipsis/index.ts", "src/templates/assets/javascripts/patches/indeterminate/index.ts", "src/templates/assets/javascripts/patches/scrollfix/index.ts", "src/templates/assets/javascripts/patches/scrolllock/index.ts", "src/templates/assets/javascripts/polyfills/index.ts"], + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchEllipsis,\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/******************************************************************************\nCopyright (c) Microsoft Corporation.\n\nPermission to use, copy, modify, and/or distribute this software for any\npurpose with or without fee is hereby granted.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\nPERFORMANCE OF THIS SOFTWARE.\n***************************************************************************** */\n/* global Reflect, Promise, SuppressedError, Symbol, Iterator */\n\nvar extendStatics = function(d, b) {\n extendStatics = Object.setPrototypeOf ||\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\n return extendStatics(d, b);\n};\n\nexport function __extends(d, b) {\n if (typeof b !== \"function\" && b !== null)\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\n extendStatics(d, b);\n function __() { this.constructor = d; }\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\n}\n\nexport var __assign = function() {\n __assign = Object.assign || function __assign(t) {\n for (var s, i = 1, n = arguments.length; i < n; i++) {\n s = arguments[i];\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\n }\n return t;\n }\n return __assign.apply(this, arguments);\n}\n\nexport function __rest(s, e) {\n var t = {};\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n t[p] = s[p];\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n t[p[i]] = s[p[i]];\n }\n return t;\n}\n\nexport function __decorate(decorators, target, key, desc) {\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\n return c > 3 && r && Object.defineProperty(target, key, r), r;\n}\n\nexport function __param(paramIndex, decorator) {\n return function (target, key) { decorator(target, key, paramIndex); }\n}\n\nexport function __esDecorate(ctor, descriptorIn, decorators, contextIn, initializers, extraInitializers) {\n function accept(f) { if (f !== void 0 && typeof f !== \"function\") throw new TypeError(\"Function expected\"); return f; }\n var kind = contextIn.kind, key = kind === \"getter\" ? \"get\" : kind === \"setter\" ? \"set\" : \"value\";\n var target = !descriptorIn && ctor ? contextIn[\"static\"] ? ctor : ctor.prototype : null;\n var descriptor = descriptorIn || (target ? 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(function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n var desc = Object.getOwnPropertyDescriptor(m, k);\n if (!desc || (\"get\" in desc ? !m.__esModule : desc.writable || desc.configurable)) {\n desc = { enumerable: true, get: function() { return m[k]; } };\n }\n Object.defineProperty(o, k2, desc);\n}) : (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n o[k2] = m[k];\n});\n\nexport function __exportStar(m, o) {\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\n}\n\nexport function __values(o) {\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\n if (m) return m.call(o);\n if (o && typeof o.length === \"number\") return {\n next: function () {\n if (o && i >= o.length) o = void 0;\n return { value: o && o[i++], done: !o };\n }\n };\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\n}\n\nexport function __read(o, n) {\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\n if (!m) return o;\n var i = m.call(o), r, ar = [], e;\n try {\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\n }\n catch (error) { e = { error: error }; }\n finally {\n try {\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\n }\n finally { if (e) throw e.error; }\n }\n return ar;\n}\n\n/** @deprecated */\nexport function __spread() {\n for (var ar = [], i = 0; i < arguments.length; i++)\n ar = ar.concat(__read(arguments[i]));\n return ar;\n}\n\n/** @deprecated */\nexport function __spreadArrays() {\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\n r[k] = a[j];\n return r;\n}\n\nexport function __spreadArray(to, from, pack) {\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\n if (ar || !(i in from)) {\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\n ar[i] = from[i];\n }\n }\n return to.concat(ar || Array.prototype.slice.call(from));\n}\n\nexport function __await(v) {\n return this instanceof __await ? (this.v = v, this) : new __await(v);\n}\n\nexport function __asyncGenerator(thisArg, _arguments, generator) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\n return i = Object.create((typeof AsyncIterator === \"function\" ? AsyncIterator : Object).prototype), verb(\"next\"), verb(\"throw\"), verb(\"return\", awaitReturn), i[Symbol.asyncIterator] = function () { return this; }, i;\n function awaitReturn(f) { return function (v) { return Promise.resolve(v).then(f, reject); }; }\n function verb(n, f) { if (g[n]) { i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; if (f) i[n] = f(i[n]); } }\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\n function fulfill(value) { resume(\"next\", value); }\n function reject(value) { resume(\"throw\", value); }\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\n}\n\nexport function __asyncDelegator(o) {\n var i, p;\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: false } : f ? f(v) : v; } : f; }\n}\n\nexport function __asyncValues(o) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var m = o[Symbol.asyncIterator], i;\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\n}\n\nexport function __makeTemplateObject(cooked, raw) {\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\n return cooked;\n};\n\nvar __setModuleDefault = Object.create ? (function(o, v) {\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\n}) : function(o, v) {\n o[\"default\"] = v;\n};\n\nexport function __importStar(mod) {\n if (mod && mod.__esModule) return mod;\n var result = {};\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\n __setModuleDefault(result, mod);\n return result;\n}\n\nexport function __importDefault(mod) {\n return (mod && mod.__esModule) ? mod : { default: mod };\n}\n\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\n}\n\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\n}\n\nexport function __classPrivateFieldIn(state, receiver) {\n if (receiver === null || (typeof receiver !== \"object\" && typeof receiver !== \"function\")) throw new TypeError(\"Cannot use 'in' operator on non-object\");\n return typeof state === \"function\" ? receiver === state : state.has(receiver);\n}\n\nexport function __addDisposableResource(env, value, async) {\n if (value !== null && value !== void 0) {\n if (typeof value !== \"object\" && typeof value !== \"function\") throw new TypeError(\"Object expected.\");\n var dispose, inner;\n if (async) {\n if (!Symbol.asyncDispose) throw new TypeError(\"Symbol.asyncDispose is not defined.\");\n dispose = value[Symbol.asyncDispose];\n }\n if (dispose === void 0) {\n if (!Symbol.dispose) throw new TypeError(\"Symbol.dispose is not defined.\");\n dispose = value[Symbol.dispose];\n if (async) inner = dispose;\n }\n if (typeof dispose !== \"function\") throw new TypeError(\"Object not disposable.\");\n if (inner) dispose = function() { try { inner.call(this); } catch (e) { return Promise.reject(e); } };\n env.stack.push({ value: value, dispose: dispose, async: async });\n }\n else if (async) {\n env.stack.push({ async: true });\n }\n return value;\n}\n\nvar _SuppressedError = typeof SuppressedError === \"function\" ? SuppressedError : function (error, suppressed, message) {\n var e = new Error(message);\n return e.name = \"SuppressedError\", e.error = error, e.suppressed = suppressed, e;\n};\n\nexport function __disposeResources(env) {\n function fail(e) {\n env.error = env.hasError ? new _SuppressedError(e, env.error, \"An error was suppressed during disposal.\") : e;\n env.hasError = true;\n }\n var r, s = 0;\n function next() {\n while (r = env.stack.pop()) {\n try {\n if (!r.async && s === 1) return s = 0, env.stack.push(r), Promise.resolve().then(next);\n if (r.dispose) {\n var result = r.dispose.call(r.value);\n if (r.async) return s |= 2, Promise.resolve(result).then(next, function(e) { fail(e); return next(); });\n }\n else s |= 1;\n }\n catch (e) {\n fail(e);\n }\n }\n if (s === 1) return env.hasError ? Promise.reject(env.error) : Promise.resolve();\n if (env.hasError) throw env.error;\n }\n return next();\n}\n\nexport default {\n __extends,\n __assign,\n __rest,\n __decorate,\n __param,\n __metadata,\n __awaiter,\n __generator,\n __createBinding,\n __exportStar,\n __values,\n __read,\n __spread,\n __spreadArrays,\n __spreadArray,\n __await,\n __asyncGenerator,\n __asyncDelegator,\n __asyncValues,\n __makeTemplateObject,\n __importStar,\n __importDefault,\n __classPrivateFieldGet,\n __classPrivateFieldSet,\n __classPrivateFieldIn,\n __addDisposableResource,\n __disposeResources,\n};\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n /** @nocollapse */\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n * @return {void}\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @nocollapse\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param {T} [value] The `next` value.\n * @return {void}\n */\n next(value?: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param {any} [err] The `error` exception.\n * @return {void}\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n * @return {void}\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @constructor\n * @param {Function} subscribe the function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @owner Observable\n * @method create\n * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n * @return {Observable} a new observable\n * @nocollapse\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @method lift\n * @param operator the operator defining the operation to take on the observable\n * @return a new observable with the Operator applied\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n * Observable.\n * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n * @return {Subscription} a subscription reference to the registered handlers\n * @method subscribe\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next a handler for each value emitted by the observable\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @method Symbol.observable\n * @return {Observable} this instance of the observable\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n * @method pipe\n * @return {Observable} the Observable result of all of the operators having\n * been called in the order they were passed in.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @method toPromise\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @nocollapse\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return {Observable} Observable that the Subject casts to\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\n/**\n * @class AnonymousSubject\n */\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n *\n * @class BehaviorSubject\n */\nexport class BehaviorSubject extends Subject {\n constructor(private _value: T) {\n super();\n }\n\n get value(): T {\n return this.getValue();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n const subscription = super._subscribe(subscriber);\n !subscription.closed && subscriber.next(this._value);\n return subscription;\n }\n\n getValue(): T {\n const { hasError, thrownError, _value } = this;\n if (hasError) {\n throw thrownError;\n }\n this._throwIfClosed();\n return _value;\n }\n\n next(value: T): void {\n super.next((this._value = value));\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param bufferSize The size of the buffer to replay on subscription\n * @param windowTime The amount of time the buffered items will stay buffered\n * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler.\n * @return {void}\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n * @return {any}\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return {number} A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param {function(state: ?T): ?Subscription} work A function representing a\n * task, or some unit of work to be executed by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler itself.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @return {Subscription} A subscription in order to be able to unsubscribe\n * the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @type {boolean}\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @type {any}\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction extends AsyncAction {\n constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (delay > 0) {\n return super.schedule(state, delay);\n }\n this.delay = delay;\n this.state = state;\n this.scheduler.flush(this);\n return this;\n }\n\n public execute(state: T, delay: number): any {\n return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n }\n\n protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n\n if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n\n // Otherwise flush the scheduler starting with this action.\n scheduler.flush(this);\n\n // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n // `TimerHandle`, and generally the return value here isn't really used. So the\n // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n // as opposed to refactoring every other instanceo of `requestAsyncId`.\n return 0;\n }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * Put every next task on a queue, instead of executing it immediately\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n * queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n * console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n * if (state !== 0) {\n * console.log('before', state);\n * this.schedule(state - 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * console.log('after', state);\n * }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n const flushId = this._scheduled;\n this._scheduled = undefined;\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an + + +

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  • If you have experience developing, supporting or systematically using open source software for sustainability applications; We aim to publish the findings of this research by summer 2022.
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  • If you want to contribute to OpenSustain.tech by identifying new and missing projects.
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  • If you have experience visualizing or processing data with Python and know how to integrate such data into a new website.
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  • If you are a funder and want to support these developer communities via open infrastructure funds, consortia-based support or other collaborative models across institutions and regions.
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Email Eirini, Tobi and follow us on twitter

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This work is partially supported by Subak, the world’s first climate not-for-profit impact accelerator.

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Connect Developers with Impactful Sustainability Projects

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Thomas Gram · Dan Brown · Andrew Nesbitt · Tobias Augspurger · Josh Hopkins · John Metric +
+ December 19, 2023

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Countless talented developers around the world are looking for ways that their talents can contribute to sustainability. The Open Sustainable Technology project already offers a directory and analysis of the open source ecosystem in the areas of climate change, energy, biodiversity and natural resources. Despite this information being available on the wide range of open source sustainability projects, it can still be difficult for potential contributors to understand how they can get involved with these projects.

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That is why ClimateTriage.com is being launched as a dedicated platform focusing on the open technologies that help us to transparently understand and cooperatively manage the stability of our natural resources. Utilizing a statically exported Next.js build, this project offers a robust and scalable solution for connecting developers with impactful sustainability projects. The Open Sustainable Technology community works closely with the Ecosyste.ms platform, which provides metadata about the entire open source ecosystem and is therefore independent of the GitHub platform. At the same time, this will enable projects to be sorted by download numbers and other dimensions from a variety of package managers in the future.

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Projects that are looking for contributors can visit the main repository at Open Sustainable Technology on GitHub, then follow the guidelines provided to submit their project. They should label issues as 'Good First Issue' or 'Help Wanted'. In this way, open source communities help new members to familiarize themselves with the project and thus contribute to the growth of a lasting community. Ecosyste.ms will do the rest in the background and the project should soon after appear on ClimateTriage.com.

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Whether you are a seasoned developer or a newcomer eager to contribute, ClimateTriage.com provides a curated directory of impactful projects and a pathway to making your first meaningful contribution to open sustainable technology. Simply visit the website and search for projects of interest to you to view what types of contributions they are seeking, then start contributing.

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ClimateTriage.com is a collaboration of Codeshark, Ecosyste.ms, Open Sustainable Technology, Open Corridor and Linux Foundation Energy.

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The ClimateTriage.com landing page at the launch date.
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Open Principles for a Sustainable Technology Transition

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Tobias Augspurger · Alejandro Aristi · Tjark Döring · January 29, 2021

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A transformation towards a sustainability-driven economy will challenge all of the upcoming generations. Given the complexity of the changes required, a global and open collaboration is essential. Here are 4 reasons why a transition towards sustainability should follow open practices.

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Transparency and Trust

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A sustainable economy requires a common understanding of how our actions affect the environment. It is the question about which practices and technologies are sustainable in the long term. In practice, many options show a high degree of complexity and it is not always certain whether a solution will be economically, environmentally or socially sustainable. Can recycling of solar cells been done in an economical way? Could carbon capture and storage be an economically and environmentally viable option? Is the land used to produce biofuel crops competing with the production of food?

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Open practices can be crucial to ensure transparency and trust in finding answers. Open datasets, models and access can create a global discussion without running the risk of being covertly influenced by individual interests. This approach has already created a global scientific consensus of the human impact on climate change with results that can be reproduced by everyone.

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Another challenge is to reach a shared understanding of what steps are needed to address this issue. Tools such as open life cycle assessments play a central role in measuring the environmental footprint of products and services. With the help of public data and open models, it creates transparent metrics to evaluate their sustainability. This practice provides widespread trust, encouraging sustainable behaviors among consumers and businesses.

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Knowledge Evolution and Adaptation

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Open Data and models are not the only fields building transparency and trust. Opening knowledge and technology will make our economy more sustainable.

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The tools we use nowadays to design new technology are the result of centuries worth of collaboration. They emerged in the form of mathematics, algorithms, and programming languages. Today, 70% of the world's codebase is open source and software development has become one of the most innovative industries. The results are revolutionary and cutting-edge developments in robotics, artificial intelligence or blockchain, just to name a few.

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With the advancements made in global communication, we can apply the creative methods behind software development to more traditional fields. With knowledge available to everybody, people will be able to maintain and adjust technology by themselves. For example, open agriculture can be used and adapted by farmers to improve their crops' sustainability. Practices like these enable faster global transition that meets local conditions and cultural needs.

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Collaborative Innovation

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In his 2003 book “Open Innovation”, Chesbrough first coined the term open innovation as a novel innovation strategy. Initially, it meant a straightforward contradiction of the traditional and secretive R&D mindset towards corporate innovation. However, the trend for openness and collaboration between and within companies has rapidly expanded.

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Over time, it has become an active innovation-seeking path for the whole ecosystem that further pushes the technological development and knowledge boundaries. Most importantly, this collaborative approach is crucial when considering the ever-increasing product complexity. An aspect that makes it difficult for even the largest companies to carry out the whole development process in-house. In particular, mobility solutions, food and changes in our housing characteristics require the personal and cultural needs to be included in the design process from the beginning. Otherwise, we run the risk of developing sustainable solutions without taking people's lifestyles into account.

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Two evident benefits of adopting an open innovation strategy are the setting up a common language between the contributors and standardization of ways to collaborate. This, in turn, translates into substantial competitive advantages from the commercial perspective: better adaptation for new business opportunities, increased flexibility in the developed solutions, as well as a faster and cheaper route to launch a product into the market. Most importantly, by integrating users into the design processes, the idea of partnership towards a larger and shared goal also brings new ways of doing business.

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Open Business Models

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Free and open practices emerged with the idea of empowering freedom for all users. Also, the advantages of cooperative software development for common standards and a higher code quality became obvious. What seems unimaginable to many is the development of a profitable company based on free and open practices. Nonetheless, over the last decade, companies have shown with great success how sustainable and open business models can look like. Open Core, as one of the most prominent open business models, combines the practices of adaptation, transparency and collaborative innovation.

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A new billion-dollar business methodology has emerged and provides the basic infrastructure for our digital world. What initially seemed possible only for software development is now being transferred to new areas as can be seen in the development of semiconductors with the rise of the RISC-V initiative. Hundreds of organization are working together to create an open ecosystem to ensure free and open design of computing architectures and processors in the future. The same mindset can be applied to other sustainable technologies that define our lives.

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The advantages of applying an open practices to the development of sustainable technologies are cleared up. The baseline for this development has already been set by many projects and is now visible through the projects listed at OpenSustain.tech. It is now up to us whether or not we are able to apply the practices that have brought us the greatest innovation to the problems that challenge us the most.

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Empowering Community-Driven Funding with OpenClimate.fund

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Tobias Augspurger · Michael Scheiwiller · Andrew Nesbitt · Richard Littauer · Benjamin Nickolls
+ December 18, 2024

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We're launching OpenClimate.fund, a community-driven initiative to support open source projects that address climate change and biodiversity loss. These are among the most pressing challenges of our time, yet open source efforts in these areas remain alarmingly underfunded. While significant resources are being spent on areas such as artificial intelligence and security, the environmental sector is largely left empty-handed when it comes to open source solutions. It's time to change that.

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OpenClimate.fund aims to bridge this gap by funding an ecosystem built on transparency, trust, and collaboration to advance climate-friendly technologies and measurable environmental impact.

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A Community-Driven Funding Approach

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In our first funding round, we’re introducing a “positive snowball system" to empower the community and drive meaningful change. Here’s what you can do:

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  1. Identify a high impact project missing in the OpenSustain.tech directory.
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  2. Submit it through our review process using the Contributing Guide, which outlines the steps for nominating impactful open source initiatives.
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  3. Nominate another project from the directory to receive a 100€ gift and a heartfelt thank-you message.
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To minimize overhead, this initial round will focus on projects that have been active within the last six months and accept donations through known donation platforms like Open Source Collective or GitHub Sponsors. Despite rigorous data mining with our partner Ecosyste.ms, only 104 eligible projects have been identified so far at the date of launch — underscoring the urgent need for this initiative. Our own experience has also shown us that donations in this area are so rare that many projects don't even bother to ask for them. If your project is already listed but not yet proposed, simply add donation information about your GitHub organisation or open source project and it will automatically be included for Round 1.

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With a pilot fund of 10,000€, we are aware that this can only be a drop in the ocean considering the 2,200+ projects on OpenSustain.tech. Our goal is to spark a broader conversation about building a sustainable ecosystem for open source solutions tackling global environmental challenges.

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Olenjok Delta - Sentinel 2 satellite imagery - CC BY 2.0
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Preparing for Round 2

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We believe that investing in the right open source projects can create a cascading effect, amplifying impact and accelerating sustainable transformation. We can no longer rely on black-box knowledge creation, because the decisions we make today are so critical. That’s why this first funding round is just the beginning.

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Our long-term goal is to establish a network of organizations and individuals committed to creating a transparent and equitable funding model for climate-related open source projects. By applying open science principles to the distribution process, we aim to identify where our investments can have the greatest global impact on the open source ecosystem. Rather than focusing on seed funding for new development, our priority is to cover the essential costs of established open source projects that serve as foundational infrastructure for computing in this field.

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Similar to our first report, we combine quantitative methods with qualitative insights to identify areas where funding can have the greatest impact.

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To achieve this, we analyze project data such as:
+- Dependents
+- Citations
+- Downloads
+- The Development Distribution Score
+- Issue frequency
+- and many more..

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Our goal is to evaluate and maximize the impact of individual projects. In addition, we integrate this data with interviews, gap analyses, and urgency analytics for key topics highlighted in the IPBES Global Assessment and IPCC Reports. This process allows us to create a detailed open science report explaining how our distribution metric was developed. This time, we’re going beyond analysis—we’re transforming these insights into actionable outcomes.

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Thanks to the support of Ecosyste.ms Funds and Open Source Collective, we will have the right technology partners to channel funds directly to all organisations, projects and their dependencies. But scaling this effort requires more than technology - it requires a collective effort.

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Join Us in Disrupting the Status Quo

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Are you ready to harness the power of openness to combat the destruction of our natural world? Whether you’re an individual, an organization, or an investor, there’s a place for you in this movement. Together, we can ensure that open source innovation drives a sustainable future.

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Visit OpenClimate.fund today to learn more, nominate projects, spread the word with #openclimatefund, get in touch with Michael or Tobias and follow us on Mastodon.

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Openness as a Key Indicator for Sustainable Investment

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Tobias Augspurger · January 29, 2022

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An open-source strategy for measuring sustainable investments can fundamentally impact the transition to a carbon-neutral and green economy. Measuring corporate sustainability based on quantitative physical values combined with Open Science practices has the potential to avoid greenwashing as the biggest threat to economical transformation.

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In the coming decade, the world will face profound changes in order to establish ecological norms and rules for our economy. The financial industry will be at the center of this transition, determining which companies and technologies are granted the resources they need to spearhead this evolution.

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In recent years there has been a systematic shift toward evaluating companies not just on monetary but also on ethical and environmental criteria. Non-financial factors known as Environmental, Social, and Governance (ESG) are frequently used to evaluate companies. Today about 40% of total assets worldwide under management are sustainable investments rated by ESG scores [1]. The high demand for such financial products is driving massive growth, with a predicted market share of over 95% by 2030 [2]. Figure 1 clearly shows the massive growth in this sector:

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Figure 1: Global AUM invested in ESG mandates (in USD tn)
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Despite this promising development, greenhouse gas emissions like carbon dioxide are still accelerating [3]. Humanity stand to lose climate stability and biodiversity, and thus our livelihoods, before the end of this century. Why is the trend toward sustainable investment not reflected to the same extent in physically measurable environmental variables? The following figure clearly shows how little CO₂ emissions are influenced by the trend towards sustainable investment.

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Figure 2: Annual CO₂ emissions

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They are not measured on the impact they deliver

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People in the financial industry are becoming increasingly aware of this inconsistent development. Today's ESG ratings are questioned by insiders from numerous institutions. Kenneth P. Pucker sums up the problem in his blog post "The Trillion-Dollar Fantasy" in a single sentence [4]:

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"They are not measured on the impact they deliver".

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Deutsche Bank's former ESG officer, Desiree Fixler, publicly expressed her concerns regarding the legitimacy of the company's claims on the percentage of assets under management that merit an "ESG" or green label. [5].

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Tariq Fancy former Chief Investment Officer for sustainable investing at BlackRock argues his essay "The Secret Diary of a Sustainable Investor" [6]:

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We need political leaders who are competent, evidence-based, and sufficiently non-partisan to focus primarily on how we can fix the rules of the game.

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Given the sheer volume of sustainable investments, it should come as no surprise that businesses around the world are trying to represent their existing business models as green and sustainable in terms of ESG ratings. Without a positive evaluation, these companies will probably lose a large part of their future investment. As a result, considerable greenwashing has increased in recent years. [7].

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ESG is used to measure the quality of green marketing campaigns

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Thus one of the critical pieces for the green transition is establishing the appropriate methodology and standards to accurately evaluate the impact of companies on the environment and society. When examining ESG scoring in its current form, it becomes clear that robust scientific approaches do not inform these "measurements". Instead, companies produce ESG reports using a range of questionnaires that vary significantly by industry. ESG rating agencies process these reports using a mix of manual and automated methods. For example, natural language processing algorithms, a subset of artificial intelligence, are used to process public media and sustainability reports to assess how "green" a company is behaving [8] [9]. ESG experts worldwide advocate for the standardisation of ESG reports and hope for guidance at European Union. [10]. However, since this is an empirical topic, one should rather look in the direction of science.

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Since the current methods of ESG ratings are also known to companies, they are trying to "represent" their own company, technology and business models as ecologically as possible to the outside world under all circumstances with the help of massiv green marketing. If you take a closer look at the algorithms and methods, you will see that ESG is used to determine the quality and quantity of green marketing campaigns. The following figure shows how such an assessment is created today using data-driven approaches. [11]

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Figure 3: A Data-driven Approach to Environmental, Social and Governance
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Independent scientific analyses and peer reviews of their own reports are the exception here. Each company designs its ESG reports at its own discretion. Also reports from the media are in the rarest cases purely objective nature and it is therefore very questionable to include them. Political and corporate influence on the public media can not be denied in many countries.

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Reproducible knowledge creation

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Science has faced similar challenges in the past as it does today with respect to ESG. The complexity of scientific experiments has led to the so-called replication crisis [12]. New tools such as neutral networks and other very complex processing chains of data made scientific results very difficult for other scientists to replicate. The published papers were not sufficient to inform outsiders about the underlying data, instruments, methods, and steps. In many respects, this development is analogous to the reports on the impact of companies on their environment.

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In the field of climate science, but also in many other scientific fields, there has therefore been a fast transformation in recent years. The triumph of Open Science, Open Access, Open Data and Open Source can no longer be denied [13]. Satellite data, climate models, software tools and the associated programming languages are now open and freely available. Instead of arguing about the results of non-transparent studies, it is now possible to discuss scientific details of how the results were obtained. However, as long as this is done according to open principles, discussions remain scientific, objective, and evidence-based. Thus, even the last doubts about the existence of climate change could be dispelled. Today there are no studies based on open principles, which are able to disprove the existence of climate change.

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Open Science and scientific discourse create reproducible knowledge. Open science communities like Pangeo represent prime examples of this field and show how Earth Science has been transformed by Open Source and Open Science. Emerging patforms like Papers with Code or Journal of Open Source Software created a new mindset on how knowledge can be published and reviewed in public. Within the energy modeling community, a key backbone of the energy transition, there is a strong trend toward open knowledge modeling. Whether and to what extent our energy supply can be covered by purely renewable energies can therefore be discussed transparently, in detail and scientifically by scientific communities such as openmod in order to find a common consensus.

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Figure 4: Open Data and Open Source creating a transparent energy system modeling
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The trend to openness in general is also strongly visible in digital industries creating a new mindset on how to do business. Emerging Business Concept like "Open Core" have led to IPO of $11 Billion just by a single company called Gitlab in 2021 [14]. Commercial Open Source Software Companies and Open Source in general is changing the way how data is processed and how we make decision based on the results [15]. 85% of software products today stands on the shoulders of the Open Source movement [16].

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Precision and automation are not always valued

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Unfortunately, this mindset is only found in very limited use within ESG and sustainable investment. This becomes clear when looking at the active Open Source projects in this domain. Based on OpenSustain.tech, there are only 10 active Open Source projects in the area of sustainable investment. Even the strong project like SBTi Temperature Alignment tool, OS-Climate or r2dii.analysis are very small compared to other Open Source projects. As part of OS Climate, a number of companies including Amazon, Red Hat, Airbus, Goldmann Sachs, and Microsoft have joined together within the Linux Foundation to develop an open framework for climate related investments. However, if you take a look at the repositories behind, you will quickly realize that the project is still far from a practical application even after 2 years.

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Although Open Source is known to provide solid and global standardization for the processing of data, there is only one attempt to standardize the ESG scoring method itself. Cary Krosinsky and the team of Real Impact Tracker released their methodology on GitHub with a blog post giving more background information [17] [18]. Nicolaas Koster, Matthias Memminger and Stefan Woerner published similar articles about the high potentical of an Open Source ESG framework and methodology [19] [20]. Despite all these initial thoughts and first good efforts, no professional Open Source rating ESG framework can be found today. ElectricityMap is one of the very few commercial Open Source platforms with a community and a technical solution that is appropriate to solve problems related to business impact on climate change. But even CEO Olivier Corradi admits that automation and precision are less in demand than one might think when it comes to corporate carbon footprints [21].

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Figure 5: Nicolaas Koster illustrates how ESG is suffering from a lack of transparency Source
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Openness as a key indicator for sustainability

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Nevertheless, the problems within sustainable investment clearly show that transparent and repeatable ESG scoring results are in demand. A company that makes it possible to disclose its own sustainability on the basis of "Open Science" has an unbeatable unique selling point in the battle for green investments.

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In the rating of companies, "openness" has the potential to become a criterion in itself making clear to what extent data and models behind the ESG reports of a company are disclosed. This allows the uncertainties and associated error bars to be calculated over sustainability statements. It enables the development of an independent scientific community concerned with corporate sustainability. Openness about one's own sustainability would demonstrate the will to transform even those companies that have not yet made much progress in this area.

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Unfortunately, recent developments have shown that decisions about the sustainability of industrial sectors and technolgy are not subject to transparent scientific evaluation. Decisions in this area are strongly influenced by intrasparent political decision-making processes [23]. A study based on Open Science principles would provide a transparent and objective answer to the question of whether gas power plants or nuclear power have a positive impact on the environment. It would also allow to talk about risks and potentials if these technologies are used worldwide in a larger scale.

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Conclusion

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The Open Source and Open Science movements have changed the way how to collaborate on digital product development and science today. Yet there is not a single company in the world that transparently discloses its impact on the environment through Open Science. Such a development, however, is the only way to verify the multitude of climate pledges made by companies. Many countries and civil organizations are already showing how such a development can look like. The question is how to incentivize companies to enable Open Science for their own environmental sustainability. One way would be to measure the openness of ESG reports and their underlying data and models.

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Sustain Open Source, Sustain the Planet: A new conversation

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Tobias Augspurger · Richard Littauer · June 14, 2024

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Originally posted on OpenSource.net.

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Since 2017, SustainOSS has been a community of people who think about what we can do to make open source software more sustainable. We've talked about making better ways of compensate coders, building better communities, and welcoming more diverse voices into the open source ecosystem.

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Many of these conversations have taken place on the Sustain Podcast. However, almost none of these conversations have been about the intersection of open source and environmental sustainability.

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OSS for Climate Logo
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It's all over the news that software energy consumption is bad for the climate. What's rarely talked about, though, is how software actually enables climate science and sustainable technology, especially when it's open source. It's the glue that brings together scientists from all disciplines—biosphere, hydrosphere, atmosphere—to create the highly complex collaborative earth and climate models that allow us to forecast what our future might look like if we continue to behave as we have in the past.

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This is why we created the OSS for Climate podcast. The is a new initiative within the Sustain ecosystem hosted by Richard Littauer, the main host for the Sustain Podcast, who has recorded hundreds of conversations on this topic. The podcast is a collaboration with OpenSustain.tech, a free community accelerating open and sustainable technology. This podcast aims to fill the gap in discussions about how open source can be a key driver for climate action and sustainability.

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Shining a light on those who take action

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OSS for Climate highlights individuals and projects, aiming to provide support in terms of funding, sustainability, and onboarding new contributors. It will explore the systematic changes open source can provide for climate action, addressing issues of transparency and trust, and emphasizing the critical role open source plays in our efforts to combat climate change.

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We need a place to give a voice to the people behind the projects, to better understand their needs and perspectives on how open source can accelerate action on the climate crisis. Our first study, The Open Source Sustainability Ecosystem, showed us that such interviews are essential to understanding the very nature of open software's impact in this area. For this reason, we decided to combine the obvious synergies of our study with a podcast to make the interviews accessible to everyone.

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That is why we started the Open Source Software for Climate podcast with Richard Littauer. He is the ideal voice to bridge the gap between what sustains the open source ecosystem and how open source can sustain the natural shared ecosystem on which we all depend. As an open source wizard, community builder, passionate birder, soon-to-be Ph.D. ecologist, and interviewer of more than 300 people in the open source community, there is no better host for this.

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Word cloud of the most frequently used words in the project descriptions on OpenSustain.tech
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Comprehensive Coverage

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On OpenSustain.tech we've listed around 1400 projects with an active community. Almost all of them are relevant to climate change. Even if you just look at climate models, the amount of software needed to make good predictions is significant.

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If you include everything that affects the climate, everything that is affected by the climate, and all the technologies needed to adapt to and combat climate change, you end up with a significant number of projects. Some of these are massive projects that are used by tens of thousands of developers; others are dependencies, part of the digital infrastructure that underpins our shared world.

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Involving Diverse Stakeholders

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Solving a global problem such as climate change requires global collaboration between different fields, making open source the most relevant methodology for combining knowledge and skills from different fields.

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This includes not only scientists, but likewise entrepreneurs, activists, politicians and citizens who are empowered by open source to participate in the implementation of solutions. We want to know how open source can change the spread of climate solutions in the world, how climate justice can be archived by enabling access to technology and knowledge for those who are most vulnerable and affected.

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Our mission

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Our mission is to promote and support the entire open source community in the area of climate and sustainability. To do this, we combined data science based on open source analytics and data mining with ecosyste.ms to discover not just those who are the loudest, but the people who are making significant contributions in the background.

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Stay tuned for insightful episodes that bring to light the important work being done in the intersection of open source and climate action. Listen to OSS for Climate for a deeper understanding of how open source software can contribute to a sustainable future. Are you still on fire and want to find out more about how you can join our mission? Do you have a special person or project that you would like us to spotlight? Feel encouraged to contact Richard Littauer directly or the OpenSustain.tech community on Mastodon.

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The Open Source Sustainability Ecosystem - Report Release 2023

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Tobias Augspurger · Eirini Malliaraki · Josh Hopkins +· January 11, 2023

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Opensustain.tech has recently published a study providing the first analysis of the health and vibrancy of open source software in sustainability and climate technology. Read the extended website version of the PDF version

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Open source culture has demonstrated how transparent and collaborative innovation can support modern digital services, data, and infrastructure. Yet, despite its transformative impact and use within an estimated 97% of digital products, the potential of open source for developing environmentally sustainable technologies is not well understood.

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Open source software (OSS) accelerates the transition to a sustainable economy by supporting traceable decision-making, building capacity for localization and customization of climate technologies, and most importantly, helping to prevent greenwashing. This transition requires technological innovation and new opportunities for society to participate in developing and adopting technologies.

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Opensustain.tech has recently published a study providing the first analysis of the health and vibrancy of OSS in sustainability and climate technology. The analysis covered multiple dimensions, including technical, social, and organizational. The report also highlights key risks and challenges for users, developers, and decision-makers, as well as opportunities for more systemic collaboration.

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For the past two years, more than one thousand actively developed open source projects and organizations were collected and systematically analyzed using qualitative and quantitative methods as part of the Open Sustainable Technology project and the associated database.

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Community engagement

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Opensustain.tech found 996 active project repositories on GitHub that had at least one commit or a closed issue in the last year. Although stars are not a perfect metric, we counted 127,038 stars across these projects. Still, a search on GitHub revealed 27 projects have more stars than the entire software in environmental sustainability combined! This is one indicator that open source still plays a minor role as a long-term transformation strategy in sustainability compared to other domains.

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Topic

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Additionally, half of all identified projects are in data-rich fields such as climate science, biosphere, energy system modeling, transportation, and buildings. Other topics, such as carbon offsets, battery technology, sustainable investment, emission observation, and integrated assessment modeling, show few notable developments. Based on popularity growth, opensustain.tech also identified newly emerging topics, such as green software. Moreover, most identified projects are relatively young, with a median age of 4.45 years.

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Fig.1 - All studied projects grouped into the corresponding fields and topics
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Programming language

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The analysis of the number and use of programming languages provided further insight into the coding skills required and the nature of the projects. For example, opensustain.tech found Python dominated the OSS movement for sustainability, and is used in 39.8% of all projects, followed by R (16.7%), and lastly Jupyter notebooks (9.34%). This indicates a strong focus on analyzing large datasets, where Python and Jupyter Notebooks are increasingly dominant and less focused on web applications.

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Fig.2 - Distribution of programming languages
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License

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The use of various licenses revealed potential intellectual property issues related to the use of software packages as well as the general openness of the projects. Opensustain.tech found permissive licenses like BSD, Apache and MIT are the most popular in sustainability. The MIT license was the top choice, used in 26% of the projects, followed by the copyleft license GPLv3 (17.3% of all projects).

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Project size

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Analysis of knowledge, work, and project governance distribution revealed small, open source communities lead most of the development. On average, open source software projects rely heavily on a single developer responsible for approximately 70% of the contributions to a project. This indicates a higher contributor risk, which may jeopardize the future of many of these projects.

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Geography

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Most OSS projects (64%) are based in Europe and North America, with a small number of projects from the Global South. Despite having more GitHub users than Europe, Asia accounts for only 1.9% of organizations working in OSS for sustainability.

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Fig.3 - Global distribution of identified organisations
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Recommendations

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Based on this analysis, opensustain.tech proposes recommendations for those interested in supporting open source software in environmental sustainability more effectively through community building, policy development, and future investment.

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  • Collaboration: strengthen the interconnectivity of the identified open source communities and connecting projects to local use cases is paramount for the long-term impact and stability of the ecosystem. It's also key to adapt and extend existing open source projects for underrepresented countries in the Global South.
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  • Funding: further support is required in the form of an open earth intelligence incubator and other support programs for open source software in environmental sustainability, as well as ongoing, dedicated funds for development and maintenance.
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  • Technical: the OSS community of users and developers should develop better technical interfaces between platforms, data, models, and open-source tools across and within sectors to “stop reinventing the wheel”, and standardize environmental data exchange across different levels of government.
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  • Advocacy: close the knowledge gap on the environmental impact of companies through open source principles and transforming financial institutions through transparent and scientific decision-making for sustainable investments.
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Digital and sustainable transformation must converge as a digital public good if we are to achieve agreed environmental goals and create a safe and equitable corridor for people and the planet. Open sustainability principles can help governments, research institutes, nongovernmental organizations, and businesses move quickly toward science-based decarbonization and conservation of natural resources and ecosystems by providing critical transparency, traceable decision-making, and collaboration on innovation.

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Everyone is invited to participate in future studies of this type. By contributing to opensustain.tech in any way, you help us build future reports. Most importantly, you join us in promoting and encouraging open sustainable technology.

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HcmV?d00001 diff --git a/blog/you_can_preserve_the_earth_s_livability_with_open_source/index.html b/blog/you_can_preserve_the_earth_s_livability_with_open_source/index.html new file mode 100644 index 0000000000..4555dd22d2 --- /dev/null +++ b/blog/you_can_preserve_the_earth_s_livability_with_open_source/index.html @@ -0,0 +1,1424 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + You can preserve the Earth's livability with open source - Open Sustainable Technology + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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You can preserve the Earth's livability with open source

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Tobias Augspurger · May 23, 2022

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Why actions rooted in open source culture can make a big impact.

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Earth's uniqueness in creating a stable environment for life in a completely hostile space is a miracle. Various life forms have taken billions of years to build up the natural resources humans depend on, such as a protective atmosphere, fertile soil, stable weather, and clean drinking water.

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As a movement to democratize technology development and knowledge creation, open source has the potential to become the central driver in preserving this stability. Here are four different ways you can preserve the livability of the planet by using, supporting, and participating in open source projects, and more than 20 resources to help you get started.

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Measure, optimize, and reduce

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Before taking action, it is important to assess your impact on the environment in a scientific way. With more accurate information, you can focus your efforts where the most potential lies. Open science discloses the models behind these measurements, which reduces uncertainties in the long term through continuous improvement and observation. Open source tools make it possible to remain objective and make meaningful comparisons to guide decision making.

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These decisions happen constantly, both in the consumption of everyday resources and in the use and development of digital products. Software developers find their work in digital products all over the world. Measuring and reducing the energy consumption of these products is the goal of open source tools like Scaphandre , Cloud Carbon Footprint, and kube-green. These tools help you track power consumption, estimate carbon emissions, and even shut down resources when they are not needed.

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Figure 1: Cloud Carbon Footprint is an open-source project, sponsored by Thoughtworks Inc +(Cloud Carbon Footprint, Apache License, Version 2.0)
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The open source platform electricityMap calculates and predicts the carbon intensity of electric power consumption within electrical grids worldwide on an hourly basis. You or your business can take action by using this information to shift computation to times when there is a high share of renewable energy sources in the electricity grid, thereby reducing emissions.

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The open source community of OpenEnergyMonitor enables people to monitor and understand the electrification of their homes. Open hardware and open source tools created by OpenEnergyMonitor track and log usage data from multiple categories, including electric vehicles, heat pumps, and photovoltaics. Their learning platform offers a deep understanding of the possibilities for increasing the use of sustainable energy in your household and becoming energy independent. At the heart of it all is the emonPi energy monitor and base station, which can bring together information about your energy consumption from multiple sensor nodes.

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Figure 2: The OpenEnergyMonitor Solar PV monitor provides real-time and historical information on solar generation and on-site consumption. +(OpenEnergyMonitor, CC BY-SA 4.0)
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Understand the planet

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The Earth is a highly complex system, which makes it vitally important to better understand the impact humans have on this system. Several open science and open source projects are working to improve transparency and cooperation in this area. Various tutorials offer beginners an introduction to the programming languages and tools used in this field. These tutorials allow you to improve your programming skills while simultaneously learning more about the environment.

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An Introduction to Earth and Environmental Data Science provides a beginner-friendly introduction to the use of open source Python tools, how these tools can be used, and data science in general. Instead of introducing the basics of Python with routine robotics projects, learners discover how to use Python and programming tools to interpret data about the planet.

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Figure 3: Understanding and representing earth observation data, such as this map of sea ice concentration, is a central part of the Introduction to Earth and Environmental Data Science course. +(Ryan Abernathey, CC BY-SA 4.0)
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The Climate Laboratory provides the perfect starting point for getting a deeper understanding of climate change and the physical science behind it. Like the previous example, this course includes tutorials to use Python, one of the main tools for climate scientists today. The textbook is a living document that has been created around climlab to provide a powerful but entry-level friendly tool for climate modeling.

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Figure 4: You can learn to make maps illustrating surface air temperature anomalies due to CO2 doubling
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Earth Lab provides Earth data science lessons that will help you learn how to work with data in the R and Python programming languages. All course material is open source. Nonspecialists who enroll in the Earth data analytics online course help ensure that understanding how the planet works is not just an academic exercise but also a field of knowledge used in all parts of society.

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Observe the planet

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Satellites provide a unique tool to map the state of the planet. Even small changes in our ecosystems such as greenhouse gas emissions, land use change, biodiversity loss, soil drought, or water pollution can be measured with high temporal and spatial resolution.. The data collected here are calibrated to the highest scientific standards and therefore provide an objective view of Earth. Recent years have seen a massive democratization of data and software tools in this field. NASA and ESA have published many data sets and tools from various satellite missions under a free license.

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The condition of even the most remote forests can be assessed with the help of satellite images in different spectra. All kinds of forest health indicators can be derived from data, like deforestation risk, wildfire risk, or tree detection. By using and improving these tools, you can actively contribute to preserving this ecosystem. Only by measuring and disclosing such information can environmentalists demonstrate that habitats are being lost.

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You can also learn how to process satellite data using modern image processing algorithms such as neural networks. One of the most robust communities in this field is Global Forest Watch. Here, multiple datasets are combined to give an interactive overview of the health of forests worldwide.

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Figure 5: Spatial probability of deforestation on Guadeloupe
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Projects like Resource Watch and Radiant Earth provide entry-level platforms for people anywhere to understand natural resources based on Earth-observation technology. NASA provides its own open source platform, NASA Harvest, to map the state of crops, providing essential information about food security. Another way to get insights about possible hunger crises is the measurement of soil moisture, which is also possible from space with packages like smapr.

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The ever-increasing resolution of satellite data in recent years means that even the impact of individual companies on the environment can be measured. This new field of science is called spatial finance, and its purpose is to guide business development and investment in alignment with good sustainability practices. The first open datasets in this field track the locations and companies involved in cement production, iron and steel production, and global coal, oil, and gas supply chains. The Carbon Mapper platform shows how to build pressure on companies by disclosing methane and carbon dioxide sources.

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Figure 6: Global coal, oil, and gas asset-level data +(Lucas Kruitwagen, MIT License)
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Demand openness

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Environmental catastrophes are often accompanied by a cover-up and manipulation of scientific findings and information. This applies to climate change as well as local environmental impacts. UN Secretary-General Antonio Guterres has given a clear warning following the release of the international climate change report, which shows no trend reversal in global warming and the loss of biodiversity in 2022.

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We are on a pathway to global warming of more than double the 1.5°C limit agreed in Paris. Some government and business leaders are saying one thing, but doing another. Simply put, they are lying. And the results will be catastrophic. This is a climate emergency.

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Figure 7: With the help of pyam you can process the data behind the latest UN report and thus better understand the different scenarios. +(pyam, Apache license)
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One of the easiest ways to change course is to consistently demand open science concerning environmental issues. Openness is a key indicator of sustainable development, because anyone pursuing truly sustainable intentions is interested in refining their conclusions through open scientific discourse. Openness will also help prevent greenwashing, or the practice of promoting a company's sustainability efforts through marketing while scientific findings and conclusions about environmental impacts are downplayed or held private.

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The next time you buy products that claim to be carbon neutral, ask for independent studies and data. If a politician demands questionable measures for environmental protection, ask for open model calculations and open access studies on this topic. Do you want to invest in a sustainable investment fund? Ask for open scientific studies, data, and models on why this investment is sustainable. Demanding openness in all these ways helps limit greenwashing and ensures that companies' environmental impacts are verifiable, accountable, and traceable.

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Contributing Guide

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Fast Lane

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If you just want to give a hint about a missing project, send an email at tobias.augspurger@protontypes.eu. However, we would be pleased to add you to the list of official contributors via a pull request.

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Guideline

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Earth's uniqueness in creating a stable environment for life in a completely hostile space is a miracle. Various life forms have taken billions of years to build up the natural resources humans depend on, such as a protective atmosphere, fertile soil, stable weather, and clean drinking water. As a movement to democratize technology development and knowledge creation, open source has the potential to become the central driver in preserving this stability. Open Sustainable Technology's mission is to gather projects that preserve natural ecosystems through open technology, methods, data, intelligence, knowledge or tools.

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Contributors

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What Criteria Must a Project Meet to be Listed?

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  • The project must comply as closely as possible with the guideline.
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  • The projects must be actively be used or developed at the time of listing.
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  • You need to show that others beyond the project's main developers are using the project. Good indicators are issues or pull requests from external users.
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  • Your project should be structured and documented in such a way that it can be reused and extended by others.
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  • If possible, the Git Link of the projects should be used for listing since this allows meta data analysis of the project. Thus, the project can become part of the data analysis and recurring report.
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  • Larger parts of the project are published under an open source license.
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Contribute to the List

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  • Create a fork of this project.
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  • Add your project in the following format:
    +- [Project Name](Project URL) - A sentence that describes your project and what makes it different from others.
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  • If available, use the Git link to the central repository of your project. This makes it easier for developers and maintainers to review the project faster.
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  • Create a pull request and wait for the reaction of the maintainer.
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More details on how to do pull requests can be found in the official GitHub documentation.

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The Project Does Not Yet Meet the Criteria, What Now?

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If your project doesn't meet the community, documentation, or usage criteria because it's in its early stages, don't hesitate to create a pull request. We will label the pull request "Under Observation" so that we can review it later. If you wish, we can use our social media channels to help you find contributing communities and users.

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Post new entries on social media

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To simply follow new project that have been reviewed successfully, the commit message of the merge will be posted on our Mastodon and Bluesky channel. To trigger the posting via GitHub Action, the reviewer of the project needs to add a short sentence describing your project and the full URL of your repository to the description of the merge commit.

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Join the Community

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We welcome developers, scientists, artists, writers, journalists, entrepreneurs, and citizens alike. Join our regular community calls to learn more about how you can make a difference through open source action on climate and sustainability.

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Open Educational Resources in Sustainable Technologies

+

Geocomputation and Geospatial

+ +

Renewable Energy

+
    +
  • To Catch The Sun - Inspiring stories of communities coming together to harness their own solar energy, and how you can do it too.
    • +
  • geothermics - Educational repository with Jupyter Notebooks all around the topic of geothermal energy.
    • +
  • Photovoltaics Education Website - A detailed deep dive to get started with photovolatic technology.
    • +
  • Predicting the Wind: Data Science in Wind Resource Assessment - Contains material for a hands-on tutorial and additional content about data science in wind resource assessment.
    • +
  • PVSC48-Python-Tutorial - Computing tutorial with focus on PV computing packages, Python, data wrangling with Pandas, and data viz.
    • +
  • Electrify - A web / mobile Tycoon-style game teaching about electricity production and consumption.
    • +
  • Fundamentals of Solar Cells and Photovoltaic Systems Engineering - Learn modeling solar radiation, calculating IV curves for varying irradiance and temperature, modeling electricity generation from PV installations, optimizing PV module orientation and inclination, and conducting time series analysis of solar generation.
    • +
  • pv-tutorials - Links to tutorial material for various open-source software packages for PV modeling and analysis.
    • +
+

Energy Systems

+ +

Consumption of Energy and Resources

+ +

Green Software

+
    +
  • Sustainable Software Engineering - A course that covers techniques for measuring and improving environmental, social, and individual sustainability at every stage of the software lifecycle.
    • +
+

Climate and Earth Science

+
    +
  • Xarray Tutorial - An open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun.
    • +
  • ClimateModeling_courseware - A collection of interactive lecture notes and assignments in Jupyter notebook format.
    • +
  • Earth and Environmental Data Science Book - An Introduction to Earth and Environmental Data Science.
    • +
  • Free Earth Data Science Courses & Textbooks - A site dedicated to tutorials, course and other learning materials and resources developed by the Earth Lab team.
    • +
  • Climate Change Impact Assessment - A practical walk-through.
    • +
  • MIT-PraCTES - Materials for MIT workshop "Practical Computing Tutorials for Earth Scientists".
    • +
  • ICAR - A simplified atmospheric model designed primarily for climate downscaling, atmospheric sensitivity tests, and hopefully educational uses.
    • +
  • GEOG0133 - Open Terrestrial Carbon modelling and monitoring lecture.
    • +
  • The Climate Laboratory - A hands-on approach to climate physics and climate modeling.
    • +
  • Climate Risks Academy 2021 - Contains solutions for the advanced assignment of the Modelling Lab of the Climate Risks Academy 2021.
    • +
  • PyEarthScience - Python modules, scripts and iPython notebooks, in particular for Earth System data processing and visualization used in climate science.
    • +
  • EDS220_Fall2021 - Provide an introduction to various environmental data sets, which should give you a good sense of the range of tools out there for manipulating and processing environmental data.
    • +
  • Climate Change Impact Assessment: A practical walk-through - Throughout the course of this book, you will learn how to acquire observed historical climate data from Environment and Climate Change Canada, and perform basic analyses of a climate index and meteorological variables.
    • +
  • LP DAAC E-Learning - Frequently updated presentations, webinars, tutorials, and video tips on accessing, managing, and processing LP DAAC data using a variety of software, web applications, custom tools, and scripts.
    • +
  • Copernicus Training - Data tutorials for the Copernicus Climate Change and Atmosphere Monitoring Services.
    • +
  • The Environmental Data Science book - A living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science.
    • +
  • Earthdata Cloud Cookbook - This Cookbook is learning-oriented to support scientific researchers using NASA Earthdata from Distributed Active Archive Centers as they migrate their workflows to the cloud.
    • +
  • Project Pythia - A community learning resource for Python-based computing in the geosciences.
    • +
  • MOOC Machine Learning in Weather & Climate - Explore the application of Machine Learning across the main stages of numerical weather and climate prediction.
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  • HPC4WC - High Performance Computing for Weather and Climate Course.
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  • ARSET Fundamentals of Machine Learning for Earth Science - This training will provide attendees an overview of machine learning in regards to Earth Science, and how to apply these algorithms and techniques to remote sensing data in a meaningful way.
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  • Applied Environmental Statistics - Applied Environmental Statistics course of the Boston University.
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  • ClimateMatch Academy - A wide-reaching, inclusive and approachable program aimed to introduce computational methods for climate science.
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  • climate-emulator-tutorial - A tutorial for getting started with machine learning for climate modeling.
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  • Utilizing Open Science Techniques for Exploring Greenhouse Gas Data - An Interactive Workshop on the U.S. Greenhouse Gas Center and VEDA Tools for use in Earth Science Research
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  • PyPRECIS - A learning tool to faciliate processing of regional climate model output.
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  • CSDMS Ivy - Course material on scientific software development for researchers in earth and planetary surface processes.
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  • Sketchbook Earth - A project aiming to illustrate the production of Climate Intelligence Reports, traditionally done with the ECMWF’s in-house developed tools.
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  • CMIP6 Cookbook - This Project Pythia Cookbook covers examples of analysis of Google Cloud CMIP6 data using Pangeo tools.
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  • PO.DAAC Tutorials - Make NASA’s ocean, climate, and surface water data universally accessible and meaningful.
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Earth Observation

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  • Radiant MLHub Tutorials - Tutorials to access Radiant MLHub Training Datasets.
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  • Fundamentals of Remote Sensing - Participants will have a basic understanding of NASA satellites, sensors, data, tools, portals and applications to environmental monitoring and management.
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  • Open Source Geoprocessing Tutorial - Tutorial of fundamental remote sensing and GIS methodologies using open source software in python.
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  • EO College - This course is part of a series of online learning materials that will give you insights on the potential of remote sensing technologies for applications over land surfaces.
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  • LPDAAC-Data-Resource - This repository is a place to find data user resources that demonstrate how to use NASA Land Processes Distributed Active Archive Center (LP DAAC) tools, services, and data.
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  • E-TRAINEE - Course on Time Series Analysis in Remote Sensing for Understanding Human-Environment Interactions
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  • GALUP - Build local capacity in the region of Ghana regarding remote sensing and GIS, and transfer the land use planning framework for continued planning.
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  • OCO2-OCO3 - For public accessibility and data visualization, a beginner-level tutorial is available for users interested in exploring the OCO2 and OCO3 data.
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  • Using NASA Satellite Data for Real-Time Environmental Monitoring - Using NASA's ECOSTRESS Satellite Data for Real-Time Environmental Monitoring.
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  • Landsat ML Cookbook - Covers the essential materials for working with Landsat data in the context of machine learning workflows.
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  • SERVIR Applied Deep Learning Book - Use satellite data and geospatial technology to address critical challenges in weather & climate resilience, agriculture and food security, ecosystem and carbon management, water security, disasters, as well as air quality and health
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  • HYR-SENSE - Hyperspectral and Thermal Remote Sensing for Environmental Justice" program. Participants will gain hands-on experience with hyperspectral and thermal imaging remote sensing technology and its applications for environmental justice issues.
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Earth Systems

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Atmosphere

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Cryosphere

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Biosphere

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Hydrosphere

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  • MarineEcosystemsJuliaCon2021.jl - Modeling Marine Ecosystems At Multiple Scales Using Julia.
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  • CoastWatch Satellite Course - The goal of the course is to familiarize university researchers and students with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research using their choice of software (R, python, ArcGIS).
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  • The Argo Online School - Teach the basic foundations to use and understand Argo an international program that collects information from inside the ocean using a fleet of floats that drift with the ocean currents.
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  • Analytical Groundwater Modeling - Analytical Groundwater Modeling: Theory and Applications Using Python.
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  • Intro to Physical Oceanography - Course materials for Introduction to Physical Oceanography.
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  • Coastal Dynamics Open Codebook - Discusses the interrelation between physical wave, flow, and sediment transport phenomena and the resulting morphodynamics of a wide variety of coastal systems.
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  • ECCO Hackweek 2024 Website - Explore NASA’s ECCO Ocean State Estimate, using Python, Julia, and cloud-based resources to work on oceanographic and climate data projects.
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  • Argo Online School - Teach the basic foundations to use and understand the Argo data, an international program that collects information from inside the ocean using a fleet of approximately 4,000 floats that drift with the ocean currents.
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Natural Resources

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Sustainable Development Goals

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Integrated Assessment Modelling and Climate Econometrics

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Open Sustainable Technology

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A directory and analysis of the open source ecosystem in the areas of climate change, sustainable energy, biodiversity and natural resources.

Earth's uniqueness in creating a stable environment for life in a completely hostile space is a miracle. Various life forms have taken billions of years to build up the natural resources humans depend on, such as a protective atmosphere, fertile soil, stable seasons, and clean drinking water. As a movement to democratize technology development and knowledge creation, open source has the potential to become the central driver in preserving this stability. Open Sustainable Technology's mission is to gather projects that preserve natural ecosystems through open technology, methods, data, intelligence, knowledge or tools.

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Your contribution is essential to keep this initative alive. Create a pull request to add a new project or send an email to give feedback, tips and ideas considering OpenSustain.tech. All Good First Issue labelled issues of the listed projects will be visible on ClimateTriage.com. All open source metadata is been provided to you by ecosyste.ms.

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Renewable Energy

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Photovoltaics and Solar Energy

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  • pvlib-python - A set of documented functions for simulating the performance of photovoltaic energy systems.
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  • pvfactors - Open source view-factor model for diffuse shading and bifacial PV modeling.
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  • gsee - Global Solar Energy Estimator.
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  • PVMismatch - An explicit Python PV system IV & PV curve trace calculator which can also calculate mismatch.
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  • rdtools - An open source library to support reproducible technical analysis of time series data from photovoltaic energy systems.
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  • Machine-Learning-for-Solar-Energy-Prediction - Predict the power production of a solar panel farm from weather measurements using machine learning.
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  • elpv-dataset - A dataset of functional and defective solar cells extracted from EL images of solar modules.
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  • feedinlib - Contains implementations of photovoltaic models to calculate electricity generation from a PV installation based on given solar radiation. Furthermore it contains all necessary pre-calculations.
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  • photovoltaic - A Python library used in photovoltaics.
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  • pvcaptest - Collection of functions and Jupyter Notebooks to partially automate running a capacity test following ASTM E2848.
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  • pvtrace - Optical ray tracing for luminescent materials and spectral converter photovoltaic devices.
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  • SolarPILOT - Solar power tower layout and optimization tool.
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  • solar-data-tools - Data analysis tools for working with historical PV solar time-series data sets.
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  • SolarPV-DER-simulation-utility - Allows user to run dynamics simulations for solar photovoltaic distributed energy resource connected to a stiff voltage source or to an external program.
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  • bifacial_radiance - Toolkit for working with RADIANCE for the ray-trace modeling of Bifacial Photovoltaics.
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  • autoXRD - A Python package for automatic XRD pattern classification of thin-films, tweaked for small and class-imbalanced datasets.
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  • BayesProcess - A Python package for Physics informed Bayesian network inference using neural network surrogate model for matching process / variable / performance in solar cells.
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  • solcore5 - A multi-scale, Python-based library for the modeling of solar cells and semiconductor materials.
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  • solax - Read energy usage data from the real-time API on Solax solar inverters.
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  • bifacialvf - Bifacial PV View Factor model for system performance calculation.
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  • solaR - Allows for reproducible research both for photovoltaics systems performance and solar radiation.
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  • SolarTherm - Solar thermal power/fuel station performance simulation and optimization using Modelica.
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  • LibreSolar - Firmware for LibreSolar BMS boards based on bq769x0 or ISL94202.
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  • Charge Controller Firmware - Firmware for LibreSolar MPPT/PWM charge controllers.
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  • pvoutput - Python code for downloading PV data from PVOutput.org.
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  • solarpy - This package aims to provide a reliable solar radiation model, mainly based on the work of Duffie, J.A., and Beckman, W. A., 1974, "Solar energy thermal processes".
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  • solariot - Leverage your IoT enabled Solar PV Inverter to stream your solar energy usage data to a real time dashboard.
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  • pvanalytics - Quality control, filtering, feature labeling, and other tools for working with data from photovoltaic energy systems.
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  • IonMonger - A free and fast perovskite solar cell simulator with coupled ion vacancy and charge carrier dynamics in one dimension.
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  • rayflare - Provide a flexible, user-friendly Python environment to model complex optical stacks, with a focus on solar cells.
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  • pv-terms - Contains nomenclature for PV-relevant terms that are used in modeling and data analysis for PV systems.
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  • StatisticalClearSky - Statistical estimation of a clear sky signal from PV system power data.
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  • Photovoltaic_Fault_Detector - Model-definition is a deep learning application for fault detection in photovoltaic plants.
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  • nasapower - Aims to make it quick and easy to automate downloading NASA-POWER global meteorology, surface solar energy and climatology data in your R session as a tidy data frame tibble object for analysis and use in modeling or other purposes.
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  • pvcompare - A model for comparing the benefits of different PV technologies in a specified local energy system in different energy supply scenarios.
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  • SolTrace - A software tool developed at NREL to model concentrating solar power (CSP) systems and analyze their optical performance.
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  • CarrierCapture.jl - A set of codes to compute carrier capture and recombination rates in semiconducting compounds like solar cells.
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  • honeybee - A Python library to create, run and visualize the results of daylight (RADIANCE) and energy analysis (EnergyPlus/OpenStudio).
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  • MPPT-Solar-Charger - Supporting documentation and software for the MPPT Solar Charger.
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  • Tonatiuh - A Monte Carlo ray tracer for the optical simulation of solar concentrating systems.
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  • PV4GER - Aims at democratizing and accelerating the access to photovoltaic systems data in Germany and beyond.
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  • PV Free - A public API for PV modeling parameters.
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  • Pysolar - A collection of Python libraries for simulating the irradiation of any point on earth by the sun. It includes code for extremely precise ephemeris calculations.
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  • PV_ICE - An open-source tool to quantify Solar Photovoltaics (PV) Energy and Mass Flows in the Circular Economy, from a Reliability and Lifetime approach.
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  • Solar electricity Nowcasting - Build the world's best near-term forecasting system for solar electricity generation.
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  • Solar Forecast Arbiter - Core data gathering, validation, processing, and reporting package for the Solar Forecast Arbiter.
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  • pv-system-profiler - Estimating PV array location and orientation from real-world power datasets.
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  • A Global Inventory of Commercial-, Industrial-, and Utility-Scale Photovoltaic Solar Generating Units - Used to produce a global inventory of utility-scale solar photovoltaic generating station.
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  • dGen - Forecast PV adoption based on user specified configurations like electricity rate prices, electricity load growth, solar resource factors, and much more.
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  • SOLECTRUS - An alternative photovoltaic dashboard that visualizes the yield and consumption.
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  • pvdeg - Set of tools to calculate degradation responses and degradation related parameters for PV.
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  • solarthing - An application that can monitor data from a variety of solar charge controllers and inverters.
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  • solXpect - Android app to forecast the output of your photovoltaic system (PV) or balcony pv using data from Open-Meteo.com.
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  • Solar Stations - A catalog of high-quality solar radiation monitoring stations.
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  • OTSun - A python package that uses the Monte Carlo Forward Ray Tracing for the optical analysis of Solar Thermal Collectors and Solar Cells.
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  • pvOps - Contains a series of functions to facilitate fusion of text-based data with time series production data collected at photovoltaic sites.
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  • CSP.guru - An open-source database of concentrating solar power plants of the world for energy modellers and analysts.
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  • Quartz Solar Forecast - The aim of the project is to build an open source PV forecast that is free and easy to use.
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  • Net Zero Citizens - Through this web and mobile applications, users in Mexico can effortlessly connect with solar panel installers and photovoltaic panel sellers, making the switch to solar energy seamless and straightforward.
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  • OpenPV - A website to simulate shading and annual PV yield based on 3D building data in the browser.
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  • OpenDTU - An alternative open source software for communicating with Hoymiles solar inverters, which synchronises everything with the s-Miles cloud.
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  • SimShady - Simulating Shadows for PV Potential Analysis on 3D Data in the Browser.
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  • InSPIRE - Here we will collect scripts, trainings, studies, and any other open-source material that can help further the understanding and research of agrivoltaics.
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  • twoaxistracking - A python package for simulating two-axis tracking solar collectors, particularly self-shading.
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  • huawei_solar - This integration exposes the information and functions made available by Huawei Solar inverters directly via one of its Modbus interfaces in Home Assistant.
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  • Sunsynk-Power-Flow-Card - A customizable Home Assistant card to emulate the Sunsynk System flow that's displayed on the Inverter screen.
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  • Solarman integration - Home Assistant component for interacting with Solarman data collectors used with a variety of inverters.
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  • SunPeek - Implements a dynamic, in situ test methodology for large solar thermal plants.
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Wind Energy

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  • DSWE - An R implementation of various data science methods for wind energy (DSWE) applications, such as providing machine learning methods to accurately estimate the wind power curve.
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  • IEA-15-240-RWT - A 15 MW reference wind turbine repository developed in conjunction with IEA Wind.
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  • windpowerlib - A library to model the output of wind turbines and farms.
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  • turbinesFoam - A library for simulating wind and marine hydrokinetic turbines in OpenFOAM using the actuator line method.
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  • nalu-wind - Solver for wind farm simulations targeting exascale computational platforms.
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  • openfast - A multi-physics, multi-fidelity tool for simulating the coupled dynamic response of wind turbines and wind farms.
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  • amr-wind - A massively parallel, block-structured adaptive-mesh, incompressible flow solver for wind turbine and wind farm simulations.
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  • OpenOA - This library provides a framework for working with large time series data from wind plants, such as SCADA.
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  • ROSCO - NREL's Reference OpenSource Controller for wind turbine applications.
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  • floris - A controls-oriented engineering wake modeling framework for evaluating the impact of wind farm controls on AEP and wind farm design.
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  • PyWake - An AEP calculator for wind farms implemented in Python including a collection of wake models.
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  • WISDEM - Wind Plant Integrated System Design and Engineering Model.
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  • WOMBAT - Windfarm Operations & Maintenance cost-Benefit Analysis Tool.
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  • LandBOSSE - The Land-based Balance-of-System Systems Engineering model is a systems engineering tool that estimates the balance-of-system costs associated with installing utility scale wind plants (10, 1.5 MW turbines or larger).
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  • TopFarm2 - A Python package developed by DTU Wind Energy to help with wind-farm optimizations.
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  • BasicDTUController - The scope of this project is to provide an open source, open access controller that can be used by the wind energy community as a reference.
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  • WindEnergyToolbox - A collection of Python scripts that facilitate working with (potentially a lot) of HAWC2,HAWCStab2, FAST or other text input based simulation tools.
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  • windfarmGA - Genetic algorithm to optimize the layout of wind farms.
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  • wtphm - The Wind Turbine Prognostics and Health Management library processes wind turbine events data, as well as operational SCADA data for easier fault detection, prognostics or reliability research.
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  • AirfoilPreppy - A Python module for pre-processing and evaluating aerodynamic airfoil data, primarily for wind turbine applications.
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  • Draco - A collection of end-to-end solutions for machine learning problems commonly found in monitoring wind energy production system.
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  • pyconturb - Constrained Stochastic Turbulence for Wind Energy Applications.
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  • ORBIT - Offshore Renewable Balance-of-system Installation Tool computes capital costs and activity times for offshore wind plant balance-of-system (everything besides the turbine) costs.
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  • WindTurbineClassification - Specification of 'normal' wind turbine operating behaviour for rapid anomaly detection.
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  • ANYstructure - Offshore Steel structure calculation tool with automatic optimization and report generation.
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  • SHARPy - Simulation of High Aspect Ratio aeroplanes and wind turbines in Python.
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  • WindSE - A Python package that uses a FEniCS backend to perform wind farm simulations and optimization.
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  • WEIS - WEIS is a framework that combines multiple tools to enable design optimization of floating offshore wind turbines.
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  • pyNuMAD - An object-oriented, open-source software written in Python which facilitates the creation and analysis of three-dimensional models of wind turbine blades.
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  • HAMS - An open-source computer program for the analysis of wave diffraction and radiation of three-dimensional floating or submerged structures.
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  • brightwind - A Python library aims to empower wind resource analysts and establish a common industry standard toolset.
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  • NRWAL - A library of offshore wind cost equations.
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  • welib - Wind energy library, python and matlab tools for wind turbines analyses.
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  • digital_wra_data_standard - This standard data model and associated tools are intended as universal building blocks for Wind Energy Resource Assessment applications.
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  • awebox - Modelling and optimal control of single- and multiple-kite systems for airborne wind energy.
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  • CCBlade.jl - A blade element momentum method for propellers and turbines.
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  • lidarwind - Retrieve wind speed and direction profiles from Doppler lidar observations from the WindCube-200s.
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  • FLOWUnsteady - An interactional aerodynamics and acoustics solver for multirotor aircraft and wind energy.
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  • stochLAB - A tool to run collision risk models for seabirds on offshore wind farms.
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  • Energy Research and Forecasting - Designed to provide a flexible computational framework for the exploration and investigation of different physics parameterizations and numerical strategies, and to characterize the flow field that impacts the ability of wind turbines to extract wind energy.
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  • FLOWFarm.jl - Wind farm simulation tool for gradient-based optimization.
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  • foxes - A modular wind farm simulation and wake modelling toolbox which is based on engineering wake models.
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  • CN-AeroModels - Developed to provide methods to compute the aerodynamic forces generated by wind propulsion systems for ships.
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Hydro Energy

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  • WecOptTool - Allows users to perform wave energy converter device design optimization studies with constrained optimal control.
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  • CACTUS - A turbine performance simulation code, based on a free wake vortex method, to study wind turbines and marine hydrokinetic devices.
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  • hydro-power-database - Collects basic information on all the European hydro-power plants.
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  • MHKiT-Python - Provides the marine renewable energy community tools for data processing, visualization, quality control, resource assessment, and device performance.
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  • hydropowerlib - Designed to calculate feed-in time series of run-of-the-river hydropower plants.
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  • HydroPowerSimulations.jl - Contains extensions on PowerSystems.jl and PowerSimulations.jl to enable enhanced hydropower representations.
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  • OpenHPL - An open source hydropower library that consists of hydropower unit models and is modeled using Modelica.
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  • WEC-Sim - Wave Energy Converter Simulator is an open source code for simulating wave energy converters.
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  • BEMRosetta - Used to model hydrodynamic forces in offshore devices like ships, offshore wind platforms and wave energy converters.
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  • Capytaine - A Python package for the simulation of the interaction between water waves and floating bodies in frequency domain.
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  • DOLPHYN - Evaluates investments and operations across the bulk supply chain for electricity and Hydrogen including production, storage, transmission, conditioning, and end-use consumption.
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  • ocp - Use AI to model and discover new catalysts for use in renewable energy storage to help in addressing climate change.
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  • wmpy-power - A hydropower simulation model developed to support long-term planning and climate impacts studies.
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Geothermal Energy

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  • pygfunction - An open source toolbox for the evaluation of thermal response factors of geothermal borehole fields.
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  • GHEtool - GHEtool is an open source Python package that contains all the functionalities needed to deal with borefield design.
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  • OpenGeoSys 6 - A scientific open source project for the development of numerical methods for the simulation of thermo-hydro-mechanical-chemical processes in porous and fractured media.
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  • FEHM - Has proved to be a valuable asset on a variety of projects of national interest including Environmental Remediation of the Nevada Test Site, the LANL Groundwater Protection Program, geologic CO2 sequestration, Enhanced Geothermal Energy programs, Oil and Gas production, Nuclear Waste Isolation, and Arctic Permafrost.
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  • thermo - Thermodynamics, phase equilibrium, transport properties and chemical database component of Chemical Engineering Design Library.
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  • waiwera - A parallel, open-source geothermal flow simulator.
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  • fractoolbox - Python tools for structural geology and borehole image analysis which includes data handling, frequency and geometric analysis, and reservoir geomechanics.
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  • GeoThermalCloud.jl - A repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration.
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  • GOLEM - A numerical simulator for modelling coupled Thermo-Hydro-Mechanical processes in faulted geothermal reservoirs.
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  • Ground Heat Exchanger Design Tool - A package with the novel capability of automatically selecting ground heat exchanger configurations based on polygonal land constraints.
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  • GEOPHIRES-X - Combines reservoir, wellbore, surface plant, and economic models to estimate the capital and operation and maintenance costs, instantaneous and lifetime energy production, and overall levelized cost of energy of a geothermal plant.
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Bioenergy

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  • biogas - Tools for biogas research in R: process biogas data and predict biogas production.
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  • biosteam - The Biorefinery Simulation and Techno-Economic Analysis Modules.
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  • BETYdb - Web-interface to the Biofuel Ecophysiological Traits and Yields Database.
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  • Low Carbon Fuel Standard - An online application for fuel suppliers to manage their compliance obligations under the Low Carbon Fuels Act.
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  • BiRD - A toolbox to simulate and analyze different designs of bioreactors in OpenFOAM.
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Energy Storage

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Battery

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  • bslib - Database with battery parameters based on PerMod as well as functions in order to simulate battery storages.
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  • impedance.py - A Python package for working with electro-chemical impedance data.
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  • PyBaMM - Fast and flexible physics-based battery models in Python.
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  • liionpack - A battery pack simulation tool that uses the PyBaMM framework.
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  • ENNOID-BMS - Open Source: Modular BMS based on LTC68XX & STM32 MCU for up to 400V EV battery pack.
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  • cellpy - Extract and tweak data from electro-chemical tests of battery cells.
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  • BatterySense - A simple Arduino library to monitor battery consumption of your battery powered projects, being LiPo, LiIon, NiCd or any other battery type, single or multiple cells: if it can power your Arduino you can monitor it.
    • +
  • beep - A set of tools designed to support Battery Evaluation and Early Prediction of cycle life corresponding to the research of the d3batt program and the Toyota Research Institute.
    • +
  • snl-quest - An open source, Python-based software application suite for energy storage simulation and analysis developed by Sandia National Laboratories.
    • +
  • simses - Software for techno-economic Simulation of Stationary Energy Storage Systems.
    • +
  • CAEBAT OAS - A flexible, robust, and computationally scalable open-architecture framework that integrates multi-physics and multi- scale battery models.
    • +
  • universal-battery-database - The Universal Battery Database is an open source software for managing Lithium-ion cell data.
    • +
  • open_BEA - Open Battery Models for Electrical Grid Applications.
    • +
  • lp_opt - Linear Programming Optimization Tool for Battery Energy Storage Systems.
    • +
  • SLIDE - SLIDE is a C++ code that simulates degradation of lithium ion cell.
    • +
  • equiv-circ-model - An equivalent circuit model for a battery cell, module, and pack.
    • +
  • ISEAFramework - Allows coupled electrical-thermal simulations of single storage systems (e.g. lithium ion batteries or double layer capacitors) or complete storage system packs.
    • +
  • Ampere - Advanced Model Package for ElectRochemical Experiments.
    • +
  • 3d_milp - Energy Arbitrage Optimization With Battery Storage.
    • +
  • LIONSIMBA - A Matlab framework based on a finite volume model suitable for Li-ion battery design, simulation, and control.
    • +
  • emobpy - An open tool for creating battery-electric vehicle time series from empirical data.
    • +
  • BattMo - The Battery Modelling Toolbox (BattMo) is a resource for continuum modelling of electrochemical devices in MATLAB.
    • +
  • LiBRA - Create reduced-order state-space models for lithium-ion batteries utilising realisation algorithms.
    • +
  • PyBOP - Provides a comprehensive suite of tools for parameterisation and optimisation of battery models.
    • +
  • MPET - Run simulations of batteries with porous electrodes using porous electrode theory, which is a volume-averaged, multiscale approach to capture the coupled behavior of electrolyte and active material within electrodes.
    • +
  • BIG-MAP - The Battery Interface Genome Materials Acceleration Platform is promoting accelerated discovery of novel battery materials.
    • +
  • BatteryML - An Open-Source Tool for Machine Learning on Battery Degradation.
    • +
  • Battery Data Toolkit - Converts battery testing data from native formats to a standardized HDF5 file.
    • +
  • galpynostatic - A Python/C++ package with physics-based models to predict optimal conditions for fast-charging lithium-ion batteries.
    • +
  • bms-c1 - A flexible Open Source Battery Management System (BMS) suitable for various applications.
    • +
  • Battery-Emulator - This software enables EV battery packs to be used for stationary storage in combination with solar inverters.
    • +
  • BLAST-Lite - Battery Lifetime Analysis and Simulation Toolsuite provides a library of battery lifetime and degradation models for various commercial lithium-ion batteries from recent years.
    • +
  • RFB-dev-kit - A benchtop flow battery development kit, extendable to other electrochemical systems with flow.
    • +
  • FAIR-Battery - We seek to present an open-hardware platform for a versatile battery technology and make the platform radically accessible.
    • +
+

Hydrogen

+
    +
  • OPEM - A modeling tool for evaluating the performance of proton exchange membrane fuel cells.
    • +
  • gopem - GOPEM is a graphical user interface of OPEM.
    • +
  • pem-dataset1 - Proton Exchange Membrane Fuel Cell Dataset.
    • +
  • HIM - Hydrogen Infrastructure model for the analysis of spatially resolved hydrogen infrastructure pathways.
    • +
  • pandapipes - A pipeflow calculation tool that complements pandapower in the simulation of multi energy grids, in particular heat and gas networks.
    • +
  • The Hydrogen Risk Assessment Models - The first-ever software toolkit that integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards impact on people and structures.
    • +
  • GasModels.jl - A Julia/JuMP Package for Gas Network Optimization.
    • +
  • SciGRID_gas - Methods to create an automated network model of the European gas transportation network.
    • +
  • Vehicle with Fuel Cell Powertrain - Fuel cell electric vehicle with battery model and cooling system.
    • +
  • VirtualFCS - A Modelica library for hybrid hydrogen fuel cell and battery power systems.
    • +
  • GEOH2 - Calculates the locational cost of green hydrogen production, storage, transport, and conversion to meet demand in a specified location.
    • +
+

Energy Systems

+

Building Energy Monitoring

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    +
  • Open Energy Dashboard - Open Energy Dashboard is a user-friendly way to display energy information from smart energy meter.
    • +
  • Emoncms - A powerful open source web application for processing, logging and visualizing energy, temperature and other environmental data.
    • +
  • Open Energy View - The goal of this project is to analyze and present resource consumption data to users empowering them to conserve and save money.
    • +
  • Green Monitoring Stack - An open-source stack that monitors the network, data center (compute / storage) infrastructure, third-party devices and external sources to understand the energy consumption and its relation to traffic and bandwidth, the cost and the carbon footprint at various levels of granularity.
    • +
  • FlexMeasures - A platform for building energy flexibility services with forecasting and scheduling, written in Python & offering a USEF-conform API.
    • +
  • OpenEMS - Open Source Energy Management System.
    • +
  • SolarNetwork - Flexible platform for the monitoring and control of energy assets, meters, sensors, weather stations, EV chargers, market pricing, load optimization, and more.
    • +
  • emonpi - The OpenEnergyMonitor system has the capability to monitor electrical energy use / generation, temperature and humidity.
    • +
  • EmonLib - Arduino Energy Monitoring Library.
    • +
  • STM32 Energy Monitoring - The following resources are a work in progress guide to using the STM32 platform for energy monitoring, being written as part of development work into the next generation of OpenEnergyMonitor hardware.
    • +
  • OpenSmartMeter - Low-cost & OEM agnostic GSM prepaid smart energy meter with a customizable API.
    • +
  • EHMASS - Energy Management for Home Assistant, is a Python module designed to optimize your home energy interfacing with Home Assistant.
    • +
  • PowerCalc - Custom component for Home Assistant to estimate the power consumption (as virtual meters) of lights, fans, smart speakers and other devices, which don't have a built-in power meter.
    • +
  • homeassistant-alphaESS - Monitor your energy generation, storage, and usage data using the official Open API from Alpha ESS.
    • +
  • eemeter - An open source Python package for implementing and developing standard methods for calculating normalized metered energy consumption and avoided energy use.
    • +
  • Home Assistant Octopus Energy - Bring your Octopus Energy details into Home Assistant to help you towards a more energy efficient (and or cheaper) home.
    • +
  • Home Assistant Glow - Makes a (not so) smart meter without a P1 port easily readable, reading the pulse LED that is always present in most cases and it works with ESPHome!
    • +
  • DSMR Toolkit - A set of Java based libraries and tools that should allow processing Dutch Smart Meter Requirements messages in a way that is as flexible and reusable as possible.
    • +
  • myenergi for Home Assistant - Home Assistant integration for MyEnergi renewable energy products and ev chargers.
    • +
  • Sankey Chart Card - A Home Assistant lovelace card to display a sankey chart showing energy, power and water consumption.
    • +
+

Energy System Modeling Frameworks

+
    +
  • Open Energy System Models - Used to explore future energy systems and are often applied to questions involving energy and climate policy.
    • +
  • Open Energy System Databases - Employ open data methods to collect, clean, and republish energy-related datasets for open use.
    • +
  • PyPSA - A free software toolbox for simulating and optimizing modern power systems that include features such as conventional generators with unit commitment, variable wind and solar generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks.
    • +
  • whobs-server - This is the code for the online optimization of zero-direct-emission electricity systems with wind, solar and storage (using batteries and electrolysed hydrogen gas) to provide a baseload electricity demand, using the cost and other assumptions of your choice.
    • +
  • System Advisor Model - A simulation program for electricity generation projects. It has models for different kinds of renewable energy systems and financial models for residential, commercial, and utility-scale projects.
    • +
  • openTEPES - Determines the investment plans of new facilities (generators, ESS and lines) for supplying the forecasted demand at minimum cost.
    • +
  • REopt_API - Offers a subset of features from NREL's more comprehensive REopt model. Both models provide concurrent, multiple technology integration and optimization capabilities to help organizations meet their cost savings and energy performance goals.
    • +
  • pandapower - An easy to use open source tool for power system modeling, analysis and optimization with a high degree of automation.
    • +
  • urbs - A linear optimization model for distributed energy systems.
    • +
  • Dispa-SET - Allows to model a power system at any level of detail e.g. micro-grid, region, country, continent.
    • +
  • Calliope - A framework to develop energy system models, with a focus on flexibility, high spatial and temporal resolution, the ability to execute many runs based on the same base model, and a clear separation of framework and model.
    • +
  • OSeMOSYS - An open source modeling system for long-run integrated assessment and energy planning. It has been employed to develop energy systems models from the scale of continents (African Power Pools, South America, EU28+2) down to the scale of countries, regions and villages.
    • +
  • FINE - Provides a framework for modeling, optimizing and assessing energy systems.
    • +
  • CoMPAS - Formed to develop open source software components related to IEC 61850 model implementation (profile management) and configuration of a power industry Protection Automation and Control System.
    • +
  • PowerSimulations.jl - A Julia package for power system modeling and simulation of Power Systems operations.
    • +
  • PowerSystems.jl - Provides a rigorous data model using Julia structures to enable power systems analysis and modeling.
    • +
  • DistAIX - A simulator for cyber-physical power systems that makes use of high performance computing techniques to scale up the simulation.
    • +
  • Antares Simulator - An Open Source power system simulator to quantify the adequacy or the economic performance of interconnected energy systems, at short or remote time horizons.
    • +
  • HELICS - Today the core uses are in the energy domain, where there is extensive and growing support for a wide-range of electric power system, natural gas, communications and control-schemes, transportation, buildings, and related domain tools.
    • +
  • oemof-solph - A model generator for energy system modeling and optimization.
    • +
  • oemof-thermal - Provides tools to model thermal energy components as an extension of oemof.solph, e.g. compression heat pumps, concentrating solar plants, thermal storage and solar thermal collectors.
    • +
  • VILLASnode - Connecting real-time power grid simulation equipment.
    • +
  • Joulia.jl - A Large-Scale Spatial Power System Model for Julia.
    • +
  • The IDAES Toolkit - Aims to provide multi-scale, simulation-based, open source computational tools and models to support the design, analysis, optimization, scale-up, operation and troubleshooting of innovative, advanced energy systems.
    • +
  • Temoa - Tools for Energy Model Optimization and Analysis (Temoa) is an open source modeling framework for conducting energy system analysis.
    • +
  • PowerSystemDataModel - Provides an extensive data model capable of modeling energy systems with high granularity e.g. for bottom-up simulations.
    • +
  • antaresViz - Visualize the results of Antares, an Open Source power system simulator meant to be used by anybody placing value in quantifying the adequacy or the economic performance of interconnected energy systems, at short or remote time horizons.
    • +
  • antaresRead - Import, manipulate and explore the results of an Antares simulation.
    • +
  • Spine-Toolbox - An application to define, manage, and execute various energy system simulation models.
    • +
  • OMEGAlpes - Aims to be an energy systems modeling tool for linear optimization (LP, MILP).
    • +
  • deflex - Flexible multi-regional energy system model for heat, power and mobility.
    • +
  • energy-py-linear - Optimizing energy systems using mixed integer linear programming.
    • +
  • AnyMOD.jl - Creating large scale energy system models with multiple periods of capacity expansion formulated as linear optimization problems.
    • +
  • FlexiGIS - Extracts, filters and categorizes the geo-referenced urban energy infrastructure and allocates the required decentralized storage in urban settings.
    • +
  • CapacityExpansion.jl - Future energy system planning (Generation and Transmission Capacity Expansion Planning) in Julia.
    • +
  • DPsim - A solver library for dynamic power system simulation.
    • +
  • GenX - A highly-configurable, open source electricity resource capacity expansion model that incorporates several state-of-the-art practices in electricity system planning to offer improved decision support for a changing electricity landscape.
    • +
  • Open Energy Platform - Aims to ensure quality, transparency and reproducibility in energy system research. It is a collection of various tools and information and that help working with energy-related data.
    • +
  • oemof - Open Energy Modelling Framework - A Python toolbox for energy system modeling and optimization. A community driven, modular, flexible and generic software project.
    • +
  • PowSyBl - An open source framework written in Java, that makes it easy to write complex software for power systems simulations and analysis.
    • +
  • PowSyBl Open Load Flow - An open source library written in Java, that makes it easy to write complex software for power systems simulations and analysis.
    • +
  • PyPowSyBl - A Python library for modelling and simulating power grids by providing bindings to the powsybl Java framework.
    • +
  • matpower - A package of M-files for solving power flow, continuation power flow and optimal power flow problems using MATLAB or Octave.
    • +
  • energyRt - Making Energy Systems Modeling as simple as a linear regression in R.
    • +
  • MVS - The multi-vector simulator allows the evaluation of local sector-coupled energy systems that include the energy carriers electricity, heat and/or gas.
    • +
  • PowNet - A least-cost optimization model for simulating the Unit Commitment and Economic Dispatch of large-scale (regional to country) power systems.
    • +
  • OpenIPSL - A library of power system component models written in the Modelica language that can be used for power system dynamic analysis, such as phasor time-domain simulations.
    • +
  • RAMP - A bottom-up stochastic model for the generation of high-resolution multi-energy profiles, conceived for application in contexts where only rough information about users' behaviour are obtainable.
    • +
  • energy-py - Reinforcement learning for energy systems.
    • +
  • glaes - Geospatial Land Availability for Energy Systems.
    • +
  • onsset - A GIS based optimization tool that has been developed to support electrification planning and decision making for the achievement of energy access goals in currently unserved locations.
    • +
  • rl-testbed-for-energyplus - Reinforcement Learning Testbed for Power Consumption Optimization using EnergyPlus.
    • +
  • tsam - A Python package which uses different machine learning algorithms for the aggregation of time series.
    • +
  • GridPath - A versatile simulation and optimization platform for power-system planning and operations.
    • +
  • ANDES - Power system transient dynamics simulation with symbolic modeling and numerical analysis.
    • +
  • ESDL - A modelling language created for the components in an energy system and their relations towards each other.
    • +
  • Transactive Energy Service System - A platform to design, deploy, and operate transactive energy systems in electric utility retail environments.
    • +
  • Minpower - An open source toolkit for students and researchers in power systems.
    • +
  • Frictionless Energy data - Common medium to facilitate the flow of data between energy and environmental models in a way that can be automated.
    • +
  • Open Modeling Framework - A set of Python libraries for simulating power systems behavior with an emphasis on cost-benefit analysis of emerging technologies.
    • +
  • PSP-UFU - Open-Source Software with advanced GUI features and CAD tools for electrical power system studies.
    • +
  • GENeSYS-MOD - An open-source energy system model, originally based on the Open-Source Energy Modeling System (OSeMOSYS) framework, with various additions.
    • +
  • PREP-SHOT - A transparent, modular, and open-source Energy Capacity Expansion Model.
    • +
  • HYBRID - A modeling toolset to assess the integration and economic viability of Integrated Energy Systems.
    • +
  • FAME - Its purpose is supporting the rapid development and fast execution of complex agent-based energy system simulations.
    • +
  • REMix - The main focus is on the broad techno-economical assessment of possible future +energy system designs and analysis of interactions between technologies.
    • +
  • StorageVET - A valuation model for analysis of energy storage technologies and some other energy resources paired with storage.
    • +
  • OpenDER - Aims to accurately represent steady-state and dynamic behaviors of inverter-based distributed energy resources.
    • +
  • ETM Pro - Professional interface of the Energy Transition model.
    • +
  • ETSource - Contains the data used by Quintel energy transition modelling applications, allowing you to explore possible future energy systems for your country, region or municipality.
    • +
  • MESMO - An open-source Python tool for the modeling, simulation and optimization of multi-scale electric and thermal distribution systems along with distributed energy resources.
    • +
  • NemoMod.jl - A high performance, open-source energy system optimization modeling tool developed in Julia.
    • +
  • Dynaωo - Aims at providing power system stakeholders with a transparent, flexible, interoperable and robust suite of simulation tools that could ease collaboration and cooperation in the power system community.
    • +
  • TIMES_model - A bottom-up optimization model used for the exploration of possible energy futures based on contrasted scenarios.
    • +
  • MUSE_OS - A open source agent-based modelling environment that can be used to simulate change in an energy system over time.
    • +
  • Energy Transition Engine - Calculation engine for the Energy Transition Model.
    • +
  • enerflow - An open-source Python framework that enables energy data scientists and modellers to write modular and reproducible energy models that solves sequential decision problems.
    • +
  • Energy System Simulation and Optimization - An open-source energy optimization system that integrates renewable energy sources, such as photovoltaic (PV) systems, battery storage, and smart load management solutions.
    • +
  • SpineOpt.jl - A highly adaptable modelling framework for multi-energy systems written in Julia.
    • +
  • BlueSky - An EIA initiative to develop an open source, next generation energy systems model, which will eventually be used to produce the Annual Energy Outlook (AEO) and International Energy Outlook (IEO).
    • +
+

Energy Markets

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    +
  • Grid Singularity Energy Exchange - An interface to download and deploy interconnected, grid-aware energy marketplaces.
    • +
  • origin - A set of toolkits that together provide a system for issuance and management of Energy Attribute Certificates.
    • +
  • AMIRIS-Py - Python tools for the electricity market model AMIRIS.
    • +
  • ASSUME - An open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup.
    • +
  • NEMS - A long-term energy-economy modeling system of U.S. energy markets.
    • +
  • POMATO - An easy to use tool for the comprehensive analysis of the modern electricity market.
    • +
  • pommesdispatch - A bottom-up fundamental power market model for the German electricity sector.
    • +
  • openCEM - Capacity Expansion Model and Optimiser for the Australian National Energy Market.
    • +
  • NEMO - The National Electricity Market Optimizer is a chronological dispatch model for testing and optimizing different portfolios of conventional and renewable electricity generation technologies.
    • +
+

Energy Data Accessibility and Integration

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    +
  • OpenNEM - Aims to make the wealth of public Australian Electricity Market data more accessible to a wider audience.
    • +
  • The Public Utility Data Liberation Project - Makes the US' energy data easier to access and use.
    • +
  • Open Data Access Tools - Aims to improve and automate access to high-value energy datasets across the U.S. Department of Energy's programs, offices, and national laboratories, and help researchers and industry to accelerate analysis and advance innovation.
    • +
  • Power Station Dictionary - A power station dictionary that enables mapping between various naming conventions and associated plant metadata.
    • +
  • powerplantmatching - A toolset for cleaning, standardizing and combining multiple power plant databases.
    • +
  • TransitionZero Python Client - This Python Client gives programmatic access to all the functionality of TransitionZero, a not-for-profit set to decarbonise energy systems by supporting global businesses and emerging market governments with open, granular and accessible data.
    • +
  • entsoe-py - A Python client for the ENTSO-E API (European Network of Transmission System Operators for Electricity).
    • +
  • pyEIA - An Energy Information Administration API Python client for researchers who need data.
    • +
  • EIA - An R package wrapping the US Energy Information Administration open data API.
    • +
  • eiapy - A simple wrapper for the U.S. Energy Information Administration API.
    • +
  • feo-client - Crafted for energy systems modellers seeking access to data for nodes, assets, technologies, as well as the data supporting models and scenarios published by TransitionZero.
    • +
  • EIAdata - Provides programmatic access to the Energy Information Administration's API.
    • +
  • PowerGenome - A tool to quickly and easily create inputs for power systems models.
    • +
  • conventional power plants - Contains data on conventional power plants for Germany as well as other selected European countries. The data include individual power plants with their technical characteristics.
    • +
  • Open Power System Data - A list of primary data sources that are helpful for power system modeling of Europe.
    • +
  • time series - Contains scripts that compile time series data of the European power system.
    • +
  • Power grid frequency data base - This data set contains precisely time stamped (GPS referenced) frequency data from several power grids around the world in one second resolution and 1 hour excerpts of raw data.
    • +
  • gridstatus - Provides standardized API to access energy data from the major Independent System Operators in the United States.
    • +
  • ElexonDataPortal - Wrapper for the Balancing Mechanism Reporting Service API to balance power flowing on to and off from the electricity Transmission System in Great Britain.
    • +
  • NYISOToolkit - A collection of modules for accessing power system data, generating statistics, and creating visualizations from the New York Independent System Operator.
    • +
  • Global Power Plant Database - A comprehensive, global and open source database of power plants.
    • +
  • GeoNuclearData - Database with information about Nuclear Power Plants worldwide.
    • +
  • UKgrid - An R data package with the UK National Grid historical demand for electricity between April 2005 and October 2019.
    • +
  • ESIOS - Comprehensive library to access the Spanish electricity market entity in Python.
    • +
  • energy-data - Data on global energy consumption (primary energy, per capita, and growth rates), energy mix, electricity mix and other relevant metrics.
    • +
  • EnergyData.Info - An open data platform from the World Bank Group providing access to datasets and data analytics that are relevant to the energy sector.
    • +
  • The FfE Open Data Portal - Offers an overview of free datasets for modelling energy demand and generation.
    • +
  • open-MaStR - Download and process German energy data from BNetzA database Marktstammdatenregister.
    • +
  • Energy System Technology Data - Compilation of assumptions about energy system technologies such as cost, efficiency and lifetime that can be read by energy system modelling software.
    • +
  • GEEST - Aims to propose a novel methodology and generate a geospatial open-source tool for mapping the enabling environments for women in a country that can inform new energy projects to support the advancement of women's economic empowerment in small island developing states while contributing to closing gender gaps in employment in the renewable energy sector.
    • +
  • Annual Technology Baseline Calculators - To inform electric and transportation sector analysis in the United States, each year NREL provides a robust set of modeling input assumptions for energy technologies and a diverse set of potential electricity generation futures or modeling scenarios.
    • +
  • ETLocal - Countries, provinces, municipalities, districts and neighborhoods have their own unique questions for their energy systems.
    • +
  • RE4AFAGRI - A multi-model framework to analyse deficits, requirements, and optimal solutions for integrated land-water-agriculture-energy-development nexus interlinkages in developing countries.
    • +
  • ENERGYDATA.INFO - An open data platform created by the World Bank, providing access to datasets and data analytics that are relevant to the energy sector.
    • +
  • jao-py - Python client for the various endpoints offered by the Joint Allocation Office, Europe's single leading trading platform for cross-border transmission capacity.
    • +
+

Energy Policy

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    +
  • Energy Policy Simulator - The open-source United States Energy Policy Simulator estimates environmental and economic impacts of hundreds of climate and energy policies.
    • +
  • Open Energy Tracker - An open data platform for monitoring and visualizing energy policy targets.
    • +
  • EMILE - An application to aggregate energy consumption data across several municipal assets and report usage to monitor and manage emissions.
    • +
  • AMIRIS - An agent-based simulation of electricity markets and their actors enabling researchers to analyse and evaluate energy policy instruments and their impact on the actors.
    • +
+

Grid Analysis and Planning

+
    +
  • Egret - A Python-based package for electrical grid optimization based on the Pyomo optimization modeling language.
    • +
  • eDisGo - Optimization of flexibility options and grid expansion for distribution grids based on PyPSA.
    • +
  • FlexPlan.jl - An open-source Julia tool for transmission and distribution expansion planning considering storage and demand flexibility.
    • +
  • ExaGO - High-performance power grid optimization for stochastic, security-constrained, and multi-period ACOPF problems.
    • +
  • DISPATCHES - Developed and used to identify and optimize Integrated Energy Systems for operation within the bulk power system via energy market signals.
    • +
  • EMMA - A techno-economic model of the north-west European power market covering France, Benelux, Germany and Poland.
    • +
  • TulipaEnergyModel.jl - Provides an optimization model for the electricity market and its coupling with other energy sectors (e.g., hydrogen, heat, natural gas, etc.) with the main objective to determine the optimal investment and operation decisions for different types of assets.
    • +
  • SimBench - The objective of the research project SimBench is the development of a benchmark dataset to support research in grid planning and operation.
    • +
  • GElectrical - A free and opensource electrical system analysis software for LV/MV electrical distribution networks.
    • +
  • GridPACK - An open-source high-performance package for simulation of large-scale electrical grids.
    • +
  • Easy SimAuto - An easy-to-use Power System Analysis Automation Platform atop PowerWorld's Simulator Automation Server.
    • +
  • SIMONA - Provides a simulation toolbox to run and implement large-scale agent-based electricity grid simulations with focus on distribution grids.
    • +
  • Power Grid Model - A library for steady-state distribution power system analysis distributed for Python and C.
    • +
  • ElectricGrid.jl - A time domain electrical energy grid modeling and simulation tool with a focus on the control of power electronics converters.
    • +
  • GridLAB-D - A simulation platform for future electricity distribution power systems.
    • +
  • PowerSimData - Is part of a Python software ecosystem developed by Breakthrough Energy Sciences to carry out power flow study in the U.S. electrical grid.
    • +
  • PowerModels.jl - Designed to enable computational evaluation of emerging power network formulations and algorithms in a common platform.
    • +
  • PowerModelsAnnex.jl - An extension of PowerModels.jl that provides a home for open source sharing of preliminary and/or exploratory methods in power system optimization.
    • +
  • Power Grid Lib - This benchmark library is curated and maintained by the IEEE PES Task Force on Benchmarks for Validation of Emerging Power System Algorithms and is designed to evaluate a well established version of the AC Optimal Power Flow problem.
    • +
  • pypownet - A power network simulator with a Reinforcement Learning-focused usage.
    • +
  • Grid2Op - A testbed platform to model sequential decision making in power systems.
    • +
  • RTS-GMLC - Reliability Test System of the Grid Modernization Lab Consortium.
    • +
  • OpenDSS - An electric power Distribution System Simulator for supporting distributed resource integration and grid modernization efforts.
    • +
  • PowerDynamics.jl - Provides all the tools you need to create a dynamic power grid model and analyze it.
    • +
  • GridCal - Aims to be a complete platform for power systems research and simulation.
    • +
  • Backbone - A generic energy network optimization tool written in GAMS.
    • +
  • mosaik - A flexible Smart Grid co-simulation framework.
    • +
  • SmartGridToolbox - Designed to provide an extensible and flexible starting point for developing a wide variety of smart grid simulations and other applications.
    • +
  • OSMoGrid - A java tool to generate life like electrical grid models based on publicly available data, mainly OpenStreetMap and a special focus on low voltage grids.
    • +
  • Open Infrastructure Map - A view of the world's energy and water infrastructure mapped in the OpenStreetMap database.
    • +
  • MicroGridsPy - Provide an open-source alternative to the problem of sizing and dispatch of energy in micro-grids in isolated places.
    • +
+

Grid Management and Microgrid

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    +
  • Open Smart Grid Platform - An open, generic, scalable and independent 'Internet of Things' platform, which enables various connected smart objects in the public space to be easily controlled and monitored.
    • +
  • openleadr - Open Automated Demand Response (OpenADR) is an open and interoperable information exchange model and emerging smart grid standard.
    • +
  • SEAPATH - Aims at developing a reference design and industrial grade open source real-time platform that can run virtualized automation and protection applications for the power grid industry.
    • +
  • InfrastructureSystems.jl - Provides utilities to support data models for infrastructure modeling in NREL-SIIP.
    • +
  • SciGRID - Its intention is to develop methods for the automated generation of models (i.e. maps) of existing electricity grids for research and other purposes.
    • +
  • GreenForce - National energy transmission system operator data hub developing to support change toward decarbonised economies.
    • +
  • OperatorFabric - A modular, extensible, industrial-strength and field-tested platform for use in electricity, water and other utility operations.
    • +
  • energy-sparks - An open source application that is designed to help schools improve their energy efficiency.
    • +
  • offgridders - Models and optimizes capacity & dispatch of electricity supply systems, off-grid or connected to a (weak) central grid.
    • +
  • openmodelica-microgrid-gym - An OpenAI Gym Environment for Microgrids.
    • +
  • APIS - Build a microgrid that utilizes variable renewable energy as the main power source and enhances the resilience of the power system.
    • +
  • python-microgrid - A Python library to generate and simulate a large number of microgrids.
    • +
  • CLOVER - A minigrid simulation and optimisation for supporting rural electrification in developing countries.
    • +
  • Gym-ANM - Design Reinforcement Learning environments that model Active Network Management tasks in electricity distribution networks.
    • +
  • PowerModelsACDC.jl - A a Julia/JuMP/PowerModels package with models for DC lines, meshed DC networks, and AC DC converters.
    • +
  • pyehub - A Python-based, modular and nestable implementation of the Energy Hub model (balancing demand and supply, system capacity sizing and network flows using Mixed-Integer Linear Programming).
    • +
  • Electra - Sovereign blockchain solution that enables local micro-grid to operate smoothly between trustless actors enabling a real circular economy based on the exchange of electricity units.
    • +
  • Vessim - A co-simulation testbed for energy-aware and carbon-aware applications and systems, based on Mosaik.
    • +
  • VILLASnode - Used in distributed- and co-simulation scenarios and developed for the field of power grid simulation at the EON Energy Research Center in Aachen.
    • +
+

Load and Demand Forecasting

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    +
  • tell - An open-source Python package to model future hourly total electricity loads.
    • +
  • load_forecasting - Load forecasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models.
    • +
  • draf - Analysis and decision support framework for local multi-energy hubs focusing on demand response.
    • +
  • OpenSTEF - A Python package which is used to make short term forecasts for the energy sector.
    • +
  • CityLearn - Official reinforcement learning environment for demand response and load shaping.
    • +
  • disaggregator - A set of tools for processing of spatial and temporal disaggregations of demands of electricity, heat and natural gas.
    • +
  • demandlib - With the demandlib you can create power and heat profiles for various sectors by scaling them to your desired demand.
    • +
  • dsgrid - Enables the compilation of high-resolution load datasets suitable for forward-looking power system and other analyses.
    • +
+

Regional Energy System Models

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    +
  • PyPSA-Earth - An Open Optimisation Model of the Earth Energy System.
    • +
  • PyPSA-Eur - A Sector-Coupled Open Optimization Model of the European Transmission System.
    • +
  • PyPSA-FES - A model that optimises investment and operation, and incorporates flexibility domestic demand flexibility to model scenarios for Great Britain's future energy system.
    • +
  • pypsa-usa - An open-source power systems model of the bulk transmission systems in the United States.
    • +
  • 24/7 CFE - This project explores the means, costs and impacts of 24/7 Carbon-Free Energy procurement in Europe.
    • +
  • TIMES-Ireland Model - Information on the Irish energy system as it is today and the best available projections for what the future technology and fuel options and demands will be.
    • +
  • Euro-Calliope - A model of the European electricity system built using Calliope.
    • +
  • ReEDS - A capacity planning and dispatch model for the North American electricity system.
    • +
  • nempy - Aims to enhance the Australian electricity industries modeling and analytical capabilities.
    • +
  • openENTRANCE - The Horizon 2020 openENTRANCE project aims at developing, using and disseminating an open, transparent and integrated modeling platform for assessing low-carbon transition pathways in Europe.
    • +
  • PyPSA-DE - High resolution, sector-coupled model of the German Energy System.
    • +
  • OSeMBE_ECEMF - Contains the energy future scenarios modelled in OSeMBE as part of the ECEMF project, which brings together energy and climate researchers and policy makers to address the central challenge of our time - how to achieve climate neutrality.
    • +
  • PyPSA-PL - An implementation of the energy modelling framework PyPSA shipped with a use-ready dataset tailored for the Polish energy system.
    • +
  • PyPSA-GB - An open dataset and power dispatch model of the GB transmission network using country-specific data over historical years and for future energy scenarios.
    • +
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Renewable Energy Integration

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    +
  • OPEN - The framework combines distributed energy resource modelling (e.g. for PV generation sources, battery energy storage systems, electric vehicles), energy market modelling, power flow simulation and multi-period optimisation for scheduling flexible energy resources.
    • +
  • SESMG - An energy system model generator with the focus on the optimization of urban energy systems.
    • +
  • optihood - A python-based framework to optimize the investment in alternative energy technologies as well as the operation of available energy resources for decentralized energy networks on a neighbourhood-scale.
    • +
  • HOPP - Assesses optimal designs for the deployment of utility-scale hybrid energy plants, particularly considering wind, solar and storage.
    • +
  • REISE.jl - Renewable Energy Integration Simulation Engine.
    • +
  • PowerGAMA - A lightweight simulation tool for high level analyses of renewable energy integration in large power systems.
    • +
  • reV - Enables the efficient and scalable computation of renewable energy generation, levelized cost of energy, application of geospatial exclusion layers, and generation of renewable energy supply curves.
    • +
  • pyGRETA - Python Generator of REnewable Time series and mAps: a tool that generates high-resolution potential maps and time series for user-defined regions within the globe.
    • +
  • RESKit - A toolkit to help generate renewable energy generation time series for energy systems analysis.
    • +
  • switch-model - Optimal planning model for power systems with large shares of renewable energy.
    • +
  • reVX - Renewable Energy Potential(V) eXchange Toot.
    • +
  • dieter_py - An open source power sector optimization model that has been developed to investigate the role of electricity storage and sector coupling options in future scenarios with high shares of renewable energy sources.
    • +
  • REVUB - The main objective is to model how flexible operation of hydropower plants can help renewable electricity mixes with variable solar and wind power to provide reliable electricity supply and load-following services.
    • +
  • GlobalEnergyGIS - Generates input data for energy models on renewable energy in arbitrary world regions using public datasets.
    • +
  • renewable power plant - Contains scripts to create lists of renewable power plants in Germany, Denmark, France and Poland, and daily time series of cumulated installed capacity per energy source type for Germany.
    • +
  • sup3r - The Super Resolution for Renewable Resource Data software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.
    • +
  • atlite - Light-weight version of Aarhus RE Atlas for converting weather data to power systems data.
    • +
  • REHO - A decision support tool for sustainable urban energy system planning.
    • +
  • REVOL-E-TION - An energy system model toolbox designed to optimize integration of electric vehicle fleets into local energy systems such as mini- and microgrids, company sites, apartment blocks or single homes and estimate the resulting technoeconomic potentials in terms of costs and revenues within the energy.
    • +
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Consumption

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Buildings and Heating

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    +
  • scout - A tool for estimating the future energy use, carbon emissions, and capital and operating cost impacts of energy efficiency and demand flexibility technologies in the U.S. residential and commercial building sectors.
    • +
  • BOPTEST - The Building Optimization Testing (BOPTEST) Framework enables the assessment and benchmarking of control algorithms for building energy management.
    • +
  • BOPTEST-Gym - The OpenAI-Gym interface of the BOPTEST framework facilitates the assessment and benchmarking of RL algorithms for building energy management.
    • +
  • HPXML to Home Energy Score Translator - This translator script takes an HPXML file or directory of files as an input and generates HEScore inputs from it.
    • +
  • LoadProfileGenerator - A program for generating load curves for residential consumers. Agent-based and extremely detailed.
    • +
  • The-building-data-genome-project - A collection of non-residential buildings for performance analysis and algorithm benchmarking.
    • +
  • EnergyPlus - A whole building energy simulation program that engineers, architects, and researchers use to model both energy consumption and water usage in buildings.
    • +
  • OpenStudio - A cross-platform collection of software tools to support whole building energy modeling using EnergyPlus and advanced daylight analysis using Radiance.
    • +
  • BEMServer - An open source Python server to deploy energy management solutions for buildings.
    • +
  • SEED - Standard Energy Efficiency Data Platform™ is a web-based application that helps organizations easily manage data on the energy performance of large groups of buildings.
    • +
  • HPWHsim - An open source simulation model for Heat Pump Water Heaters (HPWH).
    • +
  • OpenStudio-ERI - Calculates an Energy Rating Index (ERI) via an OpenStudio/EnergyPlus-based workflow. Building information is provided through an HPXML file.
    • +
  • OpenStudio-HPXML - Modeling of residential buildings in EnergyPlus using OpenStudio/HPXML.
    • +
  • AixLib - A Modelica model library for building performance simulations.
    • +
  • TEASER - Tool for Energy Analysis and Simulation for Efficient Retrofit.
    • +
  • pyCity - A Python package for data handling and scenario generation of city districts and urban energy systems.
    • +
  • IDEAS - A Modelica model library for integrated building or district energy simulations.
    • +
  • tespy - Provides a powerful simulation toolkit for thermal engineering plants such as power plants, district heating systems or heat pumps.
    • +
  • RC_BuildingSimulator - A Resistance Capacitance Model for an Energetic Simulation of a Building.
    • +
  • City Energy Analyst - Helps you to analyze the effects of building retrofits, land-use planning, district heating and cooling and renewable energy on the future costs, emissions and energy consumption of neighborhoods and districts.
    • +
  • Modelica Buildings library - A free and open source library with dynamic simulation models for building energy and control systems.
    • +
  • NYCBuildingEnergyUse - Predict the emission of greenhouse gases from buildings by looking at their age, and water consumption as well as other energy consumption metrics.
    • +
  • Hotmaps - The open source mapping and planning tool for heating and cooling.
    • +
  • BuildSysPro - EDF's Modelica library for buildings, districts and energy systems modeling.
    • +
  • MPCPy - The Python-based open source platform for model predictive control in buildings.
    • +
  • obc - Performance Evaluation, Specification, Deployment and Verification of Building Control Sequences.
    • +
  • Energy Signature Analyser - A toolbox to analyze energy signatures of buildings and compare the signatures of all buildings within an entire building stock.
    • +
  • BuildingSystems - The Modelica open source BuildingSystems library is developed for dynamic simulation of the energetic behavior of single rooms, buildings and whole districts.
    • +
  • modelica-ibpsa - A Modelica library for building and district energy systems developed within IBPSA Project 1.
    • +
  • teb - A library to calculate the urban surface energy balance at neighborhood scale assuming a simplified canyon geometry.
    • +
  • tsib - A Python package that builds up on different databases and models for creating consistent demand and production time series of residential buildings.
    • +
  • DHNx - This package provides an open toolbox for district heating and cooling network optimization and simulation models.
    • +
  • The Building Data Genome 2 Data-Set - Whole building non-residential hourly energy meter data from the Great Energy Predictor III competition.
    • +
  • BESOS - A collection of modules for the simulation and optimization of buildings and urban energy systems.
    • +
  • pythermalcomfort - Package to calculate several thermal comfort indices (e.g. PMV, PPD, SET, adaptive) and convert physical variables.
    • +
  • comfort_tool - A web interface for comfort model calculations and visualizations according to ASHRAE Standard-55, EN Standard 16798 and ISO Standard 7730.
    • +
  • resstock - Helping states, municipalities, utilities, and manufacturers identify which building stock improvements save the most energy and money.
    • +
  • DSMR-reader - Used for reading the smart meter DSMR (Dutch Smart Meter Requirements) P1 port yourself at your home.
    • +
  • Multiscale Solar Water Heating - Solar water heating system modeling and simulation for individual and community scale projects.
    • +
  • eplusr - Provides a rich toolkit of using whole building energy simulation program EnergyPlus directly in R, which enables programmatic navigation, modification of EnergyPlus, conducts parametric simulations and retrieves outputs.
    • +
  • Brick - An open-source effort to standardize semantic descriptions of the physical, logical and virtual assets in buildings and the relationships between them.
    • +
  • BETTER - Building Efficiency Targeting Tool for Energy Retrofits.
    • +
  • NILM - Non-Intrusive Load Monitoring is the process of estimating the energy consumed by individual appliances given just a whole-house power meter reading.
    • +
  • volkszaehler.org - A free smart meter implementation with focus on data privacy.
    • +
  • ModBus Measurement Daemon - A daemon for collecting measurement data from smart meters and grid inverters over modbus.
    • +
  • HiSim - Simulation and analysis of household scenarios using modern components as alternative to fossil fuel based ones.
    • +
  • hplib - Database with efficiency parameters from public Heatpump Keymark datasets as well as parameter-sets and functions in order to simulate heat pumps.
    • +
  • Thermofeel - A library to calculate human thermal comfort indexes.
    • +
  • CBE Clima Tool - A web-based application built to support the need of architects and engineers interested in climate-adapted design.
    • +
  • Kiva - Used to calculate heat loss and gain on a timestep basis from building foundations.
    • +
  • Macquette - A whole house energy assessment tool, which models a building to produce a report to help householders under how their home performs now in terms of energy use and how it might be improved.
    • +
  • Urban Multi-scale Environmental Predictor - A climate service tool, designed for researchers and service providers presented as a plugin for QGIS.
    • +
  • eensight - This Python package implements the measurement and verification (M&V) methodology that has been developed by the H2020 project SENSEI - Smart Energy Services to Improve the Energy Efficiency of the European Building Stock.
    • +
  • PointER - A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics.
    • +
  • predyce - Is the natural evolution of the conventional Energy Performance Certification into real time optimization of building performance and comfort, by capturing the building's dynamic behaviour, and at the same time providing transparent feedback, through an intuitive interface.
    • +
  • EUReCA - Provides an efficient and reliable Urban Building Energy Modeling platform, entirely developed in Python, aiming at simulating and predicting cities and urban areas energy consumption.
    • +
  • ebcpy - Provides generic functions and classes commonly used for the analysis and optimization of energy systems, buildings and indoor climate.
    • +
  • nmecr - Builds upon the energy efficiency community's past efforts to model complex and nuanced building energy use profiles.
    • +
  • SAM - An open-source software designed to help engineers create analytical models of energy-efficient buildings.
    • +
  • Meirim - A open-source smart city application that facilitates transparency in urban planning.
    • +
  • Better Thermostat - This custom component for Home Assistant will add crucial features to your climate-controlling Thermostatic Radiator Valves to save you the work of creating automations to make it smart.
    • +
  • A Carbon Tool - An open source web application that enables users to estimate embodied carbon equivalent content from Building Information Models.
    • +
  • CarboLifeCalc - An embodied carbon calculator for the built environment.
    • +
  • HeatpumpMonitor.org - An open source initiative to share and compare heat pump performance data.
    • +
  • Sinergym - Gym environment for building simulation and control using reinforcement learning.
    • +
  • pyBuildingEnergy - Provides an assessment of building performance through a methodology that must take into account various aspects such as the thermal characteristics of the building, the use of renewable energy sources, building automation and control systems.
    • +
  • Dynamic ISO Building Simulator - A simulation program for calculating the space heating, occupancy hot water, cooling and electricity demand of German non-residential buildings.
    • +
  • Home Energy Assessment Database - Documentation, conversions, and data supporting Climate Cooperative's home energy audit and calculation tools.
    • +
  • GridWorks SpaceHeat SCADA - Intended for running a heat pump thermal storage space heating system in a house, and doing this transactively.
    • +
  • ComStock - National scale modeling of the U.S. commercial building stock supported by U.S. DOE, LADWP, and others and maintained by NREL.
    • +
  • HeatPro - Finds its roots in the need of using representative heating load curve for the simulation and optimization of various District Heating Network (DHN) production plant.
    • +
  • Home Energy Analysis Tool - Provide an app that helps residential energy coaches or advocates, and some homeowners, estimate a home's heating requirements (heat load) and assess whether proposed heat pumps have sufficient heating capacity for the home.
    • +
  • Versatile Thermostat - A full featured Thermostat for Home Assistant: presets, window, motion, presence and overpowering management.
    • +
  • OCHRE - Building energy modeling (BEM) tool designed to model flexible loads in residential buildings.
    • +
  • ConStrain - A data-driven knowledge-integrated framework that automatically verifies that building system controls function as intended.
    • +
  • DistrictGenerator - An open-source, Python-based tool that provides urban planners, energy suppliers, and related professionals with essential insights into energy demands, enabling effective neighborhood energy system design and supply harmonization.
    • +
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Mobility and Transportation

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  • stplanr - A package for sustainable transport planning with R.
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  • CO2MPAS-TA - CO2MPAS is backward-looking longitudinal-dynamics CO2 and fuel-consumption simulator for light-duty vehicles.
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  • GEARSHIFT - Generate WLTC gear-shifts based on vehicle characteristics.
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  • ocpp - Python implementation of the Open Charge Point Protocol.
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  • MaaS Global - Mobility as a Service API - data model, tests and validation.
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  • CoopCycle - A self-hosted platform to order meals in your neighborhood and get them delivered by bike couriers.
    • +
  • EVNotify - Allows you to monitor your electric vehicle and lets you notify when the specified preset state of charge has been achieved.
    • +
  • icare - An open source carpooling platform used as a basis for our commercial product Company Carpool.
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  • Carpoolear - The first Argentine Facebook app that allows the users of this social network to share car trips with other users.
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  • UTD19 - Largest multi-city traffic dataset publicly available.
    • +
  • OpenEVSE - Firmware for OpenEVSE: Open Source Hardware J1772 Electric Vehicle Supply Equipment.
    • +
  • OpenEVSE WiFi gateway - Uses an ESP8266 (ESP-12) which communicates with the OpenEVSE controller via serial utilizing the existing RAPI API serial interface.
    • +
  • DeepMove - Predicting Human Mobility with Attentional Recurrent Networks.
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  • mobility-data-specification - A data standard to enable communication between mobility companies and local governments.
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  • OpenConcept - A toolkit for conceptual MDAO of aircraft with unconventional propulsion architectures.
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  • Open Charge Map - The global public registry of electric vehicle charging locations.
    • +
  • EVCC - An extensible EV Charge Controller with PV integration implemented in Go.
    • +
  • SteVe - Provides basic functions for the administration of charge points, user data and RFID cards for user authentication and was tested successfully in operation.
    • +
  • RISE-V2G - The only fully-featured reference implementation of the Vehicle-2-Grid communication interface ISO 15118.
    • +
  • simobility - A human-friendly Python framework that helps scientists and engineers to prototype and compare fleet optimization algorithms (autonomous and human-driven vehicles).
    • +
  • MobiVoc - An open vocabulary for future-oriented mobility solutions.
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  • Transportr - The public transport companion that respects your privacy and your freedom.
    • +
  • OneBusAway - The Open Source platform for Real Time Transit Info.
    • +
  • node-gtfs - Import GTFS transit data into SQLite and query routes, stops, times, fares and more.
    • +
  • Public Transport Enabler - Unleash public transport data in your Java project.
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  • osm2gtfs - Turn OpenStreetMap data and schedule information into GTFS.
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  • Quetzal - A modeling library designed for transport planning and traffic forecasts.
    • +
  • quetzal_germany - A four step transport model for Germany using the quetzal transport modeling suite.
    • +
  • OpenMobility - Driving the Evolution and Broad Adoption of Open Source Mobility Modeling and Simulation Technologies.
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  • NoiseModelling - A free and open source model to compute noise maps.
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  • NoiseCapture - Android App dedicated to the measurement of environmental noise.
    • +
  • CyclOSM - A CartoCSS map style designed with cycling in mind.
    • +
  • Gym Electric Motor - An OpenAI Gym Environment for Electric Motors.
    • +
  • BEAM - The Framework for Modeling Behavior, Energy, Autonomy, and Mobility in Transportation Systems.
    • +
  • WoBike - Public transport and multimodal routing apps could benefit from showing nearby bikes from bikesharing services. So here's a list showing the APIs of a few of these platforms.
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  • multicycles - Aggregates on one map, more than 100 share vehicles like bikes, scooters, mopeds and cars.
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  • pybikes - Provides a set of tools to scrape bike sharing data from different websites and APIs, thus providing a coherent and generalized set of classes and methods to access this sort of information.
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  • ChargyDesktopApp - Chargy is a transparency software for secure and transparent e-mobility charging processes, as defined by the German "Eichrecht".
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  • WWCP_OCPP - Connectivity between the World Wide Charging Protocol (WWCP) and the Open Charge Point Protocol (OCPP v1.6/v2.0).
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  • WWCP_Core - The World Wide Charging Protocol Suite is a collection of protocols in order to connect market actors in the field of e-mobility solutions via scalable and secure Internet protocols.
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  • openv2g - The objective to start this project is primarily to support the ISO and IEC standardization process to specify the so called "Vehicle 2 Grid Communication Interface" (V2G CI) which became the ISO IEC 15118 specification by now.
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  • RAMP-mobility - A novel application of the RAMP main engine for generating bottom-up stochastic electric vehicles load profiles.
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  • PCT - The goal is to increase the accessibility and reproducibility of the data produced by the Propensity to Cycle Tool (PCT).
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  • goat - A tool capable of modeling walking and cycling accessibility.
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  • gtfs-router - An R package for routing with GTFS (General Transit Feed Specification) data.
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  • CityFlow - A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario.
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  • Complete_Street_Rule - An ArcGIS CityEngine scenario oriented design tool intended to enable users to quickly create procedural generated multimodal streets.
    • +
  • tesla_powerwall - Python Tesla Powerwall API for consuming a local endpoint.
    • +
  • Vehicle Energy Dataset - A large-scale dataset for vehicle energy consumption research.
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  • gbfs - Documentation for the General Bikeshare Feed Specification, a standardized data feed for shared mobility system availability.
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  • Bike Index - Bike registration that works: online, powerful, free.
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  • go-ocpp - v1.5 and v1.6 Open Charge Point Protocol implementation in Golang.
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  • EVMap - Android app to access the goingelectric.de electric vehicle charging station directory.
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  • emobility-smart-charging - Smart charging algorithms with REST API for electric vehicle fleets.
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  • open-ev-data - Open Dataset of Electric Vehicle specs.
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  • BikeshareClient - Dotnet library for integrating with GBFS bikeshare systems.
    • +
  • Growing Urban Bicycle Networks - Source code for the paper Growing Urban Bicycle Networks, exploring algorithmically the limitations of urban bicycle network growth.
    • +
  • A/B Street - A traffic simulation game exploring how small changes to roads affect cyclists, transit users, pedestrians, and drivers.
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  • enviroCar - An Android App for collecting car sensor data for the enviroCar platform.
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  • EVerest - An open source software stack for EV charging infrastructure from firmware to cloud: OCPP, ISO 15118, SunSpec, Modbus, energy management and load balancing and an entire flexible middle-ware framework based on MQTT. Part of the Linux Foundation Energy ecosystem.
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  • Streetmix - Makes it easy for people to design public spaces together.
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  • 3Dstreet - Creates 3D visualizations of your 2D Streetmix.net streets.
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  • Transitland - An open data platform that collects GTFS, GTFS Realtime, and other open data feeds from transit providers around the world.
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  • cyclestreets - The goal of cyclestreets is to provide a simple R interface to the CycleStreets routing service.
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  • Transportation Fuels Reporting System - An online application for fuel suppliers to manage their compliance obligations under the Greenhouse Gas Reduction.
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  • EV Footprint - A simulation of the true impact on climate and CO2 emissions of an electric car vs a traditional gasoline car.
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  • Mobility - An open-source solution to compute the carbon emissions due to the mobility of a local population.
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  • SpiceEV - Simulation Program for Individual Charging Events of Electric Vehicles.
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  • EV Fleet Simulator - Predict the energy usage of a fleet of electric vehicles.
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  • motulator - Simulation models for an induction motor, a synchronous reluctance motor, and a permanent-magnet synchronous motor.
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  • ALTRIOS - Fully integrated, open-source software tool to evaluate strategies for deploying advanced locomotive technologies and associated infrastructure for cost-effective decarbonization.
    • +
  • lowtrip - A web app to compute travel CO2eq for different means of transport worldwide.
    • +
  • Global Healthy and Sustainable Cities Indicators - An open-source tool for calculating spatial indicators for healthy, sustainable cities worldwide using open or custom data.
    • +
  • EV-EcoSim - A grid-aware co-simulation platform for the design and optimization of EV Charging Infrastructure.
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  • e-mission - Enables people to track their travel modes—by car, bus, bike or walking and measure their associated energy use and carbon footprint.
    • +
  • CoachCO2 - Aims to raise user awareness about their carbon footprint, notably based on their transport data.
    • +
  • Radverkehrsatlas - Provides access to bicycle infrastructure data from OpenStreetMap for administrative staff.
    • +
  • EV2Gym - A V2G Simulation Environment for large scale EV charging optimization.
    • +
  • Tools for the iTEM databases - Contains tools for two databases maintained by iTEM, the International Transport Energy Modeling consortium.
    • +
  • Cartes - A French alternative to Google Maps that aims to focus on climate-friendly transports mode.
    • +
  • accessibility - Given a pre-computed travel cost matrix and a land use dataset (containing the location of jobs, healthcare and population, for example), the package allows one to calculate accessibility levels and accessibility poverty and inequality.
    • +
  • FAF5-Analysis - Produce and interactively visualize publicly available geospatial data to support trucking fleets in navigating the transition to alternative energy carriers.
    • +
  • Geo-FTADS-Analysis - Produce and interactively visualize publicly available geospatial data to support trucking fleets in navigating the transition to alternative energy carriers.
    • +
  • OpenTripPlanner - An open source multi-modal trip planner, focusing on travel by scheduled public transportation in combination with bicycling, walking, and mobility services including bike share and ride hailing.
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Production and Industry

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    +
  • btp-ai-sustainability-bootcamp - Showcasing SAP partners how to add Intelligence and Sustainability into your industry cloud solutions on SAP Business Technology Platform.
    • +
  • AMO-Tools-Desktop - An energy efficiency calculation application for use with industrial equipment such as pumps, furnaces, fans, and motors, as well as for industrial systems such as steam.
    • +
  • Industry Energy Tool - A calculator developed by NREL for projecting energy efficiency and fuel switching scenarios for the U.S. industrial sector energy use and emissions at the Census Region and county-level.
    • +
  • CalTRACK - Methods are developed in an open and transparent stakeholder process that uses empirical testing to define replicable methods for calculating normalized metered energy consumption using either monthly or interval data from an existing conditions baseline.
    • +
  • OpenModelica - An open source Modelica-based modeling and simulation environment intended for industrial and academic usage.
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+

Computation and Communication

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    +
  • Eco-CI - Estimating the energy consumption of CI / CD pipelines on GitHub and GitLab.
    • +
  • Green Metrics Tool - An open source suite to measure, display and compare software energy and CO2 consumption for containerized software. External power meters as well as RAPL and also ML-estimation models are supported.
    • +
  • Scaphandre - An open source software agent to track energy consumption of ICT services from the servers.
    • +
  • Tracarbon - Tracarbon tracks your device's energy consumption and calculates your carbon emissions using your location.
    • +
  • H2020 CATALYST - Converting data centres in energy flexibility ecosystems.
    • +
  • Energy-Languages - The complete set of tools for energy consumption analysis of programming languages, using Computer Language Benchmark Game.
    • +
  • energyusage - A Python package that measures the environmental impact of computation.
    • +
  • CPU Energy Meter - A Linux tool that allows to monitor power consumption of Intel CPUs at fine time intervals.
    • +
  • PowerAPI - A middleware toolkit for building software-defined power meters.
    • +
  • GreenFrame - A tool to measure the carbon footprint of a user scenario on a given website application. GreenFrame is able to measure CPU, memory and network usage of Docker or Kubernetes containers. By measuring resource consumption of dockerized E2E tests, GreenFrame allows to compare the consumption of an app between its different versions.
    • +
  • patch-node - The road to global carbon-neutrality will be through programmatic compensation.
    • +
  • co2.js - A npm module for accessing the green web API, and estimating the carbon emissions from using digital services.
    • +
  • nvidia-co2 - Adds gCO2eq emissions to nvidia-smi.
    • +
  • The Low Impact Website - Reduces data transfer by up to 70% in comparison to our regular website.
    • +
  • Cloud Carbon Footprint - A tool to estimate energy use (kilowatt-hours) and carbon emissions (metric tons CO2e) from public cloud usage.
    • +
  • pyJoules - A software toolkit to measure the energy footprint of a host machine along the execution of a piece of Python code.
    • +
  • Carbon free energy for Google Cloud regions - Contains sustainability characteristics of Google Cloud regions in a machine readable format.
    • +
  • FEEP - Improve the energy efficiency of free and open source software.
    • +
  • LEAF - Simulator for modeling energy consumption in cloud, fog, and edge computing environments.
    • +
  • ethereum-nft-activity - Estimate the total emissions for popular CryptoArt platforms.
    • +
  • kube-green - A k8s operator to reduce CO2 footprint of your clusters.
    • +
  • Kepler - Uses eBPF to probe energy related system stats and exports as Prometheus metrics.
    • +
  • Software Carbon Intensity Specification - A specification that describes how to calculate a carbon intensity for software applications.
    • +
  • Principles of Green Software Engineering - Are a core set of competencies needed to define, build and run sustainable software applications.
    • +
  • grid-intensity-go - A tool written in go to help you factor carbon intensity into decisions about where and when to run computing jobs.
    • +
  • Eco2AI - A Python library which accumulates statistics about power consumption and CO2 emission during running code.
    • +
  • impact - Compute your ML model's emissions with our calculator and add the results to your paper with our generated LaTeX template.
    • +
  • CodeCarbon - Track emissions from Compute and recommend ways to reduce their impact on the environment.
    • +
  • experiment-impact-tracker - Meant to be a simple drop-in method to track energy usage, carbon emissions, and compute utilization of your system.
    • +
  • Environmental Footprint Data - Aims to reference as much data as possible to help organizations to evaluate the environmental footprint of their information systems, applications and digital services.
    • +
  • Carbonalyser - Allows to visualize the electricity consumption and greenhouse gases emissions that your Internet browsing leads to.
    • +
  • carbontracker - Track and predict the energy consumption and carbon footprint of training deep learning models.
    • +
  • green-ai - The Green AI Standard aims to develop a standard and raise awareness for best environmental practices in AI research and development.
    • +
  • Carbon Aware SDK - Helps you build the carbon aware software solutions with the intelligence to use the greenest energy sources.
    • +
  • ecoCode - Reduce the environmental footprint of your programs with this cutting-edge SonarQube plugin.
    • +
  • PowerJoular - Allows monitoring power consumption of multiple platforms and processes.
    • +
  • Green Algorithms - Aims at promoting more environmentally sustainable computational science.
    • +
  • Camunda Carbon Reductor - Allows you to time shift your processes' carbon emissions when energy is clean while still fulfilling the requested service level agreement.
    • +
  • Quell - The Content Management Software that combats climate change stopping web carbon production in its tracks.
    • +
  • Ecoindex_cli - This tool provides an easy way to analyze websites with Ecoindex from your local computer using multi-threading.
    • +
  • Zeus - A Framework for Deep Learning Energy Measurement and Optimization.
    • +
  • perun - Calculates the energy consumption of Python scripts by sampling usage statistics from your hardware components.
    • +
  • Web Sustainability Guidelines - The draft specification for our Web Sustainability Guidelines and any supplementary documentation, including our introduction to Web Sustainability.
    • +
  • GEOPM - Serves as a framework for investigating energy and power optimizations geared towards heterogeneous high performance computing platforms.
    • +
  • EcoSonar - Enabling development teams to consider the environmental impact of digital technology during development and to promote knowledge of best eco-design and accessibility practices.
    • +
  • Carbon Tools - A set of CO2 footprint tools to measure the impact of the code we ship.
    • +
  • CNCF Green Reviews WG - Project Repository for the WG Green Reviews which is part of the CNCF TAG Environmental Sustainability.
    • +
  • HBERT - A open source Revit plug in that measure the embodied carbons of materials applied within a Revit model.
    • +
  • CloudScanner - Get Boavizta impact data for your AWS cloud account usage.
    • +
  • Impact Framework - Model, Measure, simulate and monitor the environmental impacts of software.
    • +
  • Sustainability Scanner - An open source tool that helps you create a more sustainable infrastructure on AWS by evaluating it against a set of sustainability best practices and generating a report with a sustainability score and suggested improvements to apply to your template.
    • +
  • EcoLogits - Tracks the energy consumption and environmental impacts of using generative AI models through APIs.
    • +
  • E-footprint model - A toolkit for exhaustively modeling the environmental impact of digital services.
    • +
  • carbonifer - Transforms your cloud infrastructure into a carbon-aware cloud by estimating the footprint of Terraform files and real-time measurement of the current carbon emissions of your infrastructure deployed in the cloud.
    • +
  • WordPress Sustainability Handbook - We seek to embed sustainable practices into WordPress to ensure the longevity of the project.
    • +
  • impactco2 - Comparing the CO₂e consumption of various equivalent products.
    • +
  • Power Monitor - An application that measures and reports the power consumption of a Mac, giving you real-time insight into your device's energy consumption and carbon footprint based on your location.
    • +
  • Energy Efficiency in Programming Languages - Provides a benchmarking tool to measure energy efficiency across various programming languages using Docker.
    • +
  • ALUMET - A modular tool that measures energy consumption and performance metrics of CPU and GPU.
    • +
+

Agriculture and Nutrition

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    +
  • Farmbot - Humanity's open-source CNC farming machine.
    • +
  • OBIA4RTM - Aims for plant parameter retrieval - relevant in smart farming applications - by using radiative transfer models and object-based image analysis that directly addresses actual user needs and policy demands in a highly efficient, flexible and scalable way.
    • +
  • DSSAT Cropping System Model - Has been used for many applications ranging from on-farm and precision management to regional assessments of the impact of climate variability and climate change.
    • +
  • MAgPIE - Model of Agricultural Production and its Impact on the Environment.
    • +
  • OBI - The Open-Bodem-Index-Calculator is a tool that evaluates the soil of agricultural fields based on four main criteria: chemical, physical, biological and management.
    • +
  • Cycles - A daily time-step agroecosystem model that simulates the biophysical processes and management practices occurring within cropping systems and other land uses.
    • +
  • ApsimX - An agricultural modeling framework used extensively worldwide.
    • +
  • Resilience Atlas - An interactive analytical tool for building understanding of the extent and severity of some of the key stressors and shocks that are affecting rural livelihoods, production systems, and ecosystems in the Sahel, Horn of Africa and South and Southeast Asia.
    • +
  • monica - A dynamic, process-based simulation model which describes transport and bio-chemical turn-over of carbon, nitrogen and water in agro-ecosystems.
    • +
  • OpenFarm - A free and open database and web application for farming and gardening knowledge.
    • +
  • Agridat - An extensive collection of datasets from agricultural experiments.
    • +
  • Karrot - Web application for organization of foodsaving groups worldwide.
    • +
  • openfoodnetwork - An online marketplace for local food. It enables a network of independent online food stores that connects farmers and food hubs with individuals and local businesses.
    • +
  • powerplant - Provides intelligent planting suggestions which maximize positive crop interaction for the mutual benefit of all your crops.
    • +
  • Trefle - A botanical JSON REST API for plants species, allowing you to search and query over all the registered species, and build the next gardening apps and farming robots.
    • +
  • HappyPlants - Creating your own plant database in a visual way.
    • +
  • dormancyR - Provide alternatives to common chill models used in horticulture to compute chill metrics in deciduous fruit tree orchards as well as functions to handle weather data.
    • +
  • seedr - An R package that provides functions to fit hydro and thermal time germination models.
    • +
  • plantFEM - A plant simulator based on Finite Element Method, which targets crops in fields. This software provides multi-physical simulations of agriculture for canopies, plants, and organs for farmers, breeders, and agronomists.
    • +
  • openfoodfacts-server - Open Food Facts is a collaborative, free and open database of food products from around the world.
    • +
  • ramlegacy - A compilation of stock assessment results for commercially exploited marine populations from around the world.
    • +
  • Growstuff - Open source and open data platform that can predict when your plantings will be ready to harvest.
    • +
  • FarmOS - Open source web-based application for farm management, planning, and record keeping.
    • +
  • Is-Vegan - Helps you to find out which food ingredients are vegan / non-vegan.
    • +
  • AgroMo - An Integrated Assessment and Modelling software that integrates a crop, biogeochemical and a agro-economical model.
    • +
  • CropHarvest - Collects data from a variety of agricultural land use datasets and remote sensing products.
    • +
  • BreizhCrops - A Satellite Time Series Dataset for Crop Type Identification.
    • +
  • Veganify - A multilingual progressive web app, which scans the EAN or UPC codes of over 3.000.000 food- or non-food-products and simply tells you if it they're vegan or not.
    • +
  • Sentinels for Common Agriculture Policy - Aims at providing to the European and national stakeholders validated algorithms, products, workflows and best practices for agriculture monitoring relevant for the management.
    • +
  • EuroCrops - A dataset collection combining all publicly available self-declared crop reporting datasets from countries of the European Union.
    • +
  • FarmVibes.AI - Can develop rich geospatial insights for agriculture and sustainability.
    • +
  • agrolib - A set of libraries for agrometeorological modeling and climate analysis.
    • +
  • FIELDimageR - A Tool to Analyze Images From Agricultural Field Trials and Lab in R.
    • +
  • AquaCrop - A free open-source tool for assessing climate and water impacts on agriculture.
    • +
  • PCSE - A framework developed for implementing crop simulation models developed in Wageningen.
    • +
  • climatrends - Provides methods to compute precipitation and temperature indices for climate models in ecology.
    • +
  • Agroecology-Map - A technological platform that allows the mapping and exchange of Agroecological and Agroforestry experiences.
    • +
  • osiris - A R package to process climate impacts on agricultural yields for the Global Change Analysis Model.
    • +
  • PoPS - This is the R package for simulating spread of pests and pathogens.
    • +
  • DiCRA - Provides open access to key geospatial datasets pertinent to climate resilient agriculture.
    • +
  • OpenOlitor - Administration platform for Community Supported Agriculture and for direct selling farming projects based on a subscription model.
    • +
  • Grimgrains - A Plant-based cooking website.
    • +
  • extractOz - Extract the Global Runoff Data Centre agroecological zone, major soil order and weather data from your GPS sampling points.
    • +
  • PyAEZ - A python package consisted of many algorithms related to Agro-ecological zoning (AEZ) framework.
    • +
  • CRITERIA3D - Includes a numerical solution for three-dimensional water and heat flow in the soil, coupled surface and subsurface flow, meteorological data interpolation, radiation budget, crop development and crop water uptake.
    • +
  • CRITERIA1D - Agro-hydrological model that simulates one-dimensional water flow in variable saturation soils, crop development, root water extraction and irrigation water needs.
    • +
  • S4A - A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning.
    • +
  • LiteFarm - A platform joining farmers and scientists together for participatory assessment of social, environmental and economic outputs of farming systems.
    • +
  • biotrade - Analyses international trade of bio-based products with a focus on the agriculture and forestry sectors.
    • +
  • PyETo - A Python library for calculating reference crop evapotranspiration, sometimes referred to as potential evapotranspiration.
    • +
  • DynACof - Process-based model computes plot-scale Net Primary Productivity, carbon allocation, growth, yield, energy, and water balance of coffee plantations according to management.
    • +
  • WorldCereal - An efficient, agile and robust EO based system for timely global crop monitoring at field scale.
    • +
  • AgML - Provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
    • +
  • Broccoli - A free eco-friendly recipe app for building your recipe collection, distraction free cooking and seasonal ingredients.
    • +
  • epicrop - Simulation modelling of crop diseases using a Susceptible-Exposed-Infectious-Removed (SEIR) model in R.
    • +
  • rnassqs - Allows users to access the USDA's National Agricultural Statistics Service Quick Stats data through their API.
    • +
  • foodsharing - A platform and initiative against food waste.
    • +
  • Cover Crop Species Selector - Used to help farmers select a cover crop that fits their goals and constraints.
    • +
  • AgIsoStack-plus-plus - ISO-11783 stack for smart agricultural machinery communication.
    • +
  • Teikei - A web application and API that maps out community-supported agriculture in Germany, Switzerland, and Austria, based on crowdsourced data.
    • +
  • PyCrop2ML - A free, open-source library for defining and sharing CropML models used to represent the various biological processes involved in crop models.
    • +
  • Cropbox.jl - Provides a domain specific language for developing crop models.
    • +
  • MAIZSIM - A mechanistic model of maize growth, development and yield.
    • +
  • Carob - Create reproducible workflows that reshape primary agricultural research data from experiments and surveys into a standard format, and to aggregate individual data sets into larger collections that can be used in further research.
    • +
  • ALLFED Integrated Food System Model - An integrated food supply model for resilient foods in nuclear winter.
    • +
  • fiboa - The Field Boundaries for Agriculture project is focused on making field boundary data openly available in a unified format on a global scale.
    • +
  • AgML - Aspires to identify key research gaps and opportunities at the intersection of agricultural modelling and machine learning research and support enhanced collaboration and engagement between experts in these disciplines.
    • +
  • Petari - An initiative by an organization aiming to redistribute excess food from various events to those in need.
    • +
  • Food Expiration Dates - This simple app helps you avoid forgetting to consume foods that are about to expire.
    • +
  • The Farmer Journal - A farm management software for the hobbyist and smallholder farmer.
    • +
  • HarvestStat - Contains cleaned and harmonized subnational global crop production data from various sources, including the Famine Early Warning Systems Network (FEWS NET) of the United States Agency for International Development (USAID) and the Food and Agriculture Organization (FAO).
    • +
  • CSA Admin - Offers you a wide range of features specifically developed for the needs of Community Supported Agriculture.
    • +
  • Land Griffon - Empowers companies to measure, manage, and transform agricultural supply chain impacts using an open scientific methodology.
    • +
  • Fields of The World - A comprehensive benchmark dataset designed to enhance the development of machine learning models for instance segmentation of agricultural field boundaries.
    • +
  • EuroCropsML - A pre-processed and ready-to-use machine learning dataset for crop type classification of agricultural parcels in Europe.
    • +
  • street2sat - A framework for obtaining large data sets of geo-referenced crop type labels obtained from vehicle mounted cameras that can be extended to other applications.
    • +
+

Emissions

+

Carbon Intensity and Accounting

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    +
  • NMF.earth app - iOS & Android app to understand and reduce your carbon footprint.
    • +
  • EnergyPATHWAYS - The energyPATHWAYS Model is a professional, open source energy and carbon planning tool for use in evaluating long-term, economy-wide greenhouse gas mitigation scenarios.
    • +
  • blockchain-carbon-accounting - Code of the Carbon Accounting and Certification Working Group.
    • +
  • footprint - An R package to calculate carbon footprints from air travel based on IATA airport codes or latitude and longitude.
    • +
  • intensegRid - Provides information on national and regional carbon intensity, the amount of carbon emitted per unit of energy consumed, for the UK.
    • +
  • carbonintensity-api - Rust library to retrieve regional carbon intensity data from the UK National Grid.
    • +
  • Silicone - Automated filling of detail in reported emission scenarios.
    • +
  • OpenClimate - Independent Climate Accounting Network in support of Paris Agreement goals.
    • +
  • Scope3 - Build a framework where the media and advertising industry can collaborate on best practices for measuring emissions from the advertising value chain.
    • +
  • NEMED - A python package to retrieve and process historical emissions data of the National Electricity Market, reproduced by datasets published by the Australian Energy Market Operator.
    • +
  • Steel Emissions Reporting Guidance - RMI's steel emissions accounting and data exchange guidance.
    • +
  • carbonr - A package in R to conveniently calculate carbon-equivalent emissions.
    • +
  • Pledge4Future - Allows you to calculate your work related CO2e emissions from heating and electricity consumptions as well as business trips and commuting.
    • +
  • Travel Impact Model - Describes the modeling assumptions and input specifications behind the Travel Impact Model (TIM), a state of the art emission estimation model that Google's Travel Sustainability team has compiled from several external data source.
    • +
  • Profit and emission database - A free public database on large corporate emissions and profits.
    • +
  • CityCatalyst - Open Source carbon accounting for cities.
    • +
  • ec0lint - A static code analysis tool that provides users with hints on how to reduce the carbon footprint of their websites during the development process.
    • +
  • Kubernetes Carbon Intensity Exporter - Provides a data exporter by which Kubernetes operators can leverage the carbon intensity data from 3rd party for carbon-aware workload scheduling.
    • +
  • Electricity Maps - A real-time visualization of the CO2 emissions from electricity consumption.
    • +
  • aioelectricitymaps - Asynchronous Python client for Electricity Maps.
    • +
  • Open Grid Emissions Initiative - Seeks to fill a critical need for high-quality, publicly-accessible, hourly grid emissions data that can be used for GHG accounting, policymaking, academic research, and energy attribute certificate markets.
    • +
  • gridemissions - Displays the hourly carbon footprint of the US electricity system.
    • +
  • pyELQ - Detect, localize, and quantify methane emissions from concentration and wind measurements.
    • +
  • Technology-Carbon-Standard - Aims to provide a unified standard for understanding, quantifying, and reducing carbon emissions from an organisation's technology landscape.
    • +
  • Open-IMO-CII-Calculator - A C# library for calculating the carbon intensity indicator for ships, in accordance with the International Maritime Organization's carbon regulations.
    • +
  • The NHS Emission Quantification Recipe Book - A transparent, collaborative and evidence-based approach to establishing a standardised means of calculating emissions associated with NHS Carbon Footprint and Carbon Footprint Plus.
    • +
  • Open Sustainability Index - An open, transparent, wikipedia-like sustainability database - aiming to accelerate comparable corporate sustainability data.
    • +
  • GHGPy - An open source greenhouse gas accounting tool that complies with IPCC 2006 and the GHG Protocol.
    • +
  • Nos Gestes Climat - In 10 Minutes,get an estimate of your carbon footprint.
    • +
  • Carbon Intensity API - The Carbon Intensity API uses cutting-edge machine learning and sophisticated power system modelling to forecast carbon intensity and generation mix 96+ hours ahead for every region in the UK.
    • +
+

Carbon Offsets and Trading

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    +
  • Carbon Dioxide Removal Database - Open science reports on carbon removal projects and technologies.
    • +
  • National Carbon Credit Registry - As an online database using national and international standards for quantifying and verifying greenhouse gas emissions reductions by programmes.
    • +
  • forest-risks - Statistical models of forest carbon potential and risks.
    • +
  • Guardian - Provides auditable, traceable, reproducible records that document the emission process and lifecycle of carbon credits, which reduce fraud in the ESG market.
    • +
  • NCX Harvest Deferral Methodology - Documents, Data, and Code for the NCX Methodology For Improved Forest Management Through Short-Term Harvest Deferral.
    • +
  • PRTR - A project for handling the European Pollutant Release and Transfer Register (E-PRTR) data and publishing it as a national web service.
    • +
  • ETS-Watch - Provides a Python client for retrieving the latest data on the EU Emissions Trading System market and its participants.
    • +
  • World Carbon Pricing Database - This dataset contains information on carbon pricing mechanisms (carbon taxes or cap-and-trade) introduced around the world since 1990.
    • +
  • elmada - Dynamic electricity carbon emission factors and prices for Europe.
    • +
  • eu_cbm_hat - Enables the assessment of forest CO2 emissions and removals under scenarios of forest management, natural disturbances and forest-related land use changes.
    • +
+

Carbon Capture

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    +
  • ClimateMARGO.jl - A Julia implementation of MARGO, an idealized framework for optimization of climate change control strategies.
    • +
  • OceanBioME.jl - A tool to study the effectiveness and impacts of ocean carbon dioxide removal strategies.
    • +
  • CDRMEx - Carbon Dioxide Removal Modeling Experiments.
    • +
  • OpenIAM - An open source integrated assessment model developed by National Risk Assessment Partnership Phase II to facilitate risk assessment, management and containment assurance for geologic carbon sequestration projects.
    • +
  • FOQUS - Framework for Optimization and Quantification of Uncertainty and Surrogates.
    • +
  • GEOS - A simulation framework for modeling coupled flow, transport, and geomechanics in the subsurface.
    • +
  • ThermoPack - A thermodynamic model library for fluid properties and pressure-volume-temperature property calculations.
    • +
+

Emission Observation and Modeling

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    +
  • Carbon Mapper - Accelerate local climate action globally by locating, quantifying and tracking methane leaks and CO2 point-sources from space.
    • +
  • Asset-level Transition Risk in the Global Coal, Oil, and Gas Supply Chains - The global fossil fuel supply chain, mapped at the asset-level.
    • +
  • ghg emissions indicator - R scripts for a greenhouse gases emissions indicator published on Environmental Reporting British Columbia.
    • +
  • DuMux - Based on the DUNE framework and aims to provide a multitude of numerical models as well as flexible discretization methods for complex non-linear phenomena, such as CO2 sequestration, soil remediation, drug delivery in cancer therapy and more.
    • +
  • oco2peak - The goal of our project is to localize CO2 emissions on Earth based on the carbon concentration data measured by the OCO-2 Satellite from NASA.
    • +
  • CO2 Emission Datasets - The CHE Data Portal provides an interface to the distributed data used and made available through the project, either as input data sets or as resulting data sets.
    • +
  • Global Carbon Budget - An annual living data publication of carbon cycle sources and sinks, generated from multiple data sources and by multiple organisations and research groups.
    • +
  • emissions-api - A solution that provides simple access to emissions data of climate-relevant gases.
    • +
  • eixport - An R package that provides functions to read emissions from VEIN and from other models in different formats and export the emissions into the appropriate format suitable to other models.
    • +
  • EmissV - This package provides some methods to create emissions (with a focus on vehicular emissions) for use in numeric air quality models such as WRF-Chem.
    • +
  • vein - An R package to estimate Vehicular Emissions INventories.
    • +
  • The Community Emissions Data System - Produces consistent estimates of global air emissions species over the industrial era (1750 - present).
    • +
  • PyChEmiss - A Python script to create the wrfchemi file from local emissions needed to run WRF-Chem model.
    • +
  • co2-data - Data on CO2 and greenhouse gas emissions by Our World in Data.
    • +
  • X-STILT - An atmospheric transport model that deals with vertically integrated column CO2 and potentially other trace gases.
    • +
  • stilt - An open source Lagrangian particle dispersion model which is widely used to simulate the transport of pollution and greenhouse gases through the atmosphere.
    • +
  • OpenGHG - A cloud platform for greenhouse gas data analysis and collaboration.
    • +
  • Open Carbon Watch - We monitor greenhouse gases emission reports published by organizations, along with their legal obligations and their own commitments, and track them over time.
    • +
  • Methane-detection-from-hyperspectral-imagery - Deep Learning based Remote Sensing Methods for Methane Detection in Airborne Hyperspectral Imagery.
    • +
  • Methane Source Finder - Explore, analyze, and download methane plumes detected from airborne platforms on an interactive map alongside VISTA infrastructure, gridded methane estimates, and other additional data layers.
    • +
  • deep-smoke-machine - Deep learning models and dataset for recognizing industrial smoke emissions.
    • +
  • Ribbit Network Frog Sensor - The sensor for the world's largest crowdsourced network of open-source, low-cost, CO2 Gas Detection Sensors.
    • +
  • FIRECAM - An online app for end-users to diagnose and explore regional differences in fire emissions from five global fire emissions inventories.
    • +
  • ESTA - A command-line tool for processing raw emissions data into spatially and temporally-allocated emissions inventories, suitable for photochemicaly modeling or other analysis.
    • +
  • FlyingClimate - Model the CO2 and non-CO2 effects like nitrogen oxide emissions and contrail formation to analyse aviation's total warming footprint.
    • +
  • Industrial Smoke Plume Detection - Characterization of Industrial Smoke Plumes from Remote Sensing Data.
    • +
  • EDGAR - Emissions Database for Global Atmospheric Research.
    • +
  • OpenGHGMap - A city-level CO2 emissions inventory for Europe.
    • +
  • Integrated Carbon Observation System - Produces standardised data on greenhouse gas concentrations in the atmosphere, as well as on carbon fluxes between the atmosphere, the earth and oceans.
    • +
  • Global Database of Cement, Iron and Steel Production Assets - The Global Database of Cement, Iron and Steel Production Assets provides information on global cement production plants that are operational today.
    • +
  • The Global Registry of Fossil Fuels - Open Source database of oil, gas and coal production and reserves globally, expressed in CO2-equivalent.
    • +
  • Easy-ERA5-Trck - A super lightweight Lagrangian model for calculating millions of trajectories using ERA5 data.
    • +
  • GRACED - Near-real-time Global Gridded Daily CO2 Emissions Dataset from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day.
    • +
  • ECAM - Water and wastewater utility operators can assess their greenhouse gas emissions and energy consumption.
    • +
  • Emissions Modeling Framework - A client-server system designed to store information related to emissions modeling, with integrated quality control processes.
    • +
  • MOVES - A state-of-the-science emission modeling system that estimates emissions for mobile sources at the national, county, and project level for criteria air pollutants, greenhouse gases, and air toxics, available under EPA's Open Source Software policy.
    • +
  • EPA_ALPHA_Model - Evaluate the Greenhouse Gas emissions of Light-Duty vehicles.
    • +
  • gtfs2emis - Estimating public transport emissions from GTFS data.
    • +
  • HEMCO - Computing atmospheric emissions from different sources, regions, and species on a user-defined grid.
    • +
  • MethaneMapper - A fast and accurate deep learning based solution for methane detection from airborne hyperspectral imagery.
    • +
  • Pyra - Automated EM27/SUN Greenhouse Gas Measurement Software.
    • +
  • EMIT-Data-Resources - Built to help scientists understand how dust affects climate, the EMIT can also pinpoint emissions of the potent greenhouse gas.
    • +
  • Integrated Methane Inversion - Contains the source code for setting up and running the Integrated Methane Inversion with GEOS-Chem.
    • +
  • eCalc - A software tool for calculation of energy demand and greenhouse gas emissions from oil and gas production and processing.
    • +
  • forest-offsets - Includes our libraries and scripts for analyzing California's compliance forest carbon offsets program.
    • +
  • Fossil Shipment Tracker - A data platform that provides decision-makers, journalists and campaigning organizations with information that helps identify fossil fuel shipments from Russia.
    • +
  • UNFCCC DI API - Data access to the total greenhouse gas emissions by country reported to the United Nations Framework Convention on Climate Change (UNFCCC).
    • +
  • STARCOP - Semantic Segmentation of Methane Plumes with Hyperspectral Machine Learning models.
    • +
  • Clean Air Markets API Portal - A suite of API's that EPA's Clean Air Markets Division provides to access the data collected to run programs designed to reduce air pollution from power plants.
    • +
  • goFlux - A user-friendly way to calculate greenhouse gas fluxes in soil yourself, regardless of user experience.
    • +
  • Klimatkollen - A web application that displays emissions within municipalities in Sweden, along with a machine learning data pipeline under development, which will find, extract, and list emissions from companies.
    • +
  • bonsai_ipcc - Enables users to calculate national greenhouse gas (GHG) inventories based on the guidelines provided by the International Panel on Climate Change.
    • +
  • OpenMethane - Method to calculate a gridded, prior emissions estimate for methane across Australia.
    • +
  • National Climate Transparency Tool - Your gateway to ensure robust Measurement, Reporting and Verification (MRV) toward the Enhanced Transparency Framework (ETF) and to accelerate implementation of the Nationally Determined Contribution (NDC).
    • +
  • FLINT - A modular system to estimate greenhouse gas emissions and removals by the land sector.
    • +
  • CARDAMOM - A Bayesian inference approach for using terrestrial ecosystem observations to optimize terrestrial carbon cycle model states and processes parameters.
    • +
  • BlueCarbon - Facilitate the estimation of organic carbon stocks and fluxes from soil/sediment cores from blue carbon ecosystems.
    • +
  • ameriflux - A network of PI-managed sites measuring ecosystem CO2, water, and energy fluxes in North, Central and South America.
    • +
  • Kausal Paths - A tool for predicting the future emissions of cities based on historical emission data and various climate actions.
    • +
  • GEPA - This repository contains the code for the EPA U.S. gridded methane greenhouse gas inventory.
    • +
  • reemission - A Python library and a command line interface tool for estimating CO2, CH4 and N2O emissions from water reservoirs.
    • +
  • Private Jet Flights - Track and quantify emissions of private jets.
    • +
  • Emiproc - A Python package for generating emission input files from diverse inventories and grids, adaptable to various atmospheric transport models, including COSMO-ART and ICON-ART.
    • +
+

Industrial Ecology

+

Life Cycle Assessment

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    +
  • bonsai - The aim of BONSAI is to make reliable, unbiased sustainability information on products (product footprints) readily and freely available whenever and wherever it is needed to support product comparisons and decisions.
    • +
  • brightway2 - An open source framework for life cycle assessment.
    • +
  • Brightway2 data management - This package provides tools for the management of inventory databases and impact assessment methods.
    • +
  • Activity Browser - An open source and free software for Life Cycle Assessment extending the brightway2 framework.
    • +
  • EOS-AYCE - Eaternity's software platform serving as an open-source environmental operating system (EOS) for all you can eat (AYCE) for climate.
    • +
  • carculator - Prospective environmental and economic life cycle assessment of vehicles made blazing fast.
    • +
  • Electricity Life Cycle Inventory - A Python package that uses standardized facility release and generation data to create regionalized life cycle inventory (LCI) models for the generation, mix of generation, mix of consumption, and distribution of electricity to end users for the US, with embedded system processes of upstream fuel production and infrastructure.
    • +
  • uslci-content - Supplementary content for the U.S. Life Cycle Inventory Database.
    • +
  • OpenLCA - An open source and free software for Sustainability and Life Cycle Assessment.
    • +
  • openlca-python-tutorial - Explains the usage of the openLCA API from Python.
    • +
  • Global LCA Data Access Network - Gathers life cycle dataset providers and other stakeholders who share the goal of improving sustainability-related decisions through enhanced, interoperable and global access to LCA datasets.
    • +
  • BioSTEAM_LCA - An agile life cycle assessment platform that enables a fast and flexible evaluation of the life cycle environmental impacts of biorefineries under uncertainty.
    • +
  • Federal LCA Commons - A central point of access to a collection of data repositories for use in Life Cycle Assessment.
    • +
  • Trase - Brings unprecedented transparency to commodity supply chains revealing new pathways towards achieving a deforestation-free economy.
    • +
  • QSDsan - A package for the quantitative sustainable design of sanitation and resource recovery systems.
    • +
  • premise - Coupling Integrated Assessment Models output with Life Cycle Assessment.
    • +
  • fedelemflowlist - A Python package that generates and provides a standardized elementary flow list for use in life cycle assessment (LCA) data as well as mappings to convert data from other sources.
    • +
  • flowsa - Library that attributes resource use, waste, emissions, and loss to economic sectors.
    • +
  • LCIA formatter - A Python tool for standardizing the format and flows of life cycle impact assessment data.
    • +
  • LCAx - The goal for LCAx is to make an open, machine and human-readable data format for exchanging LCA results, EPD's and assemblies.
    • +
  • ecobalyse - Ecobalyse makes it possible to understand and calculate the ecological impacts of the products distributed in France.
    • +
  • lca_algebraic - This library is a small layer above brightway2, designed for the definition of parametric inventories with fast computation of LCA impacts, suitable for monte-carlo analyis.
    • +
  • UN/CEFACT CRM - Critical raw materials traceability & transparency project.
    • +
  • Doughnut Biotool - A tool to calculate a building project's impacts on biodiversity over the entire life cycle.
    • +
  • saleos - Sustainability Analytics for Low Earth Orbit Satellites.
    • +
  • timex_lca - A python package for time-explicit Life Cycle Assessment that helps you assess the environmental impacts of products and processes over time.
    • +
  • pulpo - A python-based optimization tool for life cycle assessment (LCA) that aims to import, optimize, solve, retrieve, save, and summarize LCA results.
    • +
  • pathways - A Python package that characterizes the environmental impacts of products, sectors or transition scenarios over time using Life Cycle Assessment.
    • +
  • LCA as Code - A domain-specific language for life-cycle analysis experts.
    • +
  • ecoinvent_interface - This is an unofficial and unsupported Python library to get ecoinvent data, a life cycle inventory database for fuels, energy, chemicals and plastics, batteries, building and construction, waste, agriculture, forestry and wood, pulp and paper, metals, and transport.
    • +
  • LCIA - Contains important files from the ecoinvent Life Cycle Impact Assessment method implementation procedure for ecoinvent.
    • +
+

Input Output Model

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    +
  • MARIO - Perform impact analysis using regional input-output tables and models.
    • +
  • iotables - Reproducible input-output economic analysis, economic and environmental impact assessment with empirical data.
    • +
  • pymrio - Multi-Regional Input-Output Analysis in Python.
    • +
  • useeior - Estimating potential environmental impacts of goods and services in the US economy.
    • +
  • exiobase - A global and detailed Multi-Regional Environmentally Extended Supply-Use Table (MR-SUT) and Input-Output Table (MR-IOT).
    • +
  • pyLCAIO - An object class to hybridize lifecycle assessment and environmentally extended input-output (EEIO) databases.
    • +
  • RaMa-Scene - RaMa-Scene a web-platform to analyse Environmentally Extended Input-Output data and generate scenarios.
    • +
  • pycirk - Model Circular Economy policy and technological interventions in Environmentally Extended Input-Output Analysis.
    • +
  • FABIO - Forestry and Agriculture Biomass Input-Output Tables.
    • +
+

Circular Economy and Waste

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    +
  • ONEARMY - A series of tools for the Precious Plastic community to collaborate around the world and tackle plastic waste.
    • +
  • Trash-ICRA19 - A Bounding Box Labeled Dataset of Underwater Trash.
    • +
  • TACO - Trash Annotations in Context Dataset Toolkit.
    • +
  • Deep Waste - AI powered recycling app that classifies waste and promotes eco-friendly disposal with rewards.
    • +
  • circularity.ID Open Data Standard - The standard represents the results and findings of an extensive six-year research into the needs of stakeholders in the fashion industry to achieve a circular economy.
    • +
  • RecycleNet - Effective trash classification model using only a small number of annotated images.
    • +
  • trashnet - Dataset of images of trash. Torch-based CNN for garbage image classification.
    • +
  • OpenLitterMap - An open, interactive and accessible database of the world's litter and plastic pollution.
    • +
  • Recyclebot - An open source waste plastic extruder that creates 3D printer filament from waste plastic and natural polymers.
    • +
  • Global Plastic Navigator - Visualises the most recent and high-resolution data of current scientific publications on marine plastic pollution.
    • +
  • marine_debris_ML - Marine debris detection with commercial satellite imagery and deep learning.
    • +
  • ADVECT - A whole-ocean marine debris transport model which is built to handle millions of particles and terabytes of data.
    • +
  • Surfrider Plastic Origins - A citizen science project that uses AI to map plastic pollution in European rivers and share its data publicly.
    • +
  • MARIDA - A marine debris-oriented dataset on Sentinel-2 satellite images.
    • +
  • Detect waste - Detecting plastic waste in the environment to combat environmental pollution and promote circular economy.
    • +
  • Santiago.jl - A Julia package to generate appropriate sanitation system options.
    • +
  • SwolfPy - A free, multi-platform, Python-based, open-source, SWM LCA optimization framework with built-in parametric and Monte Carlo sensitivity and uncertainty analysis capabilities.
    • +
  • TrashMob - A website dedicated to organizing groups of people to clean up the world we live in. Users create cleanup events, publicize them, and recruit people to join up, as well as ask for assistance from communities and partners.
    • +
  • Haztrak - A web application that aims to illustrate how hazardous waste management software can interface with EPA's e-Manifest systems to track hazardous waste electronically from cradle-to-grave.
    • +
  • e-Manifest - The purpose of e-Manifest is to establish a national information technology system that will enable the Agency and the hazardous waste program's industry and state stakeholders to transition the manifest system from one that is paper-intensive and burdensome to a system that is much more efficient.
    • +
  • OpenSpecy - Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples.
    • +
  • waste_flow - A python package for retrieving and analyzing data concerning the waste management of European countries.
    • +
  • The Clothing Loop - An initiative that offers an easy way for people to swap clothes with others in their own neighborhood.
    • +
  • prommis - Process Optimization and Modeling for Minerals Sustainability.
    • +
  • Plastics Prototype - This tool explores different policy interventions both regionally and globally to help countries exploring ways to reduce the impact of plastic.
    • +
  • CircuLaw - A knowledge platform dedicated to enabling the transition to a circular economy by identifying opportunities in current law to support a circular future.
    • +
  • plasticparcels - A python package for simulating the transport and dispersion of plastics in the ocean.
    • +
+

Biosphere

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Avian Monitoring and Analysis

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    +
  • BirdFlowR - Predict changes in bird distributions and generate synthetic migration routes.
    • +
  • ebirdst - Access and Analyze eBird Status and Trends Data.
    • +
  • eBird API - eBird API provides a set of wrapper functions for accessing the end-points in the eBird API 2.0.
    • +
  • GeoPressureR - R package which help researchers construct the trajectory of a bird equiped with an atmospheric pressure sensor.
    • +
  • bioRad - R package for analysis and visualisation of biological signals in weather radar data.
    • +
  • BirdNET-Analyzer - A deep learning solution for avian diversity monitoring.
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  • ABAP - Code for downloading and working with data from the African Bird Atlas Project.
    • +
  • palmerpenguins - The palmerpenguins data contains size measurements for three penguin species observed on three islands in the Palmer Archipelago, Antarctica.
    • +
  • auk - eBird Data Extraction and Processing in R.
    • +
  • motus - An R package for processing automated radio telemetry data from Motus, an international collaborative research network that uses coordinated automated radio telemetry to facilitate research and education on the ecology and conservation of migratory animals.
    • +
  • naturecounts - R package to access data from the NatureCounts platform by Bird Studies Canada.
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  • bbsBayes2 - An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data.
    • +
  • birdnet - The library is geared towards providing a robust workflow for ecological data analysis in bioacoustic projects.
    • +
  • Nighthawk - A machine learning model for acoustic monitoring of nocturnal bird migration.
    • +
+

Bioacoustics and Acoustic Data Analysis

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    +
  • vak - A neural network framework for animal acoustic communication and bioacoustics.
    • +
  • scikit-maad - Enables quantitative analysis of environmental audio, offering tools for processing, segmenting, and computing acoustic features.
    • +
  • OpenSoundscape - A free and open source Python utility library analyzing bioacoustic data.
    • +
  • wildRtrax - An R package for environmental sensor data management and analytics.
    • +
  • baRulho - An R package to quantify habitat-induced degradation of (animal) acoustic signals.
    • +
  • Project Echo - Develop an AI/ML solution to detect and classify the density of noisy animals in rainforests, providing conservationists with an efficient and non-invasive tool to monitor threatened animal populations over time.
    • +
  • whoBIRD - Identify bird sounds in real time with this Android version of BirdNET, a Bird sound recognition for more than 6,000 species worldwide.
    • +
  • warbleR - Is intended to facilitate the analysis of the structure of animal acoustic signals in R.
    • +
  • BirdSet - A benchmark dataset collection for bird sound classification.
    • +
  • birdnetR - Is geared towards providing a robust workflow for ecological data analysis in bioacoustic projects.
    • +
+

Biomass

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    +
  • Forest Carbon database - Global Forest Carbon Database.
    • +
  • fgeo.biomass - Calculate biomass with allometric equations from the allodb package and ForestGEO data.
    • +
  • BIOMASS - An R package for estimating aboveground biomass and its uncertainty in tropical forests.
    • +
  • carbon budget - This model maps gross greenhouse gas emissions from forests between 2001 and 2015, gross carbon removals by forests between 2001, and the difference between them (net flux).
    • +
  • PNVmaps - Global Maps of Potential Natural Vegetation based on Machine Learning.
    • +
  • MAAP - Discover and use biomass relevant data, integrating the data for comparison, analysis, evaluation, and generation.
    • +
  • BioPAL - The BIOMASS Product Algorithm Laboratory hosts official tools for processing and analysing ESA's BIOMASS mission data.
    • +
  • allodb - An R package for biomass estimation at extratropical forest plots.
    • +
  • CarbonViewer - R-shiny application for calculating peatland volume and carbon stock to support area planners and decision makers.
    • +
  • CBM-CFS3 - The Carbon Budget Model of the Canadian Forest Sector is an aspatial, stand- and landscape-level modelling framework used for international reporting of the forest carbon balance of Canada's managed forest.
    • +
+

Biodiversity Analysis and Metrics

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    +
  • redlistr - An R package that contains a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria.
    • +
  • biodivMapR - An R package for α- and β-diversity mapping using remotely-sensed images.
    • +
  • mobr - Tools for analyzing changes in biodiversity across scales.
    • +
  • mapme.biodiversity - Efficient analysis of spatial biodiversity datasets for global portfolios.
    • +
  • Biodiverse - A tool for the spatial analysis of diversity using indices based on taxonomic, phylogenetic, trait and matrix-based relationships, as well as related environmental and temporal variations.
    • +
  • ENMTML - An R package for an integrated construction of Ecological Niche Models.
    • +
  • diversitree - Includes a number of comparative phylogenetic methods, mostly focussing on analysing diversification and character evolution.
    • +
  • Diversity.jl - Provides functionality for measuring alpha, beta and gamma diversity of metacommunities (e.g. ecosystems) and their constituent subcommunities.
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  • entropart - An R package that provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
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  • ade4 - Analysis of Ecological Data for Exploratory and Euclidean Methods in Environmental Sciences.
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  • letsR - Allow users to build presence-absence matrices, the primary analytical tool in macroecology, from species' geographical distributions and merge them with species' traits, conservation information and spatial environmental layers.
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  • mFD - Provides a user friendly interface to compute a global assessment of functional diversity by gathering computation of alpha and beta functional indices.
    • +
  • fundiversity - Provides a lightweight package to compute common functional diversity indices.
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  • funrar - A package to compute functional rarity indices, it quantifies how species are rare both from a functional and an extent point of view.
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  • oneimpact - Provides tools for the assessment of cumulative impacts of multiple infrastructure and land use modifications in ecological studies.
    • +
  • gdm - Provides functions to fit, plot, summarize, and apply Generalized Dissimilarity Models.
    • +
  • BioMonTools - Tools for biomonitoring and bioassessment; metric calculation for benthic macroinvertebrates, fish, and periphyton.
    • +
  • fqar - Floristic Quality Assessment (FQA) is a standardized method for rating the ecological value of natural areas based on the plant species found within them.
    • +
  • Swallowtail Climate Change - Data and code for North American Swallowtail and larval host plant distributions in relation to climate change.
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Biodiversity Citizen Science

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  • Naturtag - A tool for nature photographers that adds useful metadata to describe the organisms in your photos.
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  • iNaturalist - Helps you identify the plants and animals around you.
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  • pyinaturalist - Python client for iNaturalist, a community science platform that helps people get involved in the natural world by observing and identifying the living things around them.
    • +
  • iNaturalistReactNative - Official iNaturalist client written in React Native that will eventually replace our existing iOS and Android apps.
    • +
  • GeoNature-citizen - A free and Open Source web solution for citizen science projects for biodiversity data collection.
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  • DigiVol - Harness the power of online volunteers to digitise biodiversity data that is locked up in biodiversity collections, field notebooks and survey sheets.
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  • Naturblick iOS - Identify plants and animals and learn more about nature in your city.
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  • Gottesanbeterin Gesucht - An interactive web application to track Mantis Religiosa sightings in Brandenburg.
    • +
  • Arena - A cloud-based platform that enables fast, flexible setup and data entry for various field surveys, including biophysical, socio-economic, and biodiversity inventories.
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Biodiversity Data Access and Management

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  • pygbif - Making the data of the Global Biodiversity Information Facility API accessible via Python.
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  • rgbif - Interface to the Global Biodiversity Information Facility API.
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  • taxize - Allows users to search over many taxonomic data sources for species names (scientific and common) and download up and downstream taxonomic hierarchical information.
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  • Global Biotic Interactions - Global Biotic Interactions provides access to existing species interaction datasets.
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  • rredlist - An R client for the IUCN Red List of threatened and endangered species.
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  • spocc - An R package to query and collect species occurrence data from many sources.
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  • robis - Build and maintain a global alliance that collaborates with scientific communities to facilitate free and open access to, and application of, biodiversity and biogeographic data and information on marine life.
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  • ALA4R - The Atlas of Living Australia provides tools to enable users of biodiversity information to find, access, combine and visualise data on Australian plants and animals.
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  • IUCNN - Environmental data and existing IUCN Red List assessments to predict the conservation status of "Not Evaluated" species, for any taxon or geographic region of interest.
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  • IPT - Global Biodiversity Information Facility and used to publish and share biodiversity datasets through the GBIF network.
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  • TaxonWorks - An integrated web-based workbench for taxonomists and biodiversity scientists.
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  • BioDiversityHub BC - The source of British Columbia's species inventory data.
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  • specify7 - A biological collections data management platform.
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  • gbifdb - Provide a relational database interface to a parquet based serializations of gbif's AWS snapshots of its public data.
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  • rmangal - Retrieve and explore data from the ecological interactions database MANGAL.
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  • EcoReleve - A free and open source biodiversity data entry software.
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  • galah - An R interface to biodiversity data hosted by the living atlases; a set of organisations that share a common codebase, and act as nodes of the Global Biodiversity Information Facility.
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  • rotl - An R package to interact with the Open Tree of Life data APIs.
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  • rvertnet - An R Wrapper to the VertNet API, a collaborative project that makes biodiversity data free and available on the web.
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  • BioCLIP - A foundation model for the tree of life, leveraging the unique properties of biology captured by TreeOfLife-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge.
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  • pybioclip - Python package that simplifies using the BioCLIP foundation model.
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  • OpenDataBio - A system for storing and retrieving biological data - floristics, ecology and monitoring.
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  • traits - R package for accessing species trait data from multiple databases.
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  • GFBio Data Submission and Brokerage System - Provides the central entry point for the submission services provided by GFBio e.V. for the long-term data archival and publication of biodiversity, ecology and environmental science data.
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  • Open Tree of Life - Constructs a comprehensive, dynamic and digitally-available tree of life by synthesizing published phylogenetic trees along with taxonomic data.
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  • BIOSCAN-5M - A comprehensive multi-modal dataset comprised of over 5 million specimens, 98% of which are insects.
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  • PortalData - Provides access to a long-term study of a Chihuahuan desert ecosystem.
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  • wildflow - Open marine biodiversity data platform aiming to protect oceans by accelerating research and conservation.
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Biodiversity Data Cleaning and Standardization

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  • BIRDS - This set of tools has been developed for systematizing biodiversity data review in order to evaluate whether a set of species observation are fit-for-use and help take decisions upon its use on further analysis.
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  • Darwin Core - Standard for sharing of information about biological diversity.
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  • bdc - A toolkit for standardizing, integrating, and cleaning biodiversity data.
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  • Global Names Verifier - Verifies scientific names against more than 100 biodiversity databases.
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  • python-dwca-reader - A Python package to read and parse Darwin Core Archive (DwC-A) files, as produced by the GBIF website, the IPT and many other biodiversity informatics tools.
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  • SMBD - Facilitate a community of practice for aligning marine biological data to Darwin Core for sharing to Ocean Biodiversity Information System (OBIS).
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  • CoordinateCleaner - Automated flagging of common spatial and temporal errors in biological and palaeontological collection data, for the use in conservation, ecology and palaeontology.
    • +
  • Bionomia - Parse people names from biodiversity occurrence data, apply basic regular expressions and heuristics to disambiguate them, and to make these occurrence records as entities that can be claimed by people via ORCID.
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Conservation and Restoration

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  • worldpa - R interface to the World Database on Protected Areas.
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  • pywdpa - Python interface to the World Database on Protected Areas.
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  • wdpar - R Interface to the World Database on Protected Areas.
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  • prioritizr - Uses mixed integer linear programming techniques to provide a flexible interface for building and solving conservation planning problems.
    • +
  • grainscape - Efficient Modelling of Landscape Connectivity, Habitat, and Protected Area Networks.
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  • restoptr - Aims to identify priority areas for restoration efforts using optimization algorithms.
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  • ADRIA.jl - A multi-criteria decision support platform for informing reef restoration and adaptation interventions.
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  • lterdatasampler - Provide the scientific community, policy makers, and society with the knowledge and predictive understanding necessary to conserve, protect, and manage the nation's ecosystems, their biodiversity, and the services they provide.
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  • Half Earth - Provides an interactive summary of progress toward Half-Earth goal, offered as a tool to help contextualise global datasets of biodiversity, human encroachment, and protection.
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  • track2KBA - This R package is comprised of functions that facilitate the identification of areas of importance for biodiversity, such as Key Biodiversity Areas (KBAs), based on individual tracking data.
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  • oceandatr - Aims to provide simple functions for creating data for conducting a spatial conservation prioritization for large scale areas of the ocean, specifically offshore areas.
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  • Ecoscope - An open-source analysis module for tracking, environmental and conservation data analyses.
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  • EMMA - The core repository for environmental data processing in the Ecological Monitoring and Management Application EMMA.io.
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  • spatialwarnings - An R package to compute spatial early-warning signals of ecosystem degradation.
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  • projectfirma - Tracks conservation and natural resource project performance and expenditures, and helps managers visualize results at the program level.
    • +
  • Ground for Android - An map-first data collection platform built in collaboration with environmental non-profits and international organizations with the goal of driving sustainability impact at scale.
    • +
  • geohabnet - Enable users to visualise a habitat connectivity risk index for agriculture using their own +parameter values.
    • +
  • Where to Work - A web-based application for solving complex conservation scenarios using concepts of systematic conservation planning.
    • +
  • SkyTruth 30x30 Tracker - A compelling online experience that builds momentum towards meeting global biodiversity targets by unlocking opportunities for protecting the marine environment and forging connections with the wider 30x30 community.
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Deforestation and Reforestation

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  • Plant-for-the-Planet - Allows you to plant trees with over 100 reforestation projects around the world.
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  • Tree Mapper App - Tree Mapper extends the Plant-for-the-Planet App and allows on site coordinate submission during plantation.
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  • Continuous Reforestation - A GitHub Action for planting trees within your development workflow using the Reforestation as a Service (RaaS) API developed by DigitalHumani.
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  • forestatrisk - Modelling and forecasting deforestation in the tropics.
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  • Tree Tracker - Used by people who plant trees so they don't have to manually type coordinates with pictures they took.
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  • FSDL Deforestation Detection - A deep learning approach to detecting deforestation risk, using satellite images and a deep learning model.
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  • Treetracker - Coordinates the digital capture of tree growth data in the field, allowing to establish employment for people living in extreme poverty based on tree planting.
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  • gfcanalysis - Tools for working with Global Forest Change dataset.
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  • Climate Change Informed Species Selection - A Biogeoclimatic Ecosystem Classification-based analysis framework built to anticipate the change climate implications to tree species environmental suitability at a site specific level.
    • +
  • UDef-ARP - Facilitate implementation of the Verra tool, VT0007 Unplanned Deforestation Allocation.
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  • Global Reforestation Opportunity Assessment - Quantify carbon sequestration in naturally regenerating forests around the world.
    • +
  • datazoom.amazonia - Facilitates access to official Brazilian Amazon data, including agriculture, deforestation, production.
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  • Seedlot Selection Tool - A GIS mapping program designed to help forest managers match seedlots with planting sites based on climatic information.
    • +
  • riskmapjnr - Obtain maps of the spatial risk of deforestation and forest degradation following the methodology developed in the context of the Jurisdictional and Nested REDD+ (JNR) and using only a forest cover change map as input.
    • +
  • whisp - The Forest Data Partnership promotes a Convergence of Evidence approach for Forest and Commodities Monitoring.
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+

Ecological and Environmental Modeling

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  • PEcAn - The Predictive Ecosystem Analyzer is an integrated ecological bioinformatics toolbox.
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  • spatialEco - R package for spatial analysis and modelling of ecological systems.
    • +
  • ReMobidyc - A multi-agent simulator for individual-based modeling in population dynamics and ecotoxicology.
    • +
  • portalcasting - Provides a model development, deployment, and evaluation system for forecasting how ecological systems change through time, with a focus on a widely used long-term study of mammal population and community dynamics.
    • +
  • TrenchR - Facilitates microclimate modeling to translate weather station data into the environmental conditions experienced by organisms and biophysical modeling to predict organismal body temperatures given the environmental conditions.
    • +
  • NicheMapR - Modelling the thermodynamic constraints on life.
    • +
  • D-EcoImpact - A Python based kernel to perform spatialenvironmental impact assessment.
    • +
  • EcoSISTEM.jl - A Julia package that provides functionality for simulating species undergoing dynamic biological processes such as birth, death, competition and dispersal, as well as environmental changes in climate and habitat.
    • +
  • ECOTOXr - Provides access in R to ECOTOX, a comprehensive knowledgebase providing single chemical environmental toxicity data on aquatic and terrestrial species.
    • +
  • moveHMM - R package which implements hidden Markov models and associated tools (state decoding, model selection, model checking, etc.) tailored to animal movement modelling.
    • +
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Forest Modeling and Analysis

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    +
  • ECOSTRESS - The images acquired by ECOSTRESS are the most detailed temperature images of the surface ever acquired from space and can be used to measure the temperature of an individual farmers field and plants.
    • +
  • fgeo - Analyze forest diversity and dynamics.
    • +
  • FATES - Advancing model predictions of tropical forest carbon cycle responses to a changing climate over the 21st Century.
    • +
  • spanner - Utilities to support landscape-, forest-, and tree-related data collection, manipulation, analysis, modelling, and visualization.
    • +
  • Forest Vegetation Simulator - A family of individual-tree, distance-independent, forest growth simulation models.
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  • allometric - An R package for predicting tree attributes with allometric models.
    • +
  • r3PG - An R package for forest growth simulation using the 3-PG process-based model.
    • +
  • Our Forests Tomorrow - Visualizing European forests future.
    • +
  • A high-resolution canopy height model of the Earth - The model estimates canopy top height for every Sentinel-2 image pixel and was trained using sparse GEDI LIDAR data as a reference.
    • +
  • medfate - Functions to simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation.
    • +
  • dplR - A package for R that performs tree-ring analyses such as detrending, chronology building, and crossdating.
    • +
  • LandR - These models simulate forest vegetation dynamics based on LANDIS-II, and incorporate fire and insect disturbance, as well as other important ecological processes.
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  • Rprebasso - Predict the growth and carbon balance of the forest ecosystem.
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  • dfoliatR - Provide dendrochronologists with tools for identifying and analyzing the signatures of insect defoliators preserved in tree rings.
    • +
  • fellingdater - A suite of functions designed to assist dendrochronologists in inferring estimates for felling dates, derived from dated tree-ring series.
    • +
  • ITSMe - Provide easy to use functions to quickly obtain structural metrics from individual tree point clouds and their respective quantitative structure models.
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Forest Observation and Management

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  • SEPAL - Empowering people around the world to gain a better understanding of land cover dynamics in forest management by facilitating the efficient access and use of Earth observation data.
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  • Global Forest Watch - An online, global and near real-time forest monitoring tool.
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  • gfw-mapbuilder - A library to build custom Forest Atlas web applications.
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  • OpenTreeMap - A collaborative platform for crowdsourced tree inventory, ecosystem services calculations, urban forestry analysis and community engagement.
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  • forestmangr - An R package for forest mensuration and management.
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  • Gieß den Kiez - Enable coordinated citizen participation in the irrigation of urban trees.
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  • Forest Scenario Planner - An online tool for forest management scenario planning.
    • +
  • PYFOREST - Informing Forest Conservation Regulations in Paraguay.
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  • OpenTrees.org - A data visualisation of millions of publicly maintained trees around the world.
    • +
  • Mangrove Atlas - An online platform that provides the remote sensing data and tools for monitoring mangroves.
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  • forestables - Read and uniformize forest inventories data from the FIA (USA forest inventory), FFI (France forest inventory) and IFN (Spain forest inventory).
    • +
  • rFIA - Increase the accessibility and use of the USFS Forest Inventory and Analysis Database by providing a user-friendly, open source platform to easily query and analyze.
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  • DiversiTree - Help urban foresters, planners, greeners, and ecologists in quantifying tree ecosystem diversity in cities.
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  • EU forest tree point data - A compilation of analysis-ready point data for the purpose of vegetation and Potential Natural Vegetation mapping for the EU.
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  • California Forest Observatory - Python wrappers for accessing Forest Observatory data via the Salo API.
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  • WeeForest - Aims to make the data about trees in Great Britain more accessible and understandable, whilst still maintaining good industry practices.
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  • forestdata - An R package designed to provide users with easy access to various forestry and land use datasets.
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  • Netflora - The Netflora Project involves the application of geotechnologies in forest automation and carbon stock mapping in native forest areas in Western Amazonia.
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  • FMT - An open source forest management library that makes forest planning easier.
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  • knfi - An R package was developed to efficiently manage and analyze large-scale National Forest Inventory (NFI) data in South Korea, streamlining the process of monitoring forest ecosystem changes using extensive nationwide survey data.
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  • Castor - A spatial, large-scale analytical model for simulating forest harvest and it's potential influence on other forest values (e.g., wildlife habitat).
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Forest Remote Sensing

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  • treeseg - Developed to near-automatically extract tree-level point clouds from high-density larger-area lidar point clouds acquired in forests.
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  • TreeLS - High performance R functions for forest data processing based on Terrestrial Laser Scanning (but not only) point clouds.
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  • TreeQSM - Quantitative Structure Models of Single Trees from Laser Scanner Data.
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  • lidR - An R package for airborne LiDAR data manipulation and visualization for forestry application.
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  • pyfor - Tools for analyzing aerial point clouds of forest data.
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  • DeepForest - Python Package for Tree Crown Detection in Airborne RGB imagery.
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  • NeonTreeEvaluation - Benchmark dataset for tree detection for airborne RGB, Hyperspectral and LIDAR imagery.
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  • canopyLazR - An R package that estimates leaf area density and leaf area index from airborne LiDAR point clouds.
    • +
  • forestlas - Code for generating metrics of forest vertical structure from airborne LiDAR data.
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  • DeepTreeAttention - Hyperspectral Image Classification with Attention Aided CNNs.
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  • DetecTree - A Pythonic library to classify tree/non-tree pixels from aerial imagery.
    • +
  • Sentinel-Tree-Cover - This project maps tree extent at the ten-meter scale using open source artificial intelligence and satellite imagery.
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  • rGEDI - An R Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing.
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  • detectreeRGB - Tree crown delineation from RGB imagery, coupled with methods to delineate tree crowns from LiDAR data.
    • +
  • ForesToolboxRS - Remote Sensing Tools for Forest Monitoring.
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  • Forest Structural Complexity Tool - Allowing plot scale measurements to be extracted automatically from most high-resolution forest point clouds from a variety of sensor sources.
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  • ForestTools - Detect and segment individual tree from remotely sensed data.
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  • Detectree2 - Automatic tree crown delineation based on the Detectron2 implementation of Mask R-CNN.
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  • 3D Forest - Visualization, processing and analysis of Lidar point clouds, mainly focused on forest environment.
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  • sgsR - A structurally guided sampling toolbox for LiDAR-based forest inventories.
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  • ForestGapR - An R Package for Airborne Laser Scanning-derived Tropical Forest Gaps Analysis.
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  • FORTLS - Detect and segment individual tree from remotely sensed data.
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  • 3DFin - A free software for automatic computation of tree parameters in terrestrial point clouds.
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  • lasR - Production of canopy height model, digital terrain model, tree detection and segmentation on large coverage.
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  • TreeCrownDelineation - This package performs automatic delineation of individual tree crowns in remote sensing imagery.
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  • rTwig - A method for correcting unrealistic cylinders in quantitative structure models (QSMs) due to technical limitations of current LiDAR sensor technology in forestry measurements.
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Marine Life and Fishery

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  • VIAME - Video and Image Analytics for Marine Environments.
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  • ecodata - A data package for reporting on Northeast Continental Shelf ecosystem status and trends.
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  • rfishbase - An R interface to the fishbase.org database.
    • +
  • PlanktonIndividuals.jl - This package simulates the behaviors of an ensemble of phytoplankton individuals.
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  • UVic-updates-opem - Introduces optimality-based phytoplankton and zooplankton into the UVic-ESCM (version 2.9) with variable C:N:P(:Chl) stoichiometry for phytoplankton, diazotrophs and detritus.
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  • OceanAdapt - Provide information about the impacts of changing climate and other factors on the distribution of marine life to the National Climate Assessment, fisheries communities, policymakers, and to others.
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  • icesDatras - R interface to Database of Trawl Surveys web services.
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  • icesTAF - Functions to support the International Council for the Exploration of the Sea Transparent Assessment Framework.
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  • KSO - The Koster Seafloor Observatory is an open-source, citizen science and machine learning approach to analyse subsea movies.
    • +
  • FSAdata - Contains data for use in common fisheries stock analyses. See installation instructions further below.
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  • dataaimsr - Australian Institute of Marine Science (AIMS) Data Platform API Client which provides easy access to AIMS Data Platform scientific data and information.
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  • mermaid-dash - Transform your underwater insights into data-driven actions that save coral reefs.
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  • mermaidr - An open-source data platform developed to help you collect, analyze, and share coral reef monitoring data.
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  • RSP - Refining the Shortest Paths of animals tracked with acoustic transmitters in estuarine regions.
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  • aspe - An R package to analyse and visualise river fish data in France.
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  • Cifonauta - Marine biology image database by CEBIMar/USP.
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  • CoralNet - A repository and resource for benthic image analysis.
    • +
  • Aqualink - A philanthropically funded system to help people manage their local marine ecosystems in the face of increasing Ocean temperatures.
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  • Near Real-Time Survey Progress and Temperature Maps - Create daily survey station daily temperature and anomaly plots as the ships work their way through the Bering Sea.
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  • cold pool index - Calculate the cold pool index, mean sea surface temperature, and mean bottom temperature using temperature data collected during bottom trawl surveys of the eastern Bering Sea.
    • +
  • PlanktoScope - A modular, open-source hardware and software platform that allows for high-throughput quantitative imaging of plankton samples in aquatic biology and ecology.
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  • pyafscgap - Community contributed Python-based tools for working with public bottom trawl surveys data from the NOAA Alaska Fisheries Science Center Groundfish Assessment Program.
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  • PhytoFit - Used to display satellite chlorophyll concentration, and calculate statistics and model phytoplankton blooms for regions within custom polygons.
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  • Echopype - A package built to enable interoperability and scalability in ocean sonar data processing.
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  • OSMOSE - A multispecies and individual-based model which focuses on fish species.
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  • WHOI HABhub Data Portal - Is being developed as a data access and visualization portal for the New England Harmful Algal Bloom Observing Network.
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  • nwfscSurvey - Tool to pull and process NWFSC West Coast groundfish survey data for use in PFMC groundfish stock assessments.
    • +
  • ss3sim - An R package that facilitates flexible, rapid, and reproducible fisheries stock assessment simulation testing with the widely-used Stock Synthesis statistical age-structured stock assessment framework.
    • +
  • rfisheries - Interacting with fisheries databases at openfisheries.org.
    • +
  • Fisheries Stock Assessment - Provides R functions to conduct typical introductory fisheries analyses.
    • +
  • RFishBC - Helps fisheries scientists collect measurements from calcified structures and back-calculate estimated lengths at previous ages.
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  • fishRman - Shiny R Dashboard to easily query, download, analyse and visualise Global Fishing Watch data on fishing effort.
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  • mizer - An R package to run dynamic multi-species size-spectrum models of fish communities.
    • +
  • POSEIDON - A fishery agent-based model.
    • +
  • gfwr - R package for accessing data from Global Fishing Watch APIs.
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  • r4ss - A fisheries stock assessment model written by Rick Methot.
    • +
  • OrcaHello - A real-time AI-assisted killer whale notification system.
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  • planktonr - R package that facilitates the download, analysis and visualisation of phytoplankton and zooplankton data.
    • +
  • worrms - A R client for the World Register of Marine Species.
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  • FIMS - A software system designed and architected to support next-generation fisheries stock assessment, ecosystem, and socioeconomic modeling.
    • +
  • SSMSE - R package for performing Management Strategy Evaluation using Stock Synthesis.
    • +
  • bcfishobs - Known BC Fish Observations is documented as the most current and comprehensive information source on fish presence for the province.
    • +
  • wastd - Turtle strandings in Western Australian, as reported to the Department of Biodiversity, Conservation and Attractions.
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  • shadia - An R package that was designed to run dam passage performance standard models for American shad and other herrings.
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  • CASAL2 - Models can be used for a single population with a single anthropogenic event (in a fish population model this would be a single fishery), or for multiple species and populations, areas, and/or anthropogenic or exploitation methods, and including predator-prey interactions.
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  • etn - Provides functionality to access data from the European Tracking Network (ETN) database hosted by the Flanders Marine Institute (VLIZ) as part of the Flemish contribution to LifeWatch.
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  • OpenFish - An open-source system written in GoLang for classifying marine species.
    • +
  • FishGlob_data - An integrated database of fish biodiversity sampled with scientific bottom trawl survey.
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  • Monitorfish - Improve control and monitoring of fishing vessels activities.
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  • pacea - An R package of Pacific ecosystem information to help facilitate an ecosystem approach to fisheries management.
    • +
  • DISPLACE_GUI - A Scientific Research Software for Spatial Fisheries and Natural Resource Management.
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  • WhaleMap - The goal of this software is to rapidly and effectively collect and share whale survey information within and between research, government, industry, and public sectors.
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  • rCAX - A collaborative process to efficiently share and provide access to standardized derived information, such as fish population-scale high-level indicators (HLIs) and supporting metrics.
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  • echoSMs - Making acoustic scattering models available to fisheries and plankton scientists.
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  • icesSAG - Provides R functions that access the web services of the ICES Stock Assessment Graphs database to advance and share scientific understanding of marine ecosystems and the services they provide and to use this knowledge to generate state-of-the-art advice for meeting conservation, management, and sustainability goals.
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  • frasyr - The Japanese Fisheries Research Agency provides the method for calculating sustainable yield with R.
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  • Ichthtyop - A free Java tool designed to study the effects of physical and biological factors on ichthyoplankton dynamics.
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  • fathomnet-py - An open-source image database that can be used to train, test, and validate state-of-the-art artificial intelligence algorithms to help us understand our ocean and its inhabitants.
    • +
  • Pacific Salmon Explorer - Methods for assessing status and trends in Pacific salmon Conservation Units and their freshwater habitats.
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Plants and Vegetation

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  • OpenPlantPathology - Open Plant Pathology is an initiative that supports and promotes the spread of all open, transparent and reproducible practices in the field of plant pathology.
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  • CRootBox - The focus of CRootBox is the simulation of different types of root architecture, and to provide a generic interface for coupling with arbitrary soil/environmental models, e.g., in order to determine the impact of specific root architectures on function.
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  • PlantCV - Plant phenotyping using computer vision.
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  • Deep Plant Phenomics - A platform for plant phenotyping using deep learning.
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  • plant - A package for modeling forest trait ecology and evolution.
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  • monitoring-ecosystem-resilience - The focus is understanding vegetation patterns in semi-arid environments.
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  • Quantitative Plant - A website presenting image analysis software tools and models for plants.
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  • phenofit - A state-of-the-art remote sensing vegetation phenology extraction package.
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  • rnpn - R client for interacting with the USA National Phenology Network data web services.
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  • photosynthesis - An R package with modeling tools for C3 photosynthesis, as well as analytical tools for curve-fitting plant ecophysiology responses.
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  • phenor - The framework leverages measurements of vegetation phenology from four common phenology observation datasets combined with global retrospective and projected climate data.
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  • RBIEN - Tools for accessing the Botanical Information and Ecology Network database.
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  • rWCVP - A package for accessing and using plant name and distribution data from the World Checklist of Vascular Plants.
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  • kewr - Meant to make accessing data from one of Royal Botanic Gardenn Kew easier and to provide a consistent interface their public APIs.
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  • Plants of the World Online - An international collaborative programme that has as a primary aim to make available digitized data of the world's flora gathered from the past 250 years of botanical exploration and research.
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  • dvm-dos-tem - A process based Dynamic Vegetation, Dynamic Organic Soil, Terrestrial Ecosystem Model.
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  • blsp - A Bayesian hierarchical model that quantifies long-term annual land surface phenology from sparse time series of vegetation indices.
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  • austraits - R package for accessing the AusTraits Plant database, an open-source, harmonized database of Australian plant trait data.
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  • vegperiod - Provides methods to calculate climatological or thermal growing seasons solely based on daily mean temperatures and the day of the year.
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  • pyrealm - Providing an integrated toolbox for modelling plant productivity, growth and demography using Python.
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  • rsofun - R framework for site-scale simulations of ecosystem processes.
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  • BioCro - A model that predicts plant growth over time given crop-specific parameters and environmental data as input.
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  • OpenSimRoot - Source code for simulating root architecture, nutrient and water uptake and more.
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  • Germinate - An open source plant database infrastructure and application programming platform on which complex data from genetic resource collections can be stored, queried and visualized.
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  • greenR - An R package that enables the quantification, analysis, and visualization of urban greenness within city networks.
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Species Distribution Modeling

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  • enmSdmX - A set of tools in R for implementing species distribution models and ecological niche models.
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  • sdmTMB - An R package that fits spatial and spatiotemporal predictive-processes for species distribution models.
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  • ENMwizard - Advanced Techniques for Ecological Niche Modeling Made Easy.
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  • flexsdm - Useful tools for constructing species distribution models.
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  • Wallace - A modular platform for reproducible modeling of species niches and distributions, written in R.
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  • ENMeval - R package for automated runs and evaluations of ecological niche models.
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  • SpeciesDistributionToolkit - A collection of Julia packages forming a toolkit meant to deal with species distribution data.
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  • tidysdm - A Species Distribution Models in R.
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  • elapid - Species distribution modeling tools, including a python implementation of Maxent.
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  • SSDM - A package to map species richness and endemism based on stacked species distribution models.
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  • biomod2 - A computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships.
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  • ENMTools - This package implements various tests, visualizations, and metrics for use with environmental niche models (ENMs) and species distribution models (SDMs).
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  • spOccupancy - Fits single-species, multi-species, and integrated spatial occupancy models using Markov Chain Monte Carlo.
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  • ibis.iSDM - Provides a series of convenience functions to fit integrated Species Distribution Models.
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  • stemflow - A toolkit for Adaptive Spatio-Temporal Exploratory Model in Python using daily abundance estimation using eBird citizen science data.
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  • PointedSDMs - Simplify the construction of integrated species distribution models (ISDMs) for large collections of heterogeneous data.
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  • shinyssdtools - A Shiny application for fitting species sensitivity distribution, a method for getting water quality benchmarks to characterize effects of chemical contaminants for water quality or ecological risk assessment.
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  • ssdtools - An R package to fit and plot Species Sensitivity Distributions, cumulative probability distributions which are fitted to toxicity concentrations for different species.
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Terrestrial Wildlife

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  • Wildbook - Blends structured wildlife research with artificial intelligence, citizen science, and computer vision to speed population analysis and develop new insights to help fight extinction.
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  • WildBook-ia - Builds open software and artificial intelligence for the conservation research community; pairs with the Wildbook project.
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  • Wild Me Scout - Provides structure data processing and management for photographs from aerial surveys in support of animal census efforts, combining machine learning and expertise to get to accurate animal counts as quickly as possible.
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  • Wild Me Scoutbot - Open models for animal count and classification from aerial surveys; pairs with the Scout project.
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  • TSX - Provides reliable and robust measures of change in the relative abundance of Australia's threatened and near-threatened species at national, state and regional levels.
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  • SMART - Collect, measure and evaluate data to improve the effectiveness of your wildlife conservation efforts.
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  • GBIF Alert - A GBIF-based early alert system for invasive species.
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  • phenocamr - Facilitates the retrieval and post-processing of PhenoCam time series.
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  • Annotation Interface for Data-driven Ecology - Tools for detecting wildlife in aerial images using active learning.
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  • MegaDetector - Deep learning tools that accelerate the review of motion-triggered wildlife camera images.
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  • EcoAssist - An open-source application designed to streamline the work of ecologists dealing with camera trap images.
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  • TrapTagger - AI-Powered Camera-Trap Imagery Processing.
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  • ecoSecrets - An open-source web application that aims to facilitate biodiversity studies that use autonomous data collection devices such as camera traps.
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  • WildlifeDatasets - Pipeline for wildlife re-identification including dataset zoo, training tools and trained models.
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  • rcites - An R package to access information from the Speciesplus database, a centralised portal to key information on species of global concern.
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  • Invasive Species BC - Inventory and database repository for the creation, treatment, and report on BC Invasive plants and animals.
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Wildfire

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  • wildfire forecasting - The project intends to reproduce the Fire Forecasting capabilities of GEFF using Deep Learning and develop further improvements in accuracy, geography and time scale through inclusion of additional variables or optimization of model architecture and hyperparameters.
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  • caliver - CALIbration and VERification of gridded fire danger models.
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  • burnr - Basic tools to analyze forest fire history data (e.g. FHX) in R.
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  • Pyrovision - Computer vision library for wildfire detection.
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  • Pyronear Risks - The pyro-risks project aims at providing the pyronear-platform with a machine learning based wildfire forecasting capability.
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  • Wildfire Predictive Services - Wildfire Predictive Services to support decision making in prevention, preparedness, response and recovery.
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  • Global ECMWF Fire Forecasting - The model is a Fortran program to calculate fire danger indices from atmospheric inputs.
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  • FIREDpy - Classifying fire events from the Collection 6 MODIS Burned Area Product.
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  • qgis2fds - Export terrain elevation, landuse, and georeferencing for computational fluid dynamics wildfire or atmospheric pollutants dispersion simulations.
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  • Mesogeos - A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean.
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  • nasa-wildfires - Download wildfire hotspots detected by NASA satellites and the Fire Information for Resource Management System (FIRMS).
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  • WindNinja - Computes spatially-varying wind fields for wildland fire and other applications requiring high-resolution wind predictions in complex terrain.
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  • SimFire - An open-source wildfire simulator written in Python and meant to be used to train reinforcement learning agents.
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  • FireHistory - The goal of FireHistory is to provide tools to help extract useful fire metrics from the Department of Biodiversity, Conservation and Attractions Fire History dataset data Western Australia link.
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  • ForeFire - An open-source code for wildland fire spread models.
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  • ELMFIRE - An operational wildland fire spread model.
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  • Cell2Fire - A cell-based forest and wildland landscape fire spread simulator.
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  • FireAlert - Alerts you when heat anomalies are detected by NASA's FIRMS in your forest.
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  • Planscape - Help regional planners prioritize landscape treatments for mitigating fire risk, maximizing ecological benefits and helping California′s landscapes adapt to climate change.
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  • PyroRL - Simulates evacuating populated areas through paths from a grid world containing wildfires.
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Cryosphere

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Sea Ice

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  • Sea ice drift - Sea ice drift from Sentinel-1 SAR imagery using open source feature tracking.
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  • CICE - A computationally efficient model for simulating the growth, melting, and movement of polar sea ice.
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  • OSSP - Open Source Algorithm for Detecting Sea Ice Surface Features in High Resolution Optical Imagery.
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  • sea-ice - Displays the monthly mean sea ice extent for the Arctic and Antarctic along with the historical median extent.
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  • FESOM2 - Multi-resolution ocean general circulation model that solves the equations of motion describing the ocean and sea ice using finite-element and finite-volume methods on unstructured computational grids.
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  • IceNet - Code for Seasonal Arctic sea ice forecasting with probabilistic deep learning.
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  • COSIMA - Ocean and sea-ice model analysis tools and examples.
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  • ACCESS-OM2 - Global ocean-sea ice coupled model configurations.
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  • Polaris - Provides infrastructure, test cases and analysis tasks related to the ocean, land-ice and sea-ice components of the Exascale Energy Earth System Model.
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  • SOmap - The goal of this package is to make publication quality round Southern Ocean maps in polar projections with little effort.
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  • Icebergs - Model large tabular icebergs submerged in the ocean known as Kinematic Iceberg Dynamics.
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Glacier and Ice Sheets

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  • PyTrx - Its primary purpose is to obtain velocities, surface areas, and distances from oblique, optical imagery of glacial environments.
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  • OGGM - A modular open source model for glacier dynamics.
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  • GlaThiDa - Glacier Thickness Database.
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  • ALPGM - Regional glacier evolution model based on deep learning and parametrizations.
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  • Glacier Mapping From Satellite Imagery - Use computer vision to automatically segment debris and ice glaciers from satellite images.
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  • captoolkit - NASA's Cryosphere Altimetry Processing Toolkit.
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  • PISM - The Parallel Ice Sheet Model is an open source, parallel, high-resolution ice sheet model.
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  • icepack - Finite element modeling of glaciers and ice sheets.
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  • DeepBedMap - Using a deep neural network to better resolve the bed topography of Antarctica.
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  • SIS2 - Calculates the concentration, thickness, temperature, brine content and snow cover of an arbitrary number of ice thickness categories (including open water) as well as the motion of the complete pack.
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  • freshwater - Greenland liquid water runoff from 1958 through 2019.
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  • ecco_v4_py - A Python package that includes tools for loading and manipulating the ECCO v4 ocean and sea-ice state estimate.
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  • icepyx - Python tools for obtaining and working with ICESat-2 data.
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  • BedMachine - Matlab tools for loading, interpolating, and displaying BedMachine ice sheet topography.
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  • Iceberg Locations - Antarctic large iceberg positions derived from ASCAT and OSCAT-2.
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  • SICOPOLIS - A 3-d dynamic/thermodynamic model that simulates the evolution of large ice sheets and ice caps.
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  • cosipy - Solves the energy balance at the surface and is coupled to an adaptive vertical multi-layer subsurface module.
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  • QGreenland - A free mapping tool to support interdisciplinary Greenland-focused research, teaching, decision making, and collaboration.
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  • DeepIceDrain - Mapping and monitoring deep subglacial water activity in Antarctica using remote sensing and machine learning.
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  • LIVVkit - The land ice verification and validation toolkit.
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  • pypromice - Deliver data about the mass balance of the Greenland ice sheet in near real-time.
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  • GLAFT - Python module for assessing glacier velocity maps using statistics- and physics-based metrics.
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  • ITS_LIVE - Provide automated, low latency, global glacier flow and elevation change datasets.
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  • ODINN.jl - Global glacier model using Universal Differential Equations for climate-glacier interactions.
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  • Yelmo - A 3D ice-sheet-shelf model solving for the coupled dynamics and thermodynamics of the ice sheet system.
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  • WAVI.jl - A fast and friendly ice sheet model, written in Julia.
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  • SICE - An automated open source processing chain to determine daily albedo and other surface properties of glaciated areas.
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  • Iceberg Tracking Beacon - A low-cost, robust, and user-friendly ice drift tracking beacon based on the open-source electronics Arduino platform capable of providing long-term cryospheric observations.
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  • Glacier Velocity Tracker - A low-cost, open-source glacier velocity measurement system intended for use with Precise Point Positioning techniques.
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  • CryoInTheCloud - Aim to establish a curated interactive computing platform and develop Cryosphere community expertise in open-soured and cloud-based platforms.
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  • polartoolkit - A Python package with the goal of making Polar (i.e. Antarctic, Arctic, Greenland) research more efficient, reproducible, and accessible.
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  • ISSM - A large-scale thermo-mechanical 2D/3D parallelized multi-purpose finite-element software dedicated to ice sheet and sea-level modeling.
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  • MPAS-Albany - Houses the land-ice component of the U.S. Department of Energy's Energy Exascale Earth System Model known as MPAS-Albany Land Ice.
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  • Glacier flow model - Modeling glaciers on a digital elevation model based on mass balance and a modified D8 flow algorithm applied to ice.
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  • icepack - A library for modeling the flow of ice sheets and glaciers using the finite element method.
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Snow and Permafrost

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  • Planet Snowcover - A project that pairs airborne lidar and Planet Labs satellite imagery with cutting-edge computer vision techniques to identify snow-covered area at unprecedented spatial and temporal resolutions.
    • +
  • smrt - Snow Microwave Radiative Transfer model to compute thermal emission and backscatter from snowpack.
    • +
  • FSM2 - The Flexible Snow Model is a multi-physics energy balance model of snow accumulation and melt, extending the Factorial Snow Model (Essery, 2015) with additional physics, driving and output options.
    • +
  • Teaspoon - A python library designed to make working with permafrost ground temperature time series data more straightforward, efficient, and reproduceable.
    • +
  • BioSNICAR - Predicts the spectral albedo of snow and glacier ice between 200nm to 5000nm given information about the illumination conditions, ice structure and the type and concentration particulates.
    • +
  • Permamodel - A collection of numerical permafrost models with a range of capability and complexity.
    • +
  • SNOWPACK - A multi-purpose snow and land-surface model, which focuses on a detailed description of the mass and energy exchange between the snow, the atmosphere and optionally with the vegetation cover and the soil.
    • +
  • MuSA - A flexible data assimilation toolbox for experimental and operational snowpack reanalysis development.
    • +
  • snotelr - R toolbox to facilitate easy SNOTEL data exploration and downloads through a convenient shiny based GUI.
    • +
  • sar_snowmelt_timing - Contains notebooks and tools to identify snowmelt timing using time series analysis of backscatter of Sentinel-1 C-band SAR.
    • +
  • From Snow to Flow - Describe important dynamics that determine how snow turns into flow, and the charts show the connection between snowpack (measured as snow water equivalent) and streamflow (measured as discharge).
    • +
+

Hydrosphere

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Freshwater and Hydrology

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  • Raven - Made to help scientists run hydrological modeling experiments with climate change projections.
    • +
  • RavenPy - A Python wrapper to setup and run the hydrologic modelling framework Raven.
    • +
  • hydroscoper - An R interface to the Greek National Data Bank for Hydrometeorological Information.
    • +
  • WRF-Hydro - A community modeling system and framework for hydrologic modeling and model coupling.
    • +
  • rwrfhydro - A community-contributed tool box for managing, analyzing, and visualizing WRF Hydro (and HydroDART) input and output files in R.
    • +
  • PCR-GLOBWB_model - A large-scale hydrological model intended for global to regional studies.
    • +
  • HydroShare - A collaborative website for better access to data and models in the hydrologic sciences.
    • +
  • SOILWAT2 - An ecosystem water balance simulation model.
    • +
  • RivGraph - Extracting and quantifying graphical representations of river and delta channel networks from binary masks.
    • +
  • WaterDetect - End-to-end algorithm to generate open water cover mask, specially conceived for L2A Sentinel 2 imagery from MAJA1 processor, without any a priori knowledge on the scene.
    • +
  • FLAREr - Flexible, scalable, robust, and near-real time iterative ecological forecasts in lakes and reservoirs.
    • +
  • Buhayra - Obtaining water extent of small reservoirs in semi-arid regions from satellite data in real-time.
    • +
  • Wflow - A Julia package that provides a hydrological modeling framework, as well as several different vertical and lateral concepts that can be used to run hydrological simulations.
    • +
  • ParFlow - An open-source, modular, parallel watershed flow model.
    • +
  • River Runner - Visualize the path of a rain droplet from any point in the contiguous United States to its end point.
    • +
  • visGWDB - A framework for groundwater-level informatics.
    • +
  • LakePy - Pythonic user-centered front-end to the Global Lake Level Database.
    • +
  • rivr - Designed as an educational tool for students and instructors of undergraduate and graduate courses in open channel hydraulics.
    • +
  • eWaterCycle - Makes it easier to use hydrological models without having intimate knowledge about how to install and run the models.
    • +
  • Pastas - An open-source Python framework for the analysis of groundwater time series.
    • +
  • VIC - A macroscale hydrologic model that solves full water and energy balances.
    • +
  • Badlands - Basin and Landscape Dynamics is a long-term surface evolution model built to simulate landscape development, sediment transport and sedimentary basins formation from upstream regions down to marine environments.
    • +
  • LAGOSNE - Interface to the LAke multi-scaled GeOSpatial & temporal database.
    • +
  • RiverREM - Make river relative elevation models and REM visualizations from an input digital elevation model.
    • +
  • mHM - The mesoscale Hydrological Model.
    • +
  • LISF - A software framework for high performance terrestrial hydrology modeling and data assimilation developed with the goal of integrating satellite and ground-based observational data products.
    • +
  • Conceptual Functional Equivalent - A conceptual rainfall-runoff model with an implementation of the Basic Model Interface.
    • +
  • NeuralHydrology - Python library to train neural networks with a strong focus on hydrological applications.
    • +
  • Surface water network - A Python package to create and analyze surface water networks.
    • +
  • Lekan - Provide a software that assists the user doing hydrological and hydraulic studies for flood mapping and forecasting, hydraulic structure design, or other tasks linked to natural surface flow.
    • +
  • RUBEM - A distributed hydrological model to calculate monthly flows with changes in land use over time.
    • +
  • pywatershed - A sustainable integrated, hydrologic modeling framework for the U.S. Geological Survey.
    • +
  • pyMETRIC - A set of Python based tools developed for estimating and mapping evapotranspiration for large areas, utilizing the Landsat image archive.
    • +
  • SWAT - The Soil & Water Assessment Tool is a small watershed to river basin-scale model used to simulate the quality and quantity of surface and ground water and predict the environmental impact of land use, land management practices, and climate change.
    • +
  • SWATrunR - Allows the user to control the essential parameters of a SWAT simulation run, such as model parameter changes, simulation time periods, or time intervals for printing output used for global soil and water assessment.
    • +
  • SWATprepR - Developed to re-use water and nutrients in small agricultural catchments across different soil-climatic regions in Europe.
    • +
  • HydroMap - Mapping of groundwater level for realistic flow flowpaths using semi-automated kriging.
    • +
  • HydroSight - A statistical toolbox for data-driven insights into groundwater dynamics and aquifer properties. Many hundreds of bores can be easily analysed, all without any programming.
    • +
  • basin3d - A generalized data synthesis model that applies across a variety of earth science observation types (hydrology, geochemistry, climate etc.).
    • +
  • hspfbintoolbox - A Python script and library of functions to read Hydrological Simulation Program Fortran (HSPF) binary files and print to screen.
    • +
  • Download Water Data - Downloader for the Global Surface Water Data of the Copernicus Programme.
    • +
  • pyOpenRiverCam - Surface velocity, object tracking, and river flow measurements in an open-source API.
    • +
  • SHUD - A multi-process, multi-scale integrated hydrological model using the semi-discrete Finite Volume Method.
    • +
  • PyFlowline - A mesh independent river network generator for hydrologic models.
    • +
  • HexWatershed - A mesh independent flow direction model for hydrologic models.
    • +
  • rabpro - Delineating watershed basins and computing attribute statistics using Google Earth Engine.
    • +
  • imod - An open source project to make working with MODFLOW groundwater models in Python easier.
    • +
  • SuperflexPy - An open-source framework written in Python for constructing flexible, conceptual, distributed hydrological models.
    • +
  • HydPy - An interactive framework for developing and applying different types of hydrological models, originally developed at the Ruhr-University Bochum for specific research purposes.
    • +
  • Xanthos - An open-source hydrologic model, written in Python, designed to quantify and analyze global water availability.
    • +
  • nlmod - Python package to build, run and visualize MODFLOW 6 groundwater models in the Netherlands.
    • +
  • Centerline-Width - A Python package to find the centerline and width of rivers based on the latitude and longitude of the right and left bank.
    • +
  • fwapg - Extends British Columbia's Freshwater Atlas with PostgreSQL/PostGIS.
    • +
  • HSPsquared - A Python port of the Hydrological Simulation Program - FORTRAN, which has been used worldwide for more than 40 years to support water resources planning and management.
    • +
  • ClearWater-Riverine - A 2D water quality transporter model to calculate conservative advection and diffusion of constituents from an unstructured grid of flows.
    • +
  • The Canadian Hydrological Model - A novel modular unstructured mesh based approach for hydrological modelling.
    • +
  • pyflwdir - Fast methods to work with hydro- and topography data in pure Python.
    • +
  • SWOT-OpenToolkit - Community codes for processing SWOT data, to provide valuable data and information about the world's oceans and its terrestrial surface water such as lakes, rivers, and wetlands.
    • +
  • HydroMT - An open-source Python package that facilitates the process of building and analyzing spatial geoscientific models with a focus on water system model.
    • +
  • Watershed Workflow - Python workflows for data-rich, hyper-resolution simulations of hydrologic models on watersheds.
    • +
  • RoGeR - Runoff Generation Research, is a process-based hydrological model that can be applied from plot to catchment scale.
    • +
  • ATS - The Advanced Terrestrial Simulator is a code for solving ecosystem-based, integrated, distributed hydrology.
    • +
  • xHydro - Effortlessly extract geospatial and watershed data from APIs, calibrate and execute hydrological models, perform optimal interpolation and frequency analysis on hydrological indicators, compute custom hydrological metrics, and analyze climate change impacts on hydrological data.
    • +
  • flow-tiles - A tile map showing a month of streamflow conditions across the U.S.
    • +
  • Delaware River Basin Story - Visualisation and story of the Delaware River Basin, highlighting the essential water needs of its people and industries, while showcasing the vital role of the rivers and estuary in supporting unique ecosystems, species and habitats where the river meets the ocean.
    • +
  • Hy2DL - A python library to create hydrological models for rainfall-runoff prediction, which make use of deep learning methods.
    • +
  • GEB - Coupling of an agent-based model which simulates millions individual people or households, a hydrological model, a vegetation model and a hydrodynamic model.
    • +
  • WATex - A Python-based library primarily designed for Groundwater Exploration.
    • +
  • tRIBS - The TIN-based Real-Time Integrated Basin Simulator is a fully-distributed, continuous hydrologic model operating on a Triangulated Irregular Network.
    • +
  • BASINS - Better Assessment Science Integrating Point and Nonpoint Sources is a multipurpose environmental analysis system designed to help regional, state, and local agencies perform watershed- and water quality-based studies.
    • +
  • Niche Vlaanderen - A hydro-ecological model that predicts the potential occurrence of (ground)water-dependent vegetation types in an area based on information about the (abiotic) site conditions.
    • +
  • Amanzi - Provides a flexible and extensible parallel flow and reactive transport simulation capability for environmental applications.
    • +
  • fluvgeo - An R Package for Performing Fluvial Geomrphology Analysis.
    • +
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Ocean Models

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  • MOM6 - A numerical representation of the ocean fluid with applications from the process scale to the planetary circulation scale.
    • +
  • MOM5 - A numerical ocean model based on the hydrostatic primitive equations.
    • +
  • Bergen Layered Ocean Model - Employs an isopycnic vertical coordinate, with near-isopycnic interior layers and variable density layers in the surface mixed boundary layer.
    • +
  • Oceananigans.jl - Fast and friendly fluid dynamics on CPUs and GPUs.
    • +
  • NEMO - Nucleus for European Modelling of the Ocean is a state-of-the-art modeling framework for research activities and forecasting services in ocean and climate sciences.
    • +
  • GOLD - Stands for Generalized Ocean Layer Dynamics and is a hybrid coordinate finite volume ocean model code funded by NOAA and developed by the ocean group at NOAA-GFDL and Princeton University.
    • +
  • Veros - Powerful tool that makes high-performance ocean modeling approachable and fun.
    • +
  • MITgcm - A flexible non-hydrostatic formulation that efficiently simulates fluid phenomena over a wide range of scales.
    • +
  • MITgcm.jl - Julia interface to MITgcm that allows user not only to analyze model output, but also to download the source code, build the model executable, modify run-time parameters, run model simulations, or verify model results against a benchmark.
    • +
  • ccpp-physics - The Common Community Physics Package is designed to facilitate the implementation of physics innovations in state-of-the-art atmospheric models, the use of various models to develop physics, and the acceleration of transition of physics innovations to operational NOAA models.
    • +
  • MOHID-Lagrangian - Mainly developed for oceanographic and fluvial modeling, application to atmospheric and other planetary settings should be trivial.
    • +
  • Mohid - A modular finite volumes water-modeling system written in ANSI-Fortran95 using an Object-oriented programming philosophy, integrating diverse mathematical models and supporting graphical user interfaces that manage all the pre- and post-processing.
    • +
  • CDFTOOLS - A Fortran package for analysis and diagnostics on NEMO ocean model output.
    • +
  • GOTM - The General Ocean Turbulence Model is an ambitious name for a one-dimensional water column model for marine and limnological applications.
    • +
  • ROMS - A free-surface, terrain-following, primitive equations ocean model widely used by the scientific community for a diverse range of applications.
    • +
  • pyroms - A collection of tools to process input and output files from the Regional Ocean Modeling System.
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  • wrfhydropy - Provides an end-to-end python interface to support reproducible research and construction of workflows involving the WRF-Hydro model.
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  • HYCOM - A three-dimensional depiction of the ocean state at fine resolution in real time, provision of boundary conditions for coastal and regional models, and provision of oceanic boundary conditions for a global coupled ocean-atmosphere prediction model.
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  • TrackEddy - Used to investigate trends of eddy kinetic energy in the ocean using satellite sea surface height anomaly.
    • +
  • xroms - Work with ROMS ocean model output with xarray and calculate many derived variables with correct grid metrics in one line, including horizontal velocity, kinetic energy and eddy kinetic energy.
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  • SalishSeaNowcast - A collection of Python modules associated with running the SalishSeaCast ocean models system of daily nowcasts and forecasts.
    • +
  • SSN2 - An R package for spatial statistical modeling and prediction on stream networks, including models based on in-stream distance.
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  • MagicBathyNet - A Multimodal Remote Sensing Dataset for Benchmarking Learning-based Bathymetry and Pixel-based Classification in Shallow Waters.
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Waves and Currents

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  • leaflet-velocity - Create a canvas visualization layer for direction and intensity of arbitrary velocities (e.g. wind, ocean current).
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  • OpenDrift - A software for modeling the trajectories and fate of objects or substances drifting in the ocean, or even in the atmosphere.
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  • dorado - Simulating passive Lagrangian particle transport over flow-fields from any 2D shallow-water hydrodynamic model using a weighted random walk methodology.
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  • parcels - Can be used to track passive and active particulates such as water, plankton, plastic and fish.
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  • wavespectra - An open source project for working with ocean wave spectral data.
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  • LESbrary.jl - Generating a library of ocean turbulence large eddy simulation data to train ocean and climate models.
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  • dnora - A software for dynamical downscaling of wave products i.e., NORA3 wave hindcast and WW3 wave forecast from Norwegian Meteorological Institute and ERA5 from ECMWF.
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  • ecWAM - The ECMWF Ocean Wave Model describes the development and evolution of wind generated surface waves and their height, direction and period.
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Ocean Carbon and Temperature

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  • CO2SYS - A MATLAB (or Octave) project to compute variables of ocean CO2 systems.
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  • PyCO2SYS - Marine carbonate system calculations in Python.
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  • FluxEngine - An open source atmosphere ocean gas flux data processing toolbox.
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  • seacarb - An R package that calculates various parameters of the carbonate system in seawater.
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  • TSG-QC - Analysis and validation of underway Sea Surface Temperature and Sea Surface Salinity measurements from a SeaBird Thermosalinograph.
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  • Ocean Health Index Scores - Provide invaluable, comprehensive, and quantitative assessments of progress towards healthy and sustainable oceans.
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  • ERSEM - A marine biogeochemical and ecosystem model that describes the cycling of carbon, nitrogen, phosphorus, silicon, oxygen and iron through the lower trophic level pelagic and benthic ecosystems.
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  • AIBECS.jl - A Julia package that provides ocean biogeochemistry modelers with an easy-to-use interface for creating and running models of the ocean system.
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  • mocsy - Routines to model ocean carbonate system thermodynamics.
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  • cbsyst - A Python module for calculating seawater carbon and boron chemistry.
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  • Open Acidification Project - Apparatus to determine total alkalinity in sea water using an open-cell titration.
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  • m_mhw - Detect and analyse spatial marine heatwaves.
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  • AutoQC - A testing suite for automatic quality control checks of subsurface ocean temperature observations.
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  • marineHeatWaves - A module for Python which implements the Marine Heatwave definition of Hobday et al. (2016).
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  • heatwaveR - Contains the original functions from the RmarineHeatWaves package that calculate and display marine heatwaves according to the definition of Hobday et al. (2016).
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  • GSW-Python - This Python implementation of the Thermodynamic Equation of Seawater 2010.
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  • heatwave3 - The Goal of this package is to make life easier when working with the Hobday et al. marine heatwave definition on large datasets.
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  • Calkulate - Calkulate is a Python package for finding total alkalinity from titration data using PyCO2SYS.
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Coastal and Reefs

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  • py-wave-runup - A Python module which makes it easy for coastal engineers and scientists to test and use various empirical wave runup models which have been published in literature.
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  • CoastSat - Enables users to obtain time-series of shoreline position at any coastline worldwide from 30+ years of publicly available satellite imagery.
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  • PySAMOSA - A Python-based software for processing open ocean and coastal waveforms from SAR satellite altimetry to measure sea surface heights, wave heights, and wind speed for the oceans and inland waters.
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  • Digital Earth Australia Coastlines - Extracting tidally-constrained annual shorelines and robust rates of coastal change from freely available Earth observation data at continental scale.
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  • Thetis - An unstructured grid coastal ocean model built using the Firedrake finite element framework.
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  • OceanMesh2D - Precise distance-based two-dimensional automated mesh generation toolbox intended for coastal ocean/shallow water flow models.
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  • oceanmesh - A Python package for the development of unstructured triangular meshes that are used in the simulation of coastal ocean circulation.
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  • AeoLiS - Simulating aeolian sediment transport in situations where supply-limiting factors are important, like in coastal environments.
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  • REEF3D - An efficiently parallelized hydrodynamics framework with a focus on coastal, marine and hydraulic engineering flows.
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  • pygetm - A Python rewrite of the General Estuarine Transport Model.
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  • PyGnome - It is designed to support oil and other hazardous material spills in the coastal environment.
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  • Coastwards - A global citizen science project to help scientists study the risks of sea-level rise.
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  • Coastal Carbon Network Data Library - Accelerating the pace of discovery in coastal wetland carbon science by providing our community with access to data, analysis tools, and synthesis opportunities.
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  • HyperCoast - A Python package for visualizing and analyzing hyperspetral data in coastal regions.
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  • baytrends - Evaluate long-term trends in the Chesapeake Bay using a Generalized Additive Modeling approach.
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  • BayDeltaSCHISM - An application of the 3D open source SCHISM hydrodynamic and water quality suite to the San Francisco Bay Delta estuary.
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  • OpenCTD - A low-cost, open-source CTD designed for budget-restricted scientists, educators, and researchers working in nearshore coastal ecosystems.
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  • IOOS Cloud Sandbox - A collaborative platform to develop regional coastal models.
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  • COAWST - Combines many sophisticated systems that each provide relative earth-system components necessary to investigate the dynamics of coastal storm impacts.
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  • OSMCoastline - Extracts the coastline data from an OSM planet file and assembles all the pieces into polygons for use in map renderers.
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Ocean and Hydrology Data Access

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  • HyRiver - A Python software stack for retrieving hydroclimate data from web services.
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  • pydaymet - Retrieving and post-processing climate data from the Daymet Webservice.
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  • argopy - A global network of nearly 4000 autonomous probes measuring pressure, temperature and salinity from the surface to 2000m depth every 10 days.
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  • tidyhydat - An R package to import Water Survey of Canada hydrometric data and make it tidy.
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  • OceansDB - A database of marine reference data like climatologies and bathymetry.
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  • stglib - Routines used by the USGS Coastal/Marine Hazards & Resources Program to process oceanographic time-series data.
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  • noaa_coops - A Python wrapper for the NOAA CO-OPS Tides & Currents Data and Metadata APIs.
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  • Ferret - An interactive computer visualization and analysis environment designed to meet the needs of oceanographers and meteorologists analyzing large and complex gridded datasets.
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  • Blueant - Environmental data for Antarctic and Southern Ocean science.
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  • VAPOR - The Visualization and Analysis Platform for Ocean, Atmosphere and Solar Researchers.
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  • Ocean-Data-Map-Project - A Data Visualization tool that enables users to discover and view 3D ocean model output quickly and easily.
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  • OceanSpy - An open source and user-friendly Python package that enables scientists and interested amateurs to analyze and visualize ocean model datasets.
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  • oce - An R package for oceanographic processing.
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  • GPM-API - Provides an easy-to-use python interface to download, read, process and visualize most of the products of the Global Precipitation Measurement Mission (GPM) data archive.
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  • hddtools - An open source project designed to facilitate access to a variety of online open data sources relevant for hydrologists and, in general, environmental scientists and practitioners.
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  • hydrobr - Help users select, download and clean data from pluvio- and fluviometric stations from the Brazilian National Water Agency.
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  • EMODnetWFS - Allow interrogation of and access to EMODnet(European Marine Observation and Data Network) geographic vector data in R though the EMODnet Web Feature Services.
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  • STOQS - Geospatial database visualization software for oceanographic measurement data.
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  • odp_sdk - Download queried ocean data easily and efficiently into data frames, for easy exploring and further processing in your data science project.
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  • AODN Portal - The gateway to Australian marine and climate science data.
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  • Sea-level monitor - Tools and applications used to monitor sea-level rise.
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  • Ocean Networks Canada - Provides easy access to Ocean Networks Canada data in Python.
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  • StreamCat - Provides summaries of natural and anthropogenic landscape features for ~2.65 million streams, and their associated catchments, within the conterminous USA.
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  • cmocean - Colormap setup for standardizing commonly-plotting oceanographic variables.
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  • GlobalDeltaChange - A theoretical framework to predict delta morphology and delta change, and a set of codes to make this predictions on a global scale for about 11,000 deltas.
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  • WaterwayMap - River Basins from OpenStreetMap.
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  • gwavr - Allows the user to point and click on areas within the United States and get back hydrological data, e.g. flowlines, catchments, basin boundaries, comids, etc.
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  • OpenDCS - Open Data Collection System for Hydro/Meteorologic Data.
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  • Hydrocron - An API that repackages hydrology datasets from the Surface Water and Ocean Topography satellite into formats that make time-series analysis easier.
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  • EPyGrAM - Enhanced Python for Graphics and Analysis of Meteorological fields.
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  • Hydropandas - A Python package for reading, analyzing, and writing hydrological time series from a pandas DataFrame, with all of its wonderful features, and extended with custom methods and attributes related to hydrological time series.
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  • mregions2 - Offers a streamlined interface to access data from Marine Regions in R for researchers, marine scientists, and geospatial analysts seeking marine geographical information.
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  • pyTMD - Python-based tidal prediction software for estimating ocean, load, solid Earth and pole tides.
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  • gliderpy - A thin wrapper around erddapy with custom methods and plotting functionality to browse, fetch, and plot glider data.
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  • OPERA Applications - Contain several Jupyter notebooks that provide introductions and showcase applications of the OPERA products including flood mapping, water reservoir monitoring and monitoring wildfire evolution.
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  • CopernicusMarine - Subset and download marine data from the EU Copernicus Marine Service Information to import data on the physical and biogeochemical state of the oceans without the need for external software.
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  • hf_hydrodata - This Python package is a product of the HydroFrame project and is designed to provide easy access to national hydrologic simulations generated using the National ParFlow model as well as a variety of other gridded model input datasets and point observations.
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  • pybomwater - Provides access to Web Data Services ( SOS2 ) station reference data and historical time series water data for stations across Australia, collected by the Bureau of Meteorology.
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  • Ocean Data and Information System - The Ocean InfoHub facilitates access to global oceans information, data and knowledge products for management and sustainable development.
    • +
  • GroMoPo - Explore or share groundwater model data, knowledge, and insights through this unique portal of regional and global numerical groundwater models.
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  • Metocean-stats - A tool for comprehensive statistics and visualization of metocean data like wind, waves, ocean currents, tide levels, air and water temperature, sea ice, and more.
    • +
  • hubeau - An R-package proposing a collection of function to help retrieve data from the French national databases on water.
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  • grundwasser-data - A Germany-wide analysis of groundwater levels between 1990 and 2021.
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  • Enhydris - A system for the storage and management of hydrological and meteorological time series.
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  • LeakDB - A realistic leakage dataset for water distribution networks. The dataset is comprised of a large number of artificially created but realistic leakage scenarios, on different water distribution networks, under varying conditions.
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Atmosphere

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Atmospheric Composition and Dynamics

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  • qgs - Models the dynamics of a 2-layer quasi-geostrophic channel atmosphere on a beta-plane, coupled to a simple land or shallow-water ocean component.
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  • pyglow - A Python module that wraps several upper atmosphere climatological models written in FORTRAN.
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  • Mission Support System - A collaboration server to plan atmospheric research flights.
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  • MiMA - Model of an idealized Moist Atmosphere: Intermediate-complexity General Circulation Model with full radiation.
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  • Isca - A framework for the idealized modeling of the global circulation of planetary atmospheres at varying levels of complexity and realism.
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  • pyvortex - Equivalent Latitude and polar vortex edge calculation using Nash criteria.
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  • ClimaAtmos.jl - A library for building atmospheric circulation models that is designed from the outset to leverage data assimilation and machine learning tools.
    • +
  • WaveBreaking - A python package that provides detection, classification and tracking of Rossby Wave Breaking in weather and climate data.
    • +
  • typhon - A collection of tools for atmospheric research with Python 3.
    • +
  • Pace - A implementation of the FV3GFS / SHiELD atmospheric model developed by NOAA/GFDL using the GT4Py domain-specific language in Python.
    • +
  • Project Horus - A Amateur Radio High Altitude Ballooning project.
    • +
  • ANEMOI - Large-eddy simulation code written in CUDA Fortran for simulating atmospheric boundary layer flows.
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  • CIS - An open source command-line tool for easy collocation, visualization, analysis, and comparison of diverse gridded and ungridded datasets used in the atmospheric science.
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  • EMC² - An open source framework for atmospheric model and observational column comparison.
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  • Unidata Science Gateway - Provide the academic and research community an environment they can employ to access, analyze and visualize real-time and case study Earth system science data.
    • +
  • SounderPy - A python package that helps you to access and plot vertical profile data for meteorological analysis.
    • +
  • SHARPpy - A collection of open source sounding and hodograph analysis routines, a sounding plotting package, and an interactive, cross-platform application for analyzing real-time soundings all written in Python.
    • +
  • AC_tools - Contains functions and scripts used for working with atmospheric model output and observational data.
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  • ACT - The Atmospheric data Community Toolkit is an open source Python toolkit for working with atmospheric time-series datasets of varying dimensions.
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  • Freva - A data search and analysis platform developed by the atmospheric science community for the atmospheric science community.
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  • SpeedyWeather.jl - A global spectral atmospheric model with simple physics which is developed as a research playground with an everything-flexible attitude as long as it is speedy.
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  • A-Profiles - A python library dedicated to the analysis of atmospheric profilers measurements.
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  • LROSE - The Lidar Radar Open Software Environment for Atmospheric Science.
    • +
  • CalibrateEDMF.jl - Calibrate atmospheric turbulence and convection parameterizations using gradient-free ensemble Kalman methods.
    • +
  • jsmetrics - An open-source Python package containing implementations of various statistics and algorithms which have been developed to identify or characterise jet streams.
    • +
  • pySonde - Converts Vaisala's radiosonde files (mwx) to netCDF4 and interpolates them if needed to a common height grid for easier processing.
    • +
  • TurbulenceConvection.jl - Provides a framework for testing parameterizations of clouds and turbulence.
    • +
  • xinvert - A Python package to invert or solve many classical problems in atmospheric sciences and physical oceanography.
    • +
  • ACE - A 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model.
    • +
  • Pace - An implementation of the FV3GFS / SHiELD atmospheric model developed by NOAA/GFDL using the NDSL middleware in Python, itself based on GT4Py and DaCe.
    • +
  • Aurora - An atmospheric foundation machine learning model initially trained on vast amounts of data to predict atmospheric variables like temperature, which can then be adapted to specialized atmospheric forecasting tasks with relatively little additional data.
    • +
  • AtmoRep - A stochastic model of atmosphere dynamics using large scale representation learning.
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+

Atmospheric Dispersion and Transport

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    +
  • MPTRAC - A Lagrangian particle dispersion model for the analysis of atmospheric transport processes in the free troposphere and stratosphere.
    • +
  • GRAL - A Lagrangian dispersion model with reasonable demands on computational times and sensible accuracy.
    • +
  • SNAP - A lagrangian type atmospheric dispersion model specialized on modelling dispersion of radioactive debris.
    • +
  • GasDispersion.jl - A set of tools for atmospheric dispersion modeling of gaseous releases, such as might occur during an emergency at a chemical plant or more routinely from a stack.
    • +
  • CloudDrift - Accelerates the use of Lagrangian data for atmospheric, oceanic, and climate sciences.
    • +
  • IPART - A Python package for automated Atmospheric River (AR) detection, axis finding and AR tracking from gridded Integrated Vapor Transport data.
    • +
  • WAM2layers - Used to determine where precipitation originally evaporated (backtracking), or where evaporated moisture eventually ends up (forward tracking).
    • +
+

Atmospheric Chemistry and Aerosol

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    +
  • GEOS-Chem - Advance understanding of human and natural influences on the environment through a comprehensive, state-of-the-science, readily accessible global model of atmospheric composition.
    • +
  • gcpy - A Python-based toolkit containing useful functions for working specifically with the GEOS-Chem model of atmospheric chemistry and composition.
    • +
  • PartMC - Particle-resolved Monte Carlo code for atmospheric aerosol simulation.
    • +
  • PyCHAM - CHemistry with Aerosol Microphysics in Python box model for Windows, Linux and Mac.
    • +
  • Chemical Lagrangian Model of the Stratosphere - A world leader in simulating exchange processes in the atmosphere across transport barriers such as stratosphere-troposphere exchange.
    • +
  • PyBox - A Python based box-model generator and simulator designed for atmospheric chemistry and aerosol studies.
    • +
  • MICM Chemistry - A unique chemistry module that can be implemented in any atmosphere model used at NCAR.
    • +
  • PySDM - Pythonic particle-based warm-rain/aqueous-chemistry cloud microphysics package.
    • +
  • pyrcel - An implementation of a simple, adiabatic cloud parcel model for use in aerosol-cloud interaction studies.
    • +
  • ORAC - An optimal estimation retrieval scheme for the estimation of aerosol and cloud properties from a wide range of visible-infrared imaging satellites, such as MODIS, AATSR, AVHRR and SEVIRI.
    • +
  • CAMP - Allows a given chemical mechanism to be solved in atmospheric models with different aerosol representations.
    • +
  • pyaerocom - An open international initiative of scientists interested in the advancement of the understanding of the global aerosol and its impact on climate.
    • +
  • py-smps - Python package for analyzing and visualizing size-resolved aerosol data from a variety of aerosol sizing instruments.
    • +
  • MONET - An open-source project and Python package that aims to create a common platform for atmospheric composition data analysis for weather and air quality models.
    • +
  • APCEMM - Aims to assess the chemical and microphysical perturbations introduced by a conventional aircraft, equipped with gas turbine engines.
    • +
  • EUREC4A Intake catalogue - Access to data from the EUREC4A field campaign, which aims to improve the understanding of the interplay between clouds, convection and circulation and their role in climate change.
    • +
  • dwarf-p-cloudsc - Standalone mini-app of the ECMWF cloud microphysics parameterization.
    • +
  • UWLCM - A tool for numerical modeling of clouds using LES model of turbulence and Lagrangian cloud microphysics.
    • +
  • CLEO - A library for Super-Droplet Model (SDM) cloud microphysics.
    • +
  • CloudnetPy - A Python software designed for producing vertical profiles of cloud properties from ground-based remote sensing measurements.
    • +
  • tobac - A Python package for identifiying, tracking and analysing of clouds and other meteorological phenomena in different types of gridded datasets.
    • +
+

Meteorological Observation and Forecast

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    +
  • windrose - A graphic tool used by meteorologists to give a succinct view of how wind speed and direction are typically distributed at a particular location.
    • +
  • METplus - A verification framework that spans a wide range of temporal (warn-on-forecast to climate) and spatial (storm to global) scales.
    • +
  • WRF-ARW - The official repository for the Weather Research and Forecasting model.
    • +
  • wrf-python - A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting Model.
    • +
  • Open-Meteo - Global weather API for non-commercial use with hourly weather forecast.
    • +
  • stationaRy - Get hourly meteorological data from one of thousands of global stations.
    • +
  • weathercan - This package makes it easier to search for and download multiple months/years of historical weather data from the Environment and Climate Change Canada (ECCC) website.
    • +
  • metR - Several functions and utilities that make R better for handling meteorological data in the tidy data paradigm.
    • +
  • climate - The goal of the climate R package is to automatize downloading of meteorological and hydrological data from publicly available repositories.
    • +
  • rdwd - An R package to select, download and read climate data from the German Weather Service.
    • +
  • MetPy - A collection of tools in Python for reading, visualizing and performing calculations with weather data.
    • +
  • wetterdienst - Trying to make access to weather data in Python feel like a warm summer breeze.
    • +
  • AWIPS - The Advanced Weather Interactive Processing System is a meteorological display and analysis package originally developed by the National Weather Service and Raytheon.
    • +
  • Metview Python bindings - Python interface to Metview, a meteorological workstation and batch system for accessing, examining, manipulating and visualising meteorological data.
    • +
  • Herbie - A python package that downloads recent and archived numerical weather prediction model output from different cloud archive sources.
    • +
  • MEWS - A Python package designed to add extreme weather events to existing weather data or projections.
    • +
  • eeweather - Fetch NCDC ISD, TMY3, or CZ2010 weather data that corresponds to ZIP Code Tabulation Areas or Latitude/Longitude.
    • +
  • met.3D - Interactive three-dimensional visualization of numerical ensemble weather predictions and similar numerical atmospheric model datasets.
    • +
  • agera5tools - Tools for mirroring, manipulating and serving Global Weather for Agriculture data (AgERA5).
    • +
  • MeteoInfo - GIS and scientific computation environment for meteorological community.
    • +
  • thundeR - Rapid computation and visualisation of convective parameters from rawinsonde and Numerical weather prediction data.
    • +
  • AtmoSwing - Allow predicting local meteorological variables of interest, such as the daily precipitation, based on synoptic variables.
    • +
  • ufs-weather-model - Contains the model code and external links needed to build the Unified Forecast System atmosphere model and associated components, including the WaveWatch III model.
    • +
  • WeatherBench 2 - A framework for evaluating and comparing data-driven and traditional numerical weather forecasting models.
    • +
  • MeteoHist - A Streamlit app to create interactive temperature and precipitation graphs for places around the world.
    • +
  • ecPoint-Calibrate - A software that uses conditional verification tools to compare numerical weather prediction model outputs against point observations and, in this way, anticipate sub-grid variability and identify biases at grid scale.
    • +
  • imdlib - Download and handle binary grided data from Indian Meterological department.
    • +
  • MetNet - A neural network that forecasts precipitation up to 8 hours into the future at the high spatial resolution of 1 km² and at the temporal resolution of 2 minutes with a latency in the order of second.
    • +
  • jmastats - Download Weather Data from Japan Meteorological Agency Website.
    • +
  • pyIEM - A collection of python support things that make my weather processing work, work.
    • +
  • forest - Forecast and Observation Research and Evaluation Survey Tool.
    • +
  • AMBS - The Atmopsheric Machine Learning Benchmarking System aims to provide state-of-the-art video prediction methods applied to the meteorological domain.
    • +
  • gis4wrf - A free and open source QGIS plug-in to help researchers and practitioners with their Advanced Research Weather Research and Forecasting modeling workflows.
    • +
  • Weather Stations - A list of public weather stations everyone can edit and share.
    • +
  • GEMPAK - Analysis and product generation for meteorological data.
    • +
  • PostWRF - Visualization and postprocessing of the WRF and ERA5 data.
    • +
  • GraphCast - Learning skillful medium-range global weather forecasting.
    • +
  • Graph Weather - Data-driven approach for forecasting global weather using graph neural network.
    • +
  • brightsky - A JSON API for Germany's meteorological service as part of their Open Data program.
    • +
  • IEM - A website that provides weather data and forecasts for Iowa and the world.
    • +
  • IMPROVER - A library of algorithms for meteorological post-processing and verification.
    • +
  • OpenCastKit - Open-source solutions of global data-driven high-resolution weather forecasting.
    • +
  • MSC AniMet - A simple tool enabling users to interact with MSC Open Data weather data and create custom weather animations for any area in the world.
    • +
  • ifk-smhi - Python interface to the Swedish Meteorological and Hydrological Institute, which provides climate and weather data.
    • +
  • riem - Allows to get weather data from ASOS airports stations all around the globe.
    • +
  • WRaINfo - A software for real-time weather radar data processing, specifically designed for X-band weather radars of FURUNO.
    • +
  • PyCINRAD - Decode CINRAD (China New Generation Weather Radar) data and visualize.
    • +
  • Py-ART - A data model driven interactive toolkit for working with weather radar data.
    • +
  • eccodes - The primary GRIB encoding/decoding package used at European Centre for Medium-Range Weather Forecasts used in meteorology to store historical and forecast weather data.
    • +
  • magics - Plotting package to visualise meteorological data in GRIB, NetCDF, BUFR and ODB format.
    • +
  • agweather-qaqc - Flexible, command-line-driven software to quality control daily weather data and then calculate reference evapotranspiration.
    • +
  • metloom - A python library created with the goal of consistent, simple sampling of meteorology and snow related point measurments from a variety of datasources across the Western US.
    • +
  • fmi-weather-client - Simple client library for fetching weather information from Finnish Meteorological Institute.
    • +
  • gridwxcomp - A package for comparing weather station data to gridded weather data that are hosted on Google Earth Engine.
    • +
  • Skyrim - Allows you to run any large weather model with a consumer grade GPU.
    • +
  • Neural-LAM - A repository of graph-based neural weather prediction models for Limited Area Modeling.
    • +
  • OPENSENSE software sandbox - Collection of runable examples with software packages for processing opportunistic rainfall sensors.
    • +
  • Prithvi WxC - Foundation model for weather and climate, that employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data.
    • +
  • CRA5 - Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer.
    • +
  • flux-data-qaqc - Provides a framework to create reproducible workflows for validation and analysis of eddy covariance data.
    • +
  • cloudcasting - Tooling and infrastructure to enable cloud nowcasting.
    • +
  • dwd_weather - Deutscher Wetterdienst integration for Home-Assistant.
    • +
  • WRFtailor - Tailoring and modification of the WRF model input data.
    • +
+

Radiative Transfer

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    +
  • lowtran - Atmospheric absorption extinction, scatter and irradiance model in Python and Matlab.
    • +
  • vSmartMOM.jl - A full end-to-end modular software suite for radiative transfer and related atmospheric analysis.
    • +
  • libRadtran - A collection of C and Fortran functions and programs for calculation of solar and thermal radiation in the Earth's atmosphere.
    • +
  • ARTS - A radiative transfer model for the millimeter and sub-millimeter spectral range.
    • +
  • Py6S - A Python interface to the 6S Radiative Transfer Model.
    • +
  • RTE+RRTMGP - A set of codes for computing radiative fluxes in planetary atmospheres.
    • +
  • LBLRTM - Line-By-Line Radiative Transfer Model is an accurate and efficient line-by-line radiative transfer model derived from the Fast Atmospheric Signature Code.
    • +
  • RRTMGP.jl - A Julia implementation of Rapid and accurate Radiative Transfer Model for General Circulation Models.
    • +
  • Eradiate - A next-generation radiative transfer model for Earth observation applications.
    • +
  • Juelich Rapid Spectral Simulation Code - The Juelich Rapid Spectral Simulation Code (JURASSIC) is a fast infrared radiative transfer model for the analysis of atmospheric remote sensing measurements.
    • +
  • Tenstream - A 3D radiative transfer solver for earth system models.
    • +
  • reflective-potential - An empirical analysis of Earth's annual-average surface reflectivity potential.
    • +
  • radis - A fast line-by-line code for high-resolution infrared molecular spectra.
    • +
  • pyOSOAA - A python interface for the Ocean Successive Orders with Atmosphere radiative transfer.
    • +
  • ECRAD - A radiation scheme suitable for use in atmospheric weather and climate models.
    • +
  • RT1 - A first order radiative transfer model for soil- and vegetation related parameter retrievals from radar-data.
    • +
  • WOUDC Data Registry - A platform that manages Ozone and Ultraviolet Radiation data in support of the World Ozone and Ultraviolet Radiation Data Centre (WOUDC), one of six World Data Centres as part of the Global Atmosphere Watch programme of the WMO.
    • +
+

Climate Change

+

Earth and Climate Modeling

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    +
  • SCREAM - A global atmosphere model targeted towards 3 km ("cloud resolving") resolution.
    • +
  • ESMValTool - A community diagnostic and performance metrics tool for routine evaluation of Earth system models in World Climate Research Programme.
    • +
  • The Flexible Modeling System - A software framework for supporting the efficient development, construction, execution, and scientific interpretation of atmospheric, oceanic, and climate system models.
    • +
  • global-workflow - Global Superstructure/Workflow currently supporting the Finite-Volume on a Cubed-Sphere Global Forecast System.
    • +
  • pangeo - A community platform for Big Data geoscience.
    • +
  • Pangeo Docker Images - The images defined in this repository capture reproducible computing environments used by Pangeo Cloud.
    • +
  • CMEPS - The Community Mediator for Earth Prediction Systems (CMEPS) is a NUOPC-compliant Mediator component used for coupling Earth system model component.
    • +
  • E3SM - A state-of-the-art fully coupled model of the Earth's climate including important biogeochemical and cryospheric processes.
    • +
  • E3SM Diagnostics Package - Diagnostics package constructed for supporting the diagnostics task of DOE's Energy Exascale Earth System Model (E3SM) project.
    • +
  • MDTF-diagnostics - Analysis framework and collection of process-oriented diagnostics for weather and climate simulations.
    • +
  • The Community Earth System Model - Composed of separate models simultaneously simulating the Earth's atmosphere, ocean, land, river run-off, land-ice, and sea-ice, plus one central coupler/moderator component, it allows researchers to conduct fundamental research into the Earth's past, present, and future climate states.
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  • CLIMA-Land - Everything within the Land model (Soil Plant Atmosphere Module, Land Hydrology, etc).
    • +
  • The Global Environmental Multiscale Model - An integrated forecasting and data assimilation system developed by the Atmospheric Numerical Prediction Research Section, Meteorological Research Division, of Environment and Climate Change Canada.
    • +
  • atlas - A library for numerical weather prediction and climate modeling.
    • +
  • hector - An open source, object-oriented, simple global climate carbon-cycle model.
    • +
  • pyhector - A Python interface for the simple global climate carbon-cycle model Hector.
    • +
  • NorESM - Norwegian Earth System Model and Documentation.
    • +
  • CliMT - A Toolkit for building Earth system models in Python.
    • +
  • climlab - A Python package for process-oriented climate modeling.
    • +
  • FAIR - Finite Amplitude Impulse-Response simple climate-carbon-cycle model.
    • +
  • MetSim - A meteorological simulator and forcing disaggregator for hydrologic modeling and climate applications.
    • +
  • ESMF - The Earth System Modeling Framework is a suite of software tools for developing high-performance, multi-component Earth science modeling applications.
    • +
  • RegESM - Regional Earth System Model is designed to be a state-of-art coupled modeling system that allows using variety of different earth system model components as well as their integration with co-processing tool.
    • +
  • MPAS - The Model for Prediction Across Scales is a collaborative project for developing atmosphere, ocean, and other earth-system simulation components for use in climate, regional climate, and weather studies.
    • +
  • pymt - A Python toolkit for running and coupling Earth surface models.
    • +
  • ClimateModels.jl - Uniform interface to climate models of varying complexity and completeness.
    • +
  • Pymagicc - A Python wrapper around the reduced complexity climate model.
    • +
  • DALES - Dutch Atmospheric Large-Eddy Simulation model.
    • +
  • Earthformer - A space-time Transformer for Earth system forecasting that applies Deep Learning for various Earth system forecasting task.
    • +
  • ClimaX - The first foundation model for weather and climate science.
    • +
  • MAPL - A foundation layer of the GEOS architecture, whose original purpose is to supplement the Earth System Modeling Framework.
    • +
  • MESMER - Stochastically creates Earth System Model-specific spatio-temporally correlated climate variable field realizations at a negligible computational cost.
    • +
  • CMIP6_CVs - Core Controlled Vocabularies for use in CMIP6.
    • +
  • earth2mip - A python framework that enables climate researchers and scientists to explore and experiment with AI models for weather and climate.
    • +
  • sedproxy - An R package for modeling sediment archived climate proxy records.
    • +
  • uDALES - Large-eddy-simulation software for urban flow, dispersion and microclimate modelling.
    • +
  • SUEWS - Surface Urban Energy and Water Balance Scheme.
    • +
  • Dragonfly for Grasshopper - Legacy dragonfly plugin for large-scale climate and urban heat island modeling.
    • +
  • Urban Weather Generator - A Python application for modeling the urban heat island effect.
    • +
  • pycontrails - Python library for modeling aviation climate impacts.
    • +
  • LCZ4r - An R Package for Local Climate Zones and Urban Heat Island Analysis.
    • +
  • PCMDI Metrics Package - Open-source Python package for Systematic Evaluation of Climate and Earth System Models.
    • +
  • swell - Used to generate experiments using the JEDI data assimilation system and NASA's Goddard Earth Observing System (GEOS) numerical weather forecast mode.
    • +
  • TSMP - An open source scale-consistent, highly modular, massively parallel regional Earth system model.
    • +
  • helios - An R package to process heating and cooling degrees for the Global Change Analysis Model (GCAM) from various sources such as WRF and CMIP.
    • +
  • Dinosaur - A spectral dynamical core for global atmospheric modeling written in JAX.
    • +
  • NeuralGCM - A package for building hybrid ML+Physics atmospheric models for weather and climate simulations.
    • +
  • FMS Coupler - A software framework for supporting the efficient development, construction, execution, and scientific interpretation of atmospheric, oceanic, and climate system models.
    • +
  • CABLE - A land surface model, used to calculate the fluxes of momentum, energy, water and carbon between the land surface and the atmosphere and to model the major biogeochemical cycles of the land ecosystem.
    • +
  • Earth2Studio - A Python-based package designed to get users up and running with AI weather and climate models fast. Our mission is to enable everyone to build, research and explore AI driven meteorology.
    • +
  • Scores - A Python package of mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models, primarily supporting the meteorological, climatological and geoscientific communities.
    • +
  • pyunicorn - An object-oriented Python package for advanced analysis and modelling of complex networks such as climate networks, a way of applying complex network theory to the climate system by assuming that each node represents a varying dynamical system.
    • +
  • CTSM - The Community Land Model is the land model for the Community Earth System Model, which formalises and quantifies concepts of ecological climatology.
    • +
  • ICON - A flexible, scalable, high-performance modelling framework for weather, climate and environmental prediction that provides actionable information for society and advances our understanding of the Earth's climate system.
    • +
  • RegCM - This Regional Climate Model can be applied to any region of the World, with grid spacing of up to about 3 km, and for a wide range of studies, from process studies to paleoclimate and future climate simulation.
    • +
  • gospl - Global Scalable Paleo Landscape Evolution Model is an library providing a scalable parallelised Python-based numerical model to simulate landscapes and basins reconstruction at global scale.
    • +
  • ClimODE - Models precise weather evolution with value-conserving dynamics, learning global weather transport as a neural flow, which also enables estimating the uncertainty in predictions.
    • +
  • ciceroscm - Python version of the CICERO-SCM simple climate model/emulator.
    • +
  • ChaosBench - A benchmark project to improve and extend the predictability range of deep weather emulators to the subseasonal-to-seasonal (S2S) scale.
    • +
+

Climate Data Standards

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    +
  • cf-conventions - The conventions for CF (Climate and Forecast) metadata are designed to promote the processing and sharing of files created with the NetCDF API.
    • +
  • cf-python - A CF-compliant Earth Science data analysis library.
    • +
  • CF Checker - The CF Checker is a utility that checks the contents of a NetCDF file complies with the Climate and Forecasts (CF) Metadata Convention.
    • +
  • netcdf-fortran - The Unidata network Common Data Form (netCDF) in Fortran is an interface for scientific data access and a set of freely-distributed software libraries that provide an implementation of the interface.
    • +
  • netcdf-c - The Unidata network Common Data Form (netCDF) in C is an interface for scientific data access and a set of freely-distributed software libraries that provide an implementation of the interface.
    • +
  • netcdf-python - The Unidata network Common Data Form (netCDF) in Python is an interface for scientific data access and a set of freely-distributed software libraries that provide an implementation of the interface.
    • +
  • Climate Model Output Rewriter - Produce CF-compliant netCDF files that fulfill the requirements of many of the climate community's standard model experiments.
    • +
  • h5netcdf - A Python interface for the netCDF4 file format that reads and writes local or remote HDF5 files directly via h5py or h5pyd, without relying on the Unidata netCDF library.
    • +
  • xarray - An open source project and Python package that introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, which allows for more intuitive, more concise, and less error-prone user experience.
    • +
  • cf-xarray - An accessor for xarray objects that interprets CF attributes.
    • +
+

Climate Data Access and Visualization

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    +
  • CMIP6 Datasets - Provides an access to the outputs of the current phase 6 of the Coupled Model Intercomparison Project (CMIP). CMIP6 global climate models give a base to (almost) all we may know for the climate of the future.
    • +
  • climatedataguide - Search and access 212 datasets covering the Atmosphere, Ocean, Land and more. Explore climate indices, reanalyses and satellite data and understand their application to climate model metrics.
    • +
  • rnoaa - An R interface to many National Oceanic and Atmospheric Administration data sources.
    • +
  • Meteostat Python - Access and analyze historical weather and climate data with Python.
    • +
  • loadeR - An R package for climate data access building on the NetCDF-Java API.
    • +
  • easyclimate - Easy access to high-resolution daily climate data for Europe.
    • +
  • GSODR - Global Surface Summary of the Day Weather Data Client for R.
    • +
  • IPCC AR6 Chapter 9 Figures - Code and data for all figures from Chapter 9 of the IPCC AR6.
    • +
  • ATLAS - Datasets, code and virtual workspace for the Climate Change ATLAS.
    • +
  • pylandtemp - Global land surface temperature and emissivity from NASA's Landsat satellite images.
    • +
  • cdsapi - Python API to access the Copernicus Climate Data Store.
    • +
  • PRISM - Download data from the Oregon PRISM climate data project.
    • +
  • climaemet - An interface to download the climatic data of the Spanish Meteorological Agency directly from R using their API and create scientific graphs.
    • +
  • Climate Data Store Toolbox - Dive into this wealth of information about the Earth's past, present and future climate.
    • +
  • chirps - A quasi-global high-resolution rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.
    • +
  • ClimateSERVpy - Enables access to the ClimateSERV API where many types of climate data can be subset by area of interest, and time range, then either downloaded as tif, or NetCDf files, or the data can be statistically processed to render json values or csv.
    • +
  • ecmwfr - Provides easy access to the European Centre for Medium-Range Weather Forecasts web API services and Copernicus Climate Data Store from within R, matching and expanding upon the ECMWF Python tools.
    • +
  • hockeystick - Make essential Climate Change datasets easily available to non-climate experts.
    • +
  • CliFlo - Easily download and visualise climate data from New Zealand's National Climate Database.
    • +
  • rWBclimate - An R interface for the World Bank climate data used in the World Bank climate knowledge portal.
    • +
  • IceVarFigs - Weather and climate graphics for science communication.
    • +
  • ClimateSatellite.jl - Julia package that downloads measurements and observational of climate satellite mission data.
    • +
  • ClimateSERV - This web application allows development practitioners, scientists/researchers, and government decision-makers to visualize and download historical rainfall data, vegetation condition data, and 180-day forecasts of rainfall and temperature to improve understanding of, and make improved decisions for, issues related to agriculture and water availability.
    • +
  • canadaHCD - Access Canadian Historical Climate Data from R.
    • +
  • pygac - A Python package to read and calibrate NOAA and Metop AVHRR GAC and LAC data.
    • +
  • Cate - A software for ingesting, operating on and visualizing all ESA Climate Change Initiative data.
    • +
  • climetlab - A Python package aiming at simplifying access to climate and meteorological datasets, allowing users to focus on science instead of technical issues such as data access and data formats.
    • +
  • rsoi - An R package to download the most up to date climate indices.
    • +
  • Fluid Earth - Open-source Visualization of Weather and Climate Data.
    • +
  • climateR - An R package for getting point and gridded climate data by AOI.
    • +
  • Climate Data Store Toolbox for MATLAB - This toolbox allows you to easily access data and download hundreds of data sets associated with climate change for analysis in MATLAB.
    • +
  • ClimateLearn - A Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way.
    • +
  • ForestGEO - This public repository is a portal for climate data and information for ForestGEO sites.
    • +
  • Analysis-Ready, Cloud Optimized ERA5 - A global history of the climate highly accessible in the cloud.
    • +
  • CleF - Searches the Earth System Grid Federation datasets stored at the Australian National Computational Infrastructure, both data published on the NCI ESGF node as well as files that are locally replicated from other ESGF nodes.
    • +
  • Intake-esm - An intake plugin for parsing an Earth System Model catalog and loading assets into xarray datasets.
    • +
  • GHCNData.jl - Helps access the Global Historical Climatological Network's daily data.
    • +
  • get-station-data - A set of Python tools to make it easier to extract weather station data (e.g., temperature, precipitation) from the Global Historical Climatology Network Daily.
    • +
  • OGD@MeteoSwiss - Open Data products released by the Federal Office of Meteorology and Climatology MeteoSwiss.
    • +
  • ESGF PyClient - A Python package designed for interacting with the Earth System Grid Federation system.
    • +
  • ecmwf-opendata - A package to simplify the download of ECMWF open data.
    • +
  • ACCESS-NRI Intake Catalog - Aims to provide a way for Python users to discover and load data across a broad range of climate data products available on the Australian NCI supercomputer Gadi.
    • +
  • TubeDB - Efficient and easy to use database for climate station observations. Store, check, fill, aggregate, visualize and access your time series data on-demand.
    • +
  • climate-visuals - These graphics show different aspects of how the climate is changing.
    • +
  • ColOpenData - A package that acquires and wrangles Colombian socioeconomic, geospatial and climate data.
    • +
  • amadeus - A mechanism for data, environments, and user setup for common environmental and climate health datasets in R.
    • +
  • HadCRUT5 - Visualize the HadCRUT5 temperature, a gridded dataset of global historical surface temperature anomalies relative to a 1961-1990 reference period.
    • +
  • Climate Foresight - A chatbot that answers questions about climate change impacts on planned human activities.
    • +
  • urban-heat - Interactive urban heat island effect visualizations for European cities.
    • +
+

Climate Data Processing and Analysis

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    +
  • Iris - A powerful, format-agnostic, community-driven Python package for analyzing and visualizing Earth science data.
    • +
  • xCDAT - An extension of xarray for climate data analysis on structured grids.
    • +
  • Geoclimate - Geospatial processing toolbox for environmental and climate studies.
    • +
  • weather-tools - A series of command-line tools to make common data engineering tasks easier for researchers in climate and weather.
    • +
  • OpenClimateGIS - A set of geoprocessing and calculation tools for CF-compliant climate datasets.
    • +
  • AR6-WG1-Data-Compilation - Compiles the data into a uniform, csv-based data format following the standard established by the Integrated Assessment Modeling Consortium and used by IPCC WG3.
    • +
  • icclim - Python library for climate indices and climate indicators calculation.
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  • clisops - Climate simulation operations.
    • +
  • hyfo - Mainly focuses on data process and visualization in hydrology and climate forecasting.
    • +
  • aospy - A Python package for automated analysis and management of gridded climate data.
    • +
  • NASAaccess - An R package that can generate gridded ASCII tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g. SWAT, VIC, RHESSys).
    • +
  • NCL - The NCAR Command Language is a scripting language for the analysis and visualization of climate and weather data.
    • +
  • xskillscore - An open source project and Python package that provides verification metrics of deterministic (and probabilistic from properscoring) forecasts with xarray.
    • +
  • getCRUCLdata - Provides functions that automate importing CRU CL v. 2.0 climatology data into R.
    • +
  • cfgrib - A Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes.
    • +
  • epwshiftr - Create future EnergyPlus Weather files using CMIP6 data.
    • +
  • NCO - Manipulates and analyzes data stored in netCDF-accessible formats.
    • +
  • pynco - Contains the module python nco, which implements a Python style access to the NetCDF Operators (NCO).
    • +
  • semanticClimate - Conversion of IPCC documents into semantic form.
    • +
  • Miranda - A modern Python utility library for climate data collection and management.
    • +
  • Paleoclimate - Analysis of Paleoclimate Data.
    • +
  • GT4Py - Library for generating high-performance implementations of stencil kernels for weather and climate modeling from a domain-specific language.
    • +
  • ClimoPy - A succinct toolset for analyzing climate data.
    • +
  • tempestextremes - Extreme weather detection and characterization.
    • +
  • xmip - Analysis ready CMIP6 data in python the easy way with pangeo tools.
    • +
  • daymetr - Allows for easy downloads of Daymet climate data directly to your R workspace or your computer.
    • +
  • ClimSim - An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.
    • +
  • CLIMaCCF - Provide an open-source, easy-to-use, and flexible software tool that efficiently calculates spatially and temporally resolved climate impact of aviation emissions by using algorithmic climate change functions.
    • +
  • geomet-climate - Geospatial web services for Canadian climate data.
    • +
  • IPCC-WG1-Code - Code and Data of the Physical Science Basis Working Group (WG1) as part of Intergovernmental Panel on Climate Change (IPCC).
    • +
  • gecevar - Contains functions for downloading climate and environmental data from open and free online datasets for any region of the world.
    • +
  • weatherOz - Aims to facilitate access and download weather and climate data for Australia from Australian data sources.
    • +
  • Climate Data Rescue - This application seeks to aid in the digitization of paper records through the crowd-sourced transcription of scanned image files, particularly documents ill-suited for Optical Character Recognition.
    • +
  • Finch - A Web Processing Service for Climate Indicators.
    • +
  • era5cli - Command Line Interface to download ERA5 from Copernicus Climate Data Service.
    • +
  • Sacpy - An effecient Statistical Analysis tool (e.g. regression, EOF, MCA) for Climate and Meteorology data.
    • +
  • s2spy - A high-level python package integrating expert knowledge and artificial intelligence to boost (sub) seasonal forecasting.
    • +
  • xscen - A climate change scenario-building analysis framework, built with Intake-esm catalogs and xarray-based packages such as xclim and xESMF.
    • +
  • w2w - A WUDAPT-to-WRF python tool that injects World Urban Database and Access Portal Tools (WUDAPT)'s Local Climate Zone information into WRF.
    • +
  • Climate Tool - A tool for connecting together scientific information across climate, health and policy in the UK.
    • +
  • freva - A data search and analysis platform developed by the atmospheric science community.
    • +
  • Climate_Indices - Contains Python implementations of various climate index algorithms which provide a geographical and temporal picture of the severity of precipitation and temperature anomalies useful for climate monitoring and research.
    • +
  • XCast - A free and open source climate forecasting toolkit written by Kyle Hall & Nachiketa Acharya, designed to help forecasters and earth scientists apply state-of-the-art postprocessing techniques to gridded data sets.
    • +
  • GDESS - Tool for evaluating atmospheric carbon dioxide concentrations as simulated by Earth system models.
    • +
  • ClimateBase.jl - A Julia package offering basic functionality for analyzing data that are typically in the form used by climate sciences.
    • +
  • ClimateTools.jl - A collection of commonly-used tools in climate science. Basics of climate field analysis are covered, with some forays into exploratory techniques associated with climate scenarios design.
    • +
  • xgcm - A Python package for analyzing general circulation model output data.
    • +
  • MPAS-Analysis - Analysis for simulations produced with Model for Prediction Across Scales (MPAS) components and the Energy Exascale Earth System Model (E3SM), which used those components.
    • +
  • hn2016_falwa - A Python library for computing Finite-Amplitude Local Wave Activity from climate data.
    • +
  • climpred - Aims to be the primary package used to analyze output from initialized dynamical forecast models, ranging from short-term weather forecasts to long-term climate forecasts.
    • +
  • xclim - A library of derived climate variables, i.e. climate indicators, based on xarray.
    • +
  • climpact - Calculate the ET-SCI climate extremes indices.
    • +
  • CoPro - A machine-learning tool for conflict risk projections based on climate, environmental, and societal drivers.
    • +
  • python-cmethods - Serves as a collection of different scale- and distribution-based bias correction techniques for climatic research.
    • +
  • Climakitae - Enable a transition from climate vulnerability assessments to decision support for investor-owned utilities and other stakeholders.
    • +
  • xeofs - A dedicated Python package for dimensionality reduction in the realm of climate science, offering methods like PCA, known as EOF analysis within the field, and related variants.
    • +
  • clim-recal - A specialized resource designed to tackle systematic errors or biases in Regional Climate Models.
    • +
  • ibicus - Flexible and user-friendly toolkit for the bias correction of climate models and associated evaluation.
    • +
  • agroclimatico - A set of functions for calculating hydrological climate indices and statistics from tidy data, including a function for plotting geo-referenced results and mapping information.
    • +
  • CRAI - Software to train/evaluate models to reconstruct missing values in climate data (e.g., HadCRUT4) based on a U-Net with partial convolutions.
    • +
  • chopin - Computation for Climate and Health research On Parallelized infrastructure is a Scalable GIS methods for environmental and climate data analysis.
    • +
  • uxarray - Xarray extension for unstructured climate and global weather data analysis and visualization.
    • +
  • CHAP - Designed to be used by researchers and public health professionals to forecast and assess the impact of climate on health outcomes.
    • +
  • ccviR - A rapid assessment tool designed to allow a relative grouping of unrelated taxa by vulnerability to climate change and to highlight which factors contribute to the climate change vulnerability of individual species or groups of taxa.
    • +
  • Climate Data Operators - A collection of command line Operators to manipulate and analyse Climate and NWP model Data.
    • +
  • index_calculator - Calculate climate indicators with standardized project-specific attributes.
    • +
  • CLIVAR ENSO Metrics - CLIVAR ENSO Metrics package proposes metric collections addressing science questions about the evaluation of El Niño-Southern Oscillation in climate models.
    • +
+

Climate Downscaling

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  • ICAR - A simplified atmospheric model designed primarily for climate downscaling, atmospheric sensitivity tests, and hopefully educational uses.
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  • downscaleR - An R package for empirical-statistical downscaling focusing on daily data and covering the most popular approaches (bias correction, Model Output Statistics, Perfect Prognosis) and techniques (e.g. quantile mapping, regression, analogs, neural networks).
    • +
  • KrigR - An R Package for downloading, preprocessing, and statistical downscaling of the European ERA5 dataset.
    • +
  • scikit-downscale - Statistical downscaling and postprocessing models for climate and weather model simulations.
    • +
  • TopoPyScale - Perform topography-based downscaling of climate data to the hillslope scale.
    • +
  • ClimDown - A Climate Downscaling package for the R statistical programming language.
    • +
  • esd - Designed for climate and weather data analysis, empirical-statistical downscaling, and visualisation.
    • +
  • cmip6-downscaling - Climate downscaling using CMIP6 data.
    • +
  • climate4R - A bundle of R packages for transparent climate data access, post-processing (including data collocation and bias correction / downscaling) and visualization.
    • +
  • downscaleCMIP6 - Global Downscaled Projections for Climate Impacts Research.
    • +
  • PyESD - Downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets to point scale.
    • +
  • DeepSensor - A Python package for tackling diverse environmental prediction tasks with neural processes.
    • +
  • DeepR - Global reanalysis downscaling to regional scales by means of deep learning techniques.
    • +
+

Natural Hazard and Storm

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    +
  • CLIMADA - Stands for CLIMate ADAptation and is a probabilistic natural catastrophe impact model, that also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioural, etc.).
    • +
  • InaSAFE - QGIS plugin for estimating impact from natural disasters.
    • +
  • PRISM - Assesses the potential risk and forecasts the impact of climate hazards on the most vulnerable communities, in order to design risk reduction activities and target disaster responses.
    • +
  • Flood Mapping - Flood forecasting via the National Water Model.
    • +
  • GLOCOFFS - An ADCIRC-based global storm tide modeling system providing real-time forecasts of coastal flooding.
    • +
  • ML4Floods - An ecosystem of data, models and code pipelines to tackle flooding with machine learning.
    • +
  • CaMa - Designed to simulate the hydrodynamics in continental-scale rivers.
    • +
  • Global Flood Database Scripts & Data - Used to produce the Global Flood Database and assess changes in population exposed to floods.
    • +
  • LISFLOOD - A spatially distributed water resources model, developed by the Joint Research Centre of the European Commission since 1997.
    • +
  • CanFlood - Flood Risk modelling toolbox for Canada.
    • +
  • HYDRAFloods - An open source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data.
    • +
  • OasisLMF - An open source catastrophe modelling platform, free to use by anyone.
    • +
  • CAMS - Critical Asset Management and Disaster Resilience for Islands, Cities, and Communities.
    • +
  • StormEvents - Provides Python interfaces for observational data surrounding named storm events.
    • +
  • Hagelslag - An object-based severe storm forecasting system that utilizing image processing and machine learning tools to derive calibrated probabilities of severe hazards from convection-allowing numerical weather prediction model output.
    • +
  • HAFS - Hurricane Analysis and Forecast System.
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  • rrricanes - An R library that extracts information from Atlantic and east Pacific hurricanes and tropical storms.
    • +
  • FrEDI - Estimates economic damages and impacts from climate change and sea level rise through the 21st century.
    • +
  • FACTS - Repository for the Framework for Accessing Changes To Sea-level.
    • +
  • FLOODPY - A free and open-source python toolbox for mapping of floodwater.
    • +
  • Tropical Cyclone Risk Model - A statistical-parametric model for assessing wind hazard from tropical cyclones.
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  • OpenQuake - Allows users to compute seismic hazard and seismic risk of earthquakes on a global scale.
    • +
  • OasisPiWind - PiWind is a wind storm model for a small area of the UK.
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  • ThinkHazard! - Provides a general view of the hazards, for a given location, that should be considered in project design and implementation to promote disaster and climate resilience.
    • +
  • Tropycal - A Python package intended to simplify the process of retrieving and analyzing tropical cyclone data, both for past storms and in real time.
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  • tcpyPI - A set of scripts and notebooks that compute and validate tropical cyclone (TC) potential intensity (PI) calculations in Python.
    • +
  • SFINCS - Super-fast modelling of dynamic compound flooding in Coastal Systems.
    • +
  • RAS2FIM - Creation of flood inundation raster libraries and rating curves from HEC-RAS models.
    • +
  • BG_Flood - The model is designed to simulate flood/inundation caused by tsunami, river, rain, tide or storm surge (from wind stress or atm pressure differential) or any combination of these.
    • +
  • TATHU - A Python package for tracking and analyzing the life cycle of thunderstorms.
    • +
  • Risk Data Library Standard - Provides a common description of the data used and produced in risk assessments, including hazard, exposure, vulnerability, and modelled loss, or impact, data.
    • +
  • Global Flood Monitor - A global database of historic and real-time flood events based on social media.
    • +
  • ASGS - Provides software infrastructure for automating coastal ocean modelling for real time hurricane decision support.
    • +
  • StormR - An R package allowing to easily extract storm track data for given locations or areas of interests, to generate wind speed and direction fields, and to compute summary statistics characterising the behaviour of winds generated by tropical storms and cyclones.
    • +
  • TREC - Allows users to efficiently assess the climate risk for transit stations within the context of the access it provides to vital services and regions.
    • +
  • TECA - The Toolkit for Extreme Climate Analysis, contains a collection of climate anlysis algorithms targetted at extreme event detection and analysis.
    • +
  • Inundation Mapping - Flood inundation mapping and evaluation software configured to work with U.S. National Water Model.
    • +
  • Intensity duration frequency analysis - Reads the measurement data of the rainfall and calculates the distribution of the rainfall as a function of the return period and the duration for duration steps up to 12 hours.
    • +
  • FwDET - Calculates floodwater depths using a digital elevation model and a flood extent polygon.
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  • RICorDE - Calculates floodwater depths using a digital elevation model and a flood extent polygon.
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  • FloodAdapt - A decision-support tool that seeks to advance and accelerate flooding-related adaptation planning.
    • +
  • Infrastructure Risk Visualisation Tool - Aims to support climate adaptation decision-making by identifying spatial vulnerabilities and risks under current and future climate scenarios.
    • +
  • Open Global Infrastructure Risk/Resilience Analysis - Can be used to analyse environmental risks to infrastructure networks using global open data.
    • +
  • TropiDash - A visualization tool designed to empower researchers, meteorologists, and enthusiasts with the ability to analyze and interpret tropical cyclone data effectively.
    • +
  • ADCIRC - A system of computer programs for solving time-dependent, free-surface circulation and transport problems in two and three dimensions in applications such as storm surge and flood prediction.
    • +
  • DamageScanner - A python toolkit for direct damage assessments for natural hazards.
    • +
  • Climate & Disaster Risk Screening Tools - A collection of scripted tools developed to inform risk analytics for the World Bank's Country Climate and Development Report risk screening activities.
    • +
  • Kuro Siwo - A meticulously curated multi-temporal dataset spanning 32 global flood events, mapping over 63 billion areas of land.
    • +
  • Climate Risk Assessment Book - Guides you through your regional Climate Risk Assessments and provides you with tools and datasets for the assessment.
    • +
  • FReDT - Provide a better understanding of the degree of impact flood events can have on physical assets like buildings, roads, railways, transmission lines, etc.
    • +
  • SynxFlow - Dynamically simulate flood inundation, landslides runout and debris flows using multiple CUDA-enabled GPUs.
    • +
  • Humanitarian OpenStreetMap - Provide map data which revolutionises disaster management, reduces risks, and contributes to achievement of the Sustainable Development Goals.
    • +
  • FLEXTH - Flood extent enhancement and water depth estimation tool for satellite-derived inundation maps.
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Integrated Assessment and Climate Policy

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  • PRIMAP2 - The next generation climate policy analysis suite.
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  • Climate Policy Radar Methodology - Climate Policy Radar and Climate Change Laws of the World methodology documents.
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  • MESSAGEix - A versatile, dynamic systems-optimization modeling framework developed by the IIASA Energy, Climate, and Environment (ECE) Program since the 1980s.
    • +
  • DICE.jl - The Dynamic Integrated model of Climate and the Economy model family are a popular and capable type of simple Integrated Assessment Model of climate change economics pioneered by William Nordhaus.
    • +
  • LPJmL - Designed to simulate the global terrestrial carbon cycle and the response of carbon and vegetation patterns under climate change.
    • +
  • ENGAGE - Source code for figure generation and analysis of the ENGAGE netzero scenario analysis.
    • +
  • pyam - A Python package for data-wrangling, analysis and visualization of integrated-assessment scenarios and energy systems modeling results.
    • +
  • GCAM - A dynamic-recursive model with technology-rich representations of the economy, energy sector, land use and water linked to a climate model that can be used to explore climate change mitigation policies including carbon taxes, carbon trading, regulations and accelerated deployment of energy technology.
    • +
  • EXIOMOD - A macro-economic model that predicts the effects of public authority measures, shifts in consumer behaviour and associated enviromnetal impacts.
    • +
  • remind - A numerical model that represents the future evolution of the world economies with a special focus on the development of the energy sector and the implications for our world climate.
    • +
  • mrremind - MadRat REMIND Input Data Package.
    • +
  • edgeTransport - Prepare EDGE Transport Data for the REMIND model.
    • +
  • META 2021 - Advanced integrated assessment model (SC-IAM), designed as a model-based meta-analysis of the effects of tipping points on the social cost of carbon.
    • +
  • WorldDynamics.jl - Aims to provide a modern framework to investigate integrated assessment models of sustainable development, based on current software engineering and scientific machine learning techniques.
    • +
  • ipcc_sr15_scenario_analysis - Scenario analysis notebooks for the IPCC Special Report on Global Warming of 1.5°C.
    • +
  • rwlts - Support governments in making decisions about the impact of human activities on the environment, planning the use of natural resources, conserving biodiversity and monitoring climate change.
    • +
  • AI for Global Climate Cooperation - Modeling global cooperation in the RICE-N Integrated Assessment Model.
    • +
  • Future Technology Transformation - Integrated assessment model with a realistic treatment of technology diffusion.
    • +
  • SDEverywhere - The framework for system dynamics models created for the global climate simulator En-ROADS, the climate policy simulator C-ROADS and the Energy Policy Simulator.
    • +
  • GCAM-China - The Global Change Analysis Model with China provincial-level details.
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Natural Resources

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Air Quality

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  • CAMx - A multi-scale photochemical modeling system for gas and particulate air pollution.
    • +
  • CMAQ - Code base for the U.S. EPA's Community Multiscale Air Quality Model.
    • +
  • AMET - A suite of software designed to facilitate the analysis and evaluation of predictions from meteorological and air quality models.
    • +
  • MLAir - Machine Learning on Air data is an environment that simplifies and accelerates the creation of new machine learning models for the analysis and forecasting of meteorological and air quality time-series.
    • +
  • shoot-i-smoke - See your city's air pollution measured in daily cigarettes.
    • +
  • airqmon - A macOS menu bar application that displays live air quality data from the nearest sensor station.
    • +
  • AirCasting - The project aims to build a platform for gathering, visualization and sharing of environmental data.
    • +
  • openair - An R package developed for the purpose of analyzing air quality data — or more generally atmospheric composition data.
    • +
  • openairmaps - Combine the robust analytical methods found in openair with the highly capable leaflet package.
    • +
  • airQualityMeter - Detects air particulate matter (PM - pm1, pm2.5, pm10) concentrations and sends data to an MQTT server.
    • +
  • OpenAQ API Version 2 - A web-accessible API that provides endpoints to query the real-time and historical air quality measurements on the platform.
    • +
  • OpenAQ Data Ingest Pipeline - A tool to collect data for OpenAQ platform.
    • +
  • SMOKE - Create emissions inputs for multiple air quality modeling systems with unmatched speed and flexibility.
    • +
  • inmap - A multi-scale emissions-to-health impact model for fine particulate matter (PM2.5) that mechanistically evaluates air quality and health benefits of perturbations to baseline emissions.
    • +
  • Open Source EMEP MSC-W model - Designed to calculate air concentrations and deposition fields for major acidifying and eutrophying pollutants, photo-oxidants and particulate matter.
    • +
  • Rpollution - The goal of Rpollution is to assemble R functions to analyze air pollution data.
    • +
  • YETI - A bottom-up traffic emission calculation tool developed at the Institute for Advanced Sustainability Studies in Potsdam and built in Python.
    • +
  • PM2.5-GNN - A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting.
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  • rdefra - Interact with the UK AIR Pollution Database from DEFRA.
    • +
  • rmweather - An R package to conduct meteorological/weather normalisation on air quality so trends and interventions can be investigated in a robust way.
    • +
  • Safecast - Building out a network of sensors to monitor particulate matter in sizes PM1.0, PM2.5, PM10.
    • +
  • West Oakland Air Quality Project - A platform for impacted communities to understand their local air quality and advocate for environmental justice.
    • +
  • Sensor.Community - Making the world a better place through community driven, open environmental data.
    • +
  • openSenseMap - Wikipedia for environmental data in order to awaken and promote education, environmental and climate protection.
    • +
  • rfasst - Estimation of a consistent range of adverse health and agricultural effects attributable to air pollution for a GCAM scenario.
    • +
  • saqgetr - Import Air Quality Monitoring Data in a Fast and Easy Way.
    • +
  • Ozone3 - An open-source package to easily obtain real-time, historical, or forecasted air quality data for anywhere in the world.
    • +
  • RAQSAPI - Retrieve data from the United States Environmental Protection Agency's Air Quality Systems.
    • +
  • qualR - Download of air pollutants and meteorological information from CETESB QUALAR System for São Paulo, and MonitorAr Program, for Rio de Janeiro.
    • +
  • AirQo - Develop hardware and software tools to help deliver Clean Air for All African Cities.
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  • AirMonitor - Utilities for working with hourly air quality monitoring data with a focus on small particulates PM2.5.
    • +
  • BEETHOVEN - Building an extensible, reproducible, test-driven, harmonised, open-source, versioned, ensemble model for air quality.
    • +
  • CanAirIO - A citizen science project using mobile and static sensors to measure air quality with cell phones and low-cost technology.
    • +
  • NAPS Data Analysis Toolbox - Canada National Air Pollution Surveillance Program data downloader, importer, extractor, analysis, and visualization toolbox.
    • +
  • AirBase - An easy downloader for air quality data provided by the European Environment Agency.
    • +
  • Air Quality Forecast - This end-to-end machine learning collaboration project is used to predict ozone and nitrogen dioxide levels in Utrecht as a form of timeseries regression.
    • +
  • FHWA's DANA Tool - The Database for Air Quality and Noise Analysis (DANA) Tool provides traffic-related inputs to the Motor Vehicle Emission Simulator (MOVES) vehicle emissions model and the Traffic Noise Model (TNM).
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+

Water Supply and Quality

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  • Stormwater-Management-Model - Used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas.
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  • dataRetrieval - This R package is designed to obtain USGS or EPA water quality sample data, streamflow data and metadata directly from web services.
    • +
  • EGRET - An R package for the analysis of long-term changes in water quality and streamflow, including the water-quality method Weighted Regressions on Time, Discharge, and Season.
    • +
  • WaterModels.jl - Designed to enable computational evaluation of historical and emerging water network formulations and algorithms using a common platform.
    • +
  • Tree-based Inland Hydraulic Routing Project - The program under development here seeks to effectively manage the traversal of a network of streams with defined hydraulic properties specifically for the purpose of hydraulic routing in an operational flood and water resources forecasting system.
    • +
  • Next Gen Water Modeling Framework - This framework includes an encapsulation strategy which focuses on the hydrologic data first, and then builds a functional abstraction of hydrologic behavior.
    • +
  • nhdplusTools - This package is a growing set of tools for manipulation of hydrographic data using the NHDPlus data model.
    • +
  • The Safe Water Project - A team of volunteers at Code for Boston who are using data modeling, data visualization and machine learning to predict, visualize, and share data about the presence of hazardous drinking and surface water contaminants in the United States.
    • +
  • HydroFunctions - A suite of convenience functions for working with hydrology data in an interactive Python session.
    • +
  • The General Lake Model - A water balance and one-dimensional vertical stratification hydrodynamic model.
    • +
  • nhdR - An R interface to the US National Hydrography Dataset.
    • +
  • modflow6 - Has been widely used by academics, private consultants, and government scientists to accurately, reliably, and efficiently simulate groundwater flow.
    • +
  • iMOD - An easy to use Graphical User Interface + an accelerated Deltares-version of MODFLOW with fast, flexible and consistent sub-domain modeling techniques.
    • +
  • pysheds - Simple and fast watershed delineation in Python.
    • +
  • WhiteboxTools - WhiteboxTools is an advanced geospatial data analysis platform.
    • +
  • Water Network Tool for Resilience - A Python package designed to simulate and analyze resilience of water distribution networks.
    • +
  • fasstr - An R package to analyze, summarize, and visualize daily streamflow data.
    • +
  • KnowFlow - KnowFlow Automatic Water Monitoring device is an open source tool enabling everyone to have access to first hand water quality data with low cost.
    • +
  • gwells - Groundwater Wells and Aquifers application for the Ministry of Environment in British Columbia.
    • +
  • waterquality - A package designed to detect and quantify water quality and cyanobacterial harmful algal bloom (CHABs) from remotely sensed imagery.
    • +
  • QGEP - A waste-water application based on QGIS in compliance with SIA-405.
    • +
  • PooPyLab_Project - A biological wastewater treatment software.
    • +
  • aguaclara - A Python package for designing and performing research on AguaClara water treatment plants.
    • +
  • RTC-Tools - A toolbox for control and optimization of water systems.
    • +
  • EPANET - The Water Distribution System Hydraulic and Water Quality Analysis Toolkit.
    • +
  • EPANET-RTX Library - The real-time extension to the EPANET Hydraulic Toolkit.
    • +
  • EPyT - A Common programming framework for research and development in the growing field of smart water networks.
    • +
  • pyswmm - Python Wrapper for Stormwater Management Model.
    • +
  • SWMM5plus - Prototype Fortran 2008 engine for the EPA Storm Water Management Model.
    • +
  • pystorms - Simulation Sandbox for the Design and Evaluation of Stormwater Control Algorithms.
    • +
  • SELEN - An open source Fortran code for the numerical solution of the Sea Level Equation for a spherical, layered, rotating earth with viscoelastic rheology.
    • +
  • rivamap - An Automated River Analysis and Mapping Engine.
    • +
  • AWSM - Automated Water Supply Model was developed at the USDA Agricultural Research Service.
    • +
  • AED2 - A community-driven library of modules and algorithms for simulation of "aquatic ecodynamics": water quality, aquatic biogeochemsitry, biotic habitat and aquatic ecosystem dynamics.
    • +
  • DORiE - A Discontinuous Galerkin Solver for Soil Water Flow and Passive Solute Transport Based on DUNE.
    • +
  • wqbc - An R package for water quality thresholds and index calculation for British Columbia.
    • +
  • Model My Watershed - A watershed-modeling web app for citizens, conservation practitioners, municipal decision-makers, educators, and students.
    • +
  • AquaSat - A data set to enable remote sensing of water quality for inland waters.
    • +
  • CDE - Facilitates searching and download of the Water Framework Directive reporting data for all waterbodies from the EA website.
    • +
  • pyGSFLOW - A set of Python modules to run the GSFLOW integrated hydrologic model program.
    • +
  • bcgroundwater - An R package to facilitate analysis and visualization of groundwater data from the British Columbia groundwater observation well network.
    • +
  • GSFLOW-GRASS - Generates inputs for and runs the coupled groundwater-surface water model "GSFLOW".
    • +
  • GWHAT - A free, open source, and cross-platform interactive computer program whose main focus is the interpretation of observation well hydrographs.
    • +
  • Soil-Water-Balance - A Modified Thornthwaite-Mather Soil-Water-Balance Code for Estimating Groundwater Recharge.
    • +
  • Long-term Trends in Groundwater Levels in B.C. - R scripts for an indicator on long-term trends in groundwater levels in British Columbia published on Environmental Reporting British Columbia.
    • +
  • GSFLOW - A coupled Groundwater and Surface-water FLOW model based on the integration of the USGS Precipitation-Runoff Modeling System and the USGS Modular Groundwater Flow Model.
    • +
  • PEST++ - Software suite aimed at supporting complex numerical models in the context of decision support, with a focus on supporting environmental models like groundwater or surface water.
    • +
  • canwqdata - An R package to download open water quality data from Environment and Climate Change Canada's National Long-term Water Quality Monitoring Data.
    • +
  • HASP - Hydrologic AnalySis Package.
    • +
  • CSHShydRology - This is a collection of R functions used by the Canadian Association Society for Hydrological Sciences.
    • +
  • Liquid Prep - Offers an end-to-end solution for farmers looking to optimize their water usage; especially during times of drought.
    • +
  • flopy - A Python package to create, run, and post-process MODFLOW-based models.
    • +
  • PCRaster - A collection of tools and software libraries tailored to the construction of spatio-temporal environmental models.
    • +
  • dataretrieval-python - A Python alternative to USGS-R's dataRetrieval package for obtaining USGS or EPA water quality data, streamflow data, and metadata directly from web services.
    • +
  • CWatM - Assess water supply, water demand and environmental needs at global and regional level.
    • +
  • WWTP - EU28 Waste Water Treatment Plants.
    • +
  • AWH-Geo - Tool and calculations for Global Potential of Harvesting Drinking Water from Air using Solar Energy.
    • +
  • pySTEPS - An open-source and community-driven Python library for probabilistic precipitation nowcasting, i.e. short-term ensemble prediction systems.
    • +
  • reservatoriosBR - R package for Brazilian reservoirs data.
    • +
  • PyForecast - A statistical modeling tool used by Reclamation water managers and reservoir operators to train and build predictive models for seasonal inflows and streamflows.
    • +
  • pyWaPOR - Data manual and Algorithm to compute the evapotranspiration of large areas using remote sensing data.
    • +
  • HydroSHEDS - Seamless hydrographic data for global and regional applications.
    • +
  • OpenET - Provides Google Earth Engine Python API based implementation of the SSEBop ET model used within OpenET.
    • +
  • SPEI - Computing the Standardized Precipitation-Evapotranspiration Index and other reference evapotranspiration.
    • +
  • pipedream - Interactive hydrodynamic solver for sewer/stormwater networks.
    • +
  • TADA - Help developers compile and evaluate water quality data for samples collected from surface water monitoring sites on streams and lakes.
    • +
  • wqtrends - R package to assess water quality trends with generalized additive models.
    • +
  • pyet - Estimate reference and potential evaporation.
    • +
  • SPEI - Calculate and visualize some popular drought indices such as the SPI, SPEI and SGI.
    • +
  • GISWATER - This software connects different IT solutions and pre-existent databases allowing you to setup a high performance water management system in combination with hydraulic software.
    • +
  • Ribasim - A water resources model, designed to be the replacement of the regional surface water modules Mozart and SIMRES in the Netherlands Hydrological Instrument.
    • +
  • Pywr - Simulates customisable water allocation and operation rules throughout complex multi-purpose managed water systems at each user-defined time-step.
    • +
  • mosartwmpy - A python translation of MOSART-WM, a model for water routing and reservoir management written in Fortran.
    • +
  • ClearWater Modules - A collection of water quality and vegetation process simulation modules designed to couple with water transport models.
    • +
  • RAT - Uses satellite remote sensing data to monitor water surface area and water level changes in artificial reservoirs.
    • +
  • LDCast - A precipitation nowcasting model based on a latent diffusion model.
    • +
  • WaterTAP - Create an open-source water treatment model library that is compatible with the IDAES Platform.
    • +
  • MATILDA - Tool for modeling water resources in glacierized catchments.
    • +
  • PyPES - A class hierarchy designed to represent configurations of process engineering systems, such as wastewater treatment or desalination plants.
    • +
  • PARETO - An Optimization Framework for Produced Water Management and Beneficial Reuse.
    • +
  • WSIMOD - Provides a message passing interface to enable different subsystem models to communicate water flux and water quality information between each other, and self-contained representations of the key parts of the water cycle.
    • +
  • ODM - Aims to improve wastewater monitoring through interoperable data by providing robust support for reporting of biological, toxic or other health hazards.
    • +
  • wqbench - Generate download and compile data from EPA ECOTOX database to generate aquatic life water quality benchmarks.
    • +
  • Microplastic Data Portal - An Open Source project to increase access to microplastics data.
    • +
  • One4All - R package for data validation and visualization with a focus on microplastic data.
    • +
  • gw-conditions - A visualization showing groundwater conditions as little peaks above or below their normal values.
    • +
  • EPyT-Flow - Provides easy access to water distribution network simulations with a high level interface for generation of hydraulic and water quality scenario data.
    • +
  • LibreWater - A Small Scale Desalination and Water Purification Prototype that is powered by electric heating for scientific purpose and prototyping ahead according to the Libre Water Mission.
    • +
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Soil and Land

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  • SoilGrids250m - Global Spatial predictions of soil properties and classes at 250 m resolution.
    • +
  • SoilGrids - A system for automated soil mapping based on global soil profile and environmental covariate data at 250 m spatial resolution.
    • +
  • Hyperspectral Soilmoisture Dataset - Hyperspectral benchmark dataset on soil moisture.
    • +
  • soilReports - An R package that assists with the setup and operation of a collection of soil data summary, comparison, and evaluation reports.
    • +
  • AQP - Algorithms for Quantitative Pedology is a collection of code, ideas, documentation, and examples wrapped-up into several R packages.
    • +
  • sharpshootR - Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.
    • +
  • soilDB - Simplified Access to NCSS Soil Databases.
    • +
  • Landslides Detection - Machine learning tool to detect landslides from optical satellite imagery.
    • +
  • pyTSEB - Contains Python code for Two Source Energy Balance models (Priestley-Taylor TSEB-PT, Dual Time Difference DTD and TSEB with component soil and canopy temperatures TSEB-2T) for estimating sensible and latent heat flux based on measurements of radiometric surface temperature.
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  • slga - Offers the ability to download geographic subsets of raster data from the Soil and Landscape Grid of Australia.
    • +
  • smapr - An R package for acquisition and processing of NASA (Soil Moisture Active-Passive) SMAP data.
    • +
  • DRIP-SLIP - Detecting Realtime Increased Precipitation / Sudden Landslide Identification Product.
    • +
  • esa_cci_sm - Readers and converters for ESA CCI Soil Moisture image and time series data.
    • +
  • US_SoilGrids100m - Complete-coverage gridded predictions of soil properties (percent organic carbon, total nitrogen, bulk density, pH, and percent sand and clay) and classes (taxonomic great group and particle size in the control section) for the Conterminous U.S.
    • +
  • pytesmo - Python Toolbox for the Evaluation of Soil Moisture Observations.
    • +
  • Drought Indices Map - This project attempts to find an accessible and friendly way to visualize data of drought indices in Spain from 1961 until 2017.
    • +
  • ml_drought - A Machine Learning Pipeline to Predict Vegetation Health.
    • +
  • srdb - Global soil respiration database.
    • +
  • cosore - Data, metadata, and software tools for the COSORE database of continuous soil respiration measurements.
    • +
  • sidb - Contains information on laboratory soil incubation experiments, with emphasis on time series of CO2 release.
    • +
  • ISRaD - Improve the use of radiocarbon as a constraint for understanding the soil carbon cycle.
    • +
  • OpenFLUID - A software environment for modeling and simulation of complex landscape systems.
    • +
  • Trends.Earth - A QGIS plugin that supports monitoring of land change, including changes in productivity, land cover, and soil organic carbon.
    • +
  • pycoal - A Python toolkit for characterizing Coal and Open-pit surface mining impacts on American Lands.
    • +
  • stats_for_soil_survey - Lecture material on "Statistics for Pedologists".
    • +
  • Simsphere - A one-dimensional model that allows one to simulate the transfer of heat and moisture between plants, soil and atmosphere over a 24 hour day.
    • +
  • CryoGrid3 - A one-dimensional land surface model dedicated to simulate ground temperatures in permafrost environments.
    • +
  • CNN-SoilTextureClassification - One-dimensional convolutional neural networks (CNN) for the classification of soil texture based on hyperspectral data.
    • +
  • EarthML - Improving, documenting, and illustrating how to use other, freely available, general-purpose open source projects to solve problems in the earth sciences.
    • +
  • STEP - ESA is developing free open source toolboxes for the scientific exploitation of Earth Observation missions under the Scientific Exploitation of Operational Missions programme element.
    • +
  • globsim - Using global reanalyses for permafrost simulation.
    • +
  • rSFSW2 - An R package to create soil water balance simulation experiment.
    • +
  • landscapemetrics - Landscape Metrics for Categorical Map Patterns in R.
    • +
  • SCOPE - Simulation model for radiative transfer, photosynthesis and energy fluxes in vegetation and soil.
    • +
  • Demeter - A land use land cover disaggregation and change detection model.
    • +
  • OpenLandMap - Data, services and web-apps providing access and interactive visualizations of the TB of high resolution data produced by the OpenGeoHub Foundation and contributing organizations.
    • +
  • PyLandStats - An open-source Pythonic library to compute landscape metrics.
    • +
  • NLMR - R package to simulate neutral landscape models.
    • +
  • LT-GEE - Google Earth Engine implementation of the LandTrendr spectral-temporal segmentation algorithm.
    • +
  • ismn - Readers for the data from the International Soil Moisture Database.
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  • soils-revealed - Platform for direct visualization, analysis and reporting of soil organic carbon predictions and changes over time.
    • +
  • Soil Erosion Watch - A Google Earth Engine App to explore the state of the world's degraded soils.
    • +
  • Land Matrix - Independent global land monitoring initiative that promotes transparency & accountability in decisions over large-scale land acquisitions.
    • +
  • SITS - Enables users to apply machine learning techniques for classifying image time series obtained from earth observation data cubes.
    • +
  • iSDAsoil - Open access soil property map with 30m resolution for Africa.
    • +
  • Landlab - An open source Python package for numerical modeling of Earth surface dynamics.
    • +
  • Terraso - Develop an open source technology platform to help local community leaders practice Integrated Landscape Management.
    • +
  • Open Soil Spectral Library - Brings together soil scientists, spectroscopists, informaticians, data scientists and software engineers to overcome some of the current bottlenecks preventing wider and more efficient use of soil spectroscopy.
    • +
  • OpenLand - Tool for Quantitative Analysis and Visualization of Land Use and Land Cover Change.
    • +
  • cultionet - A library for semantic segmentation of cultivated land using a neural network.
    • +
  • pygef - Simple parser for soil properties measurements.
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  • pydov - Aggregates data about soil, subsoil and groundwater of Flanders and makes them publicly available.
    • +
  • ILAMB - Python software used in the International Land Model Benchmarking project.
    • +
  • QA4SM - Provides an operational online validation service for soil moisture products.
    • +
  • envalysis - An R package containing miscellaneous functions for data analysis in environmental chemistry and ecotoxicolog.
    • +
  • Statistics for Soil Survey - Develop solutions to investigate soil survey correlation problems and update activities.
    • +
  • LWFBrook90R - Simulates daily transpiration, interception, soil and snow evaporation, streamflow and soil water fluxes through a soil profile covered with vegetation.
    • +
  • downscalr - An R package for downscaling of land-use and land-use change projections.
    • +
  • droughtR - An R package for non-stationary meteorological drought monitoring.
    • +
  • meteoland - Functions to estimate weather variables at any position of a landscape.
    • +
  • ausplotsR - Directly access plot-based data on vegetation and soils across Australia, with simple function calls to extract the data and merge them into species occurrence matrices for analysis or to calculate things like basal area and fractional cover.
    • +
  • PyLUSAT - Intends to provide users with tools that can be used to conduct land-use suitability analysis.
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  • earthlib - Contains several thousand unique spectral endmembers representing green vegetation, soil, non-photosynthetic vegetation, urban materials, and burned materials.
    • +
  • Soil Freeze-thaw Model - The soil freeze-thaw model simulates the transport of heat in soil using a one-dimensional vertical column.
    • +
  • soils - Collection of soil health data visualization and reporting tools, including a RStudio project template with everything you need to generate custom HTML and Microsoft Word reports for each participant in your soil health survey.
    • +
  • rsi - Code for Retriving STAC Information, addressing Repeated Spatial Infelicities and interfacing with Rsome Spectral Indices.
    • +
  • ClimaLand.jl - Create and run land models in integrated or standalone modes.
    • +
  • RBBGCMuso - A software package that supports the application of the Biome-BGCMuSo biogeochemical mode, which simulates the storage and fluxes of water, carbon and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystem.
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  • Fractional Cover - A remote sensing algorithm which measures the photosynthetic, non-photosynthetic and bare earth components of a Landsat image.
    • +
  • soiltexture - A toolbox for plotting, classifying and transforming soil texture data like clay, silt and sand contents.
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  • Noah-MP - A widely-used state-of-the-art land surface model used in many research and operational weather and climate models.
    • +
  • SMODERP2D - Distributed event-based model for surface and subsurface runoff and erosion.
    • +
  • rOPTRAM - Implements The OPtical TRapezoid Model (OPTRAM) to derive soil moisture based on the linear relation between a vegetation index, i.e. NDVI, and Shortwave Infra-red.
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  • AquaBEHER - Estimation and Prediction of Wet Season Calendar and Soil Water Balance for Agriculture.
    • +
  • Sand Mining Watch - A custom sand-mine detection tool by fine-tuning foundation models for earth observation, which leverage self supervised learning.
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Ecotoxicology

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  • Health Assessment Workspace Collaborative - A website designed to capture key data and analyses performed in conducting human-health assessment of chemicals and other environmental exposures.
    • +
  • toxpiR - R package for the Toxicological Priority Index (ToxPi) prioritization algorithm.
    • +
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Sustainable Development

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Sustainable Development Goals

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  • open-sdg - A platform for collecting and disseminating data for the Sustainable Development Goal global indicators.
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  • SustainBench - Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning.
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  • OSDG - Tool that assigns Sustainable Development Goals labels to your input.
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  • OSDG Community Dataset - A public dataset of thousands of text excerpts, validated by OSDG Community Platform citizen scientists with respect to the Sustainable Development Goals.
    • +
  • US Sustainable Development Goal indicators - U.S. National Reporting Platform for the Sustainable Development Goals.
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  • The SDG Transformation Center - Science-based tools and analytics for SDG pathways, policies and financing.
    • +
  • SDGmapR - R functions and datasets related to the mapping of text to the United Nations 17 Sustainable Development Goals.
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  • Europe Sustainable Development Reports - Track the progress of the European Union and European Countries towards the Sustainable Development Goals.
    • +
  • text2sdg - Multi-system analysis package that identifies SDGs in text, opening up the opportunity to monitor any type of text-based data.
    • +
  • SDG Detector - Identify and visualize 17 Sustainable Development Goals and associated 169 Targets in text.
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  • Polish SDG reporting platform - Polish indicators for the Sustainable Development Goals of the 2030 Agenda.
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Population and Poverty

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  • wopr - An R package and Shiny application to provide API access to the WorldPop Open Population Repository.
    • +
  • censusdis - A Python package for discovering, loading, and analyzing U.S. Census demographic, economic, and geographic data and metadata with access to the full collection of data and maps the U.S. Census publishes via their APIs.
    • +
  • Boosting Upward Mobility from Poverty - A multi-year effort to understand upward mobility from poverty and to work with local leaders to promote policies that boost upward mobility from poverty.
    • +
  • UNICEF AI4D Relative Wealth Project - Aims to develop open datasets and machine learning models for poverty mapping estimation across nine countries in Southeast Asia.
    • +
  • Social Income - Fighting global poverty with the help of everyday people and your coding skills.
    • +
  • Justice40 Tool - A tool to identify disadvantaged communities due to environmental, socioeconomic and health burdens.
    • +
  • Accessible Poverty Estimates - We utilize random forests regressions and features derived from a range of geospatial datasets (nighttime lights, landcover, OpenStreetMap features, and more) to produce estimates of development indicators such as household wealth/poverty.
    • +
  • Poverty and Inequality Platform Methodology - Describes how global and regional poverty rates are calculated.
    • +
  • tidycensus - An R package that allows users to interface with the US Census Bureau's decennial Census and five-year American Community APIs and return tidyverse-ready data frames, optionally with simple feature geometry included.
    • +
  • pipr - Allows R users to compute poverty and inequality indicators for more than 160 countries and regions from the World Bank's database of household surveys.
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Sustainable Investment

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  • Integrated Valuation of Ecosystem Services and Tradeoffs - A family of tools for quantifying the values of natural capital in clear, credible, and practical ways.
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  • The REgional Model of INvestments and Development - A modular open source multi-regional model incorporating the economy, the climate system and a detailed representation of the energy sector.
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  • WHAT-IF - Water, Hydropower, Agriculture Tool for Investment and Financing decision support tool.
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  • r2dii.match - These tools implement in R a fundamental part of the software PACTA (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios.
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  • r2dii.analysis - These tools help you to assess if a financial portfolio aligns with climate goals.
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  • SBTi Temperature Alignment tool - Helps companies and financial institutions to assess the temperature alignment of current targets, commitments, and investment and lending portfolios, and to use this information to develop targets for official validation.
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  • Real-Impact-Tracker-Methodology - A methodology to assess the Real Impact of funds on social and environmental issues.
    • +
  • MimiFUND.jl - Used to perform cost-benefit and cost-effectiveness analyses of greenhouse gas emission reduction policies.
    • +
  • open-climate-investing - Application and data for analyzing and structuring portfolios for climate investing.
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  • SSC - Contains the source code for the technology and financial models contained within the National Renewable Energy Laboratory's System Advisor Model.
    • +
  • OS-Climate Data Commons - Collect, normalize and integrate climate and ESG data from public and private sources.
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  • XDC Model - Enable users, or any intereted subject, to understand and familiarize with the basic concepts of the X-Degree Compatibility methodology.
    • +
  • gfer - Designed for green finance and environmental risk research focused on data collecting and analyzing in green finance and environmental risk research and analysis.
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  • WikiRate - Facilitates research and analysis on complex topics in collaboration with partners, to make ESG data open, comparable and useful for all.
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  • Equinox - Supports the holistic risk management of sustainable finance projects.
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  • PACTA - Measuring the alignment of financial portfolios with climate scenarios.
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  • Dataland - A decentralized ecosystem for raw ESG-data where market participants exchange ESG-data in a transparent way.
    • +
  • physrisk - Primarily designed to run 'bottom-up' calculations that model the impact of climate hazards on large numbers of individual assets including natural and operations.
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  • climate-finance - Is the python package to get, clean, and work with international public climate finance.
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  • HeCo Invest - A digital collaborative platform pilot aimed to support filling the conservation financing gap in the Amazon Basin by optimizing project financing channels in this region.
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  • DEEP - Our goal is to reconfigure the economics of ecology by finding ways to fund and expand projects that sustain life on our planet.
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  • Greenlang - The first open-source component promoting transparency, interoperability, and collaboration in the ESG reporting domain.
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Knowledge Platforms

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  • Climate Watch - Offers open data, visualizations and analysis to help policymakers, researchers and other stakeholders gather insights on countries' climate progress.
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  • Appropedia - Shares knowledge to build rich, sustainable lives.
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  • Open Hardware Observatory - Search engine and assessment platform for sustainable open hardware.
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  • Open Source Ecology - Developing open source industrial machines that can be made for a fraction of commercial costs, and sharing the designs for free.
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  • ProjectDrawdown - The mission of Project Drawdown is to help the world reach "Drawdown"— the point in the future when levels of greenhouse gases in the atmosphere stop climbing and start to steadily decline, thereby stopping catastrophic climate change — as quickly, safely, and equitably as possible.
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  • NOAA-Affiliated-Projects - A list of GitHub accounts and repositories that are contributed to by National Oceanic and Atmospheric Administration staff from line offices and/or staff office throughout the organization.
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  • Climatescape - Discover the organizations solving climate change.
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  • Open Climate Knowledge - An open research project for data mining Open Access papers related to Climate Change.
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  • OpenClimate - An open source initiative exploring the application of distributed ledger technology and other emerging technologies, such as IoT, big data and machine learning, to the challenge of helping the world keep a transparent climate accounting system towards the climate targets.
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  • ModularSensors - This Arduino library gives environmental sensors a common interface of functions for use with Arduino-compatible dataloggers.
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  • OpenSourceSeeds - Committed to re-establishing seed as a common good for all.
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  • Transition Project - An Open Data Initiative designed to help enable rapid carbon abatement for cities worldwide.
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  • Climate Strike Software - Prevent oil and gas companies from co-opting your work and extracting more fossil fuels with this software license.
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  • Climate Impact Lab - A team of economists, climate scientists, data engineers, and risk analysts are building the world's most comprehensive body of research quantifying the impacts of climate change, sector-by-sector and community-by-community around the world.
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  • Climate change in the contiguous United States - The Washington Post's analysis of NOAA climate change data for the contiguous United States.
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  • Regen Ledger - Providing a structured database of claims regarding ecological state and change of state.
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  • Stripe Climate Carbon Removal Purchases - Source materials supporting Stripe Climate carbon removal purchases.
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  • Net Zero Tracker - Create the definitive global resource for collating, assessing and presenting the scale and quality of net zero pledges across nationals, sub-nationals, companies and other entities.
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  • Credible Goals Database - A public goals database containing leading sustainability goals and commitments set by large companies globally.
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  • Digital Public Goods - Accelerate the attainment of the sustainable development goals in low- and middle-income countries by facilitating the discovery, development, use of, and investment in digital public goods.
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  • SWEET - Official repository for Semantic Web for Earth and Environmental Terminology Ontologies.
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  • U.S. Climate Resilience Toolkit - A website designed to help people find and use tools, information, and subject matter expertise to build climate resilience.
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  • Climatemind - Explore how your values and personal interests are being affected by climate change and shows you attractive solutions exist that you may not be aware of.
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  • Decarb My State - What does it take to decarbonize your state.
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  • STRESS Tool - Show climate risk on a map of the US, with the MIT Joint Program data.
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  • Climate Change Tracker - Empower individuals and communities to take action against the global threat of climate change by providing a reliable, user-friendly platform for tracking and understanding its progression.
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  • NATUR'ADAPT - A community interested in climate change and its impacts on nature, especially in protected natural areas.
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  • The Climate Tech Handbook - Help us build the most accessible and accurate climate tech resource on the planet.
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  • esa-climate-from-space - The role of the Climate from Space application is to increase the visibility of the Climate Change Initiative programme and the role of ESA satellite data in climate science.
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  • ClimateTriage - Discover a meaningful way to contribute to open source projects focused on climate technology and sustainability.
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  • Open Sustainable Technology - A directory and analysis of the open source ecosystem in the areas of climate change, energy, biodiversity and natural resources.
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  • Digital Earth Australia Notebooks - Hosts Jupyter Notebooks, Python scripts and workflows for analysing Digital Earth Australia (DEA) satellite data and derived products.
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  • ONEARMY - A platform to build useful communities that aim to tackle social and environmenta problems.
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  • kartevonmorgen - We make all sustainable companies and change initiatives visible on the map of tomorrow.
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  • Education4Climate - A tool for crawling and analyzing data from higher education schools.
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  • NRPTI - On this site you'll find records, documents and details of compliance and enforcement activities undertaken by British Columbia natural resource agencies such as administrative sanctions, administrative penalties, court convictions, inspections, orders, violation tickets and community environmental justice forums.
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  • QuotaClimat - The aim of this work is to deliver a tool to a consortium around QuotaClimat, Climat Medias allowing them to quantify the media coverage of the climate crisis.
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  • Gold Mine Detector - Automated detection of artisanal gold mines in Sentinel-2 satellite imagery, with links to related journalism.
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Data Catalogs and Interfaces

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    +
  • Climate Data Store - Dive into this wealth of information about the Earth's past, present and future climate.
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  • Global Fishing Watch - We use cutting-edge technology to visualise, track and share data about global fishing activity in near real-time and for free.
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  • Global Wind Atlas - Immediately start exploring windy areas.
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  • Global Solar Atlas - Start exploring solar potential.
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  • Food and Agriculture Organization Map Catalog - Allows to easily share geographically referenced thematic information between different organizations.
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  • The Subak Data Catalogue - Exists to make climate data more discoverable, more trusted and more connected.
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  • EEA geospatial data catalogue - Discover and access easily the geospatial data catalogue of the European Environment Agency.
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  • Radiant MLHub Python Client - Open community commons for geospatial training data, machine learning models, and standards to encourage collaboration and share information.
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  • Global Environmental Database - Providing long-term monitoring data, data analysis results, output of models.
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  • Resource Watch - Features hundreds of data sets all in one place on the state of the planet's resources and citizens.
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  • EarthData - Our vision is to make NASA's free and open Earth science data interactive, interoperable, and accessible for research and societal benefit both today and tomorrow.
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  • owidR - An R Package for Interacting with Data from Our World in Data.
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  • Custom Scripts Sentinel Hub - A repository of custom scripts to be used with Sentinel Hub.
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  • eurostat R package - R tools to access open data from Eurostat, the statistical office of the European Union.
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  • giscoR - An R API package that helps to retrieve data from Eurostat Geographic Information System of the Commission.
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  • Digital Earth Africa Notebooks - Using Earth observations to address social, environmental and economic changes on the Africa continent.
    • +
  • MeCCO - Open Database on the Media Coverage of Climate Change and Global Warming.
    • +
  • Climate Action Plans - A simple, open database of local government climate action plan documents and emissions data.
    • +
  • AI for Earth Data Sets - Notebooks and documentation for AI-for-Earth-managed datasets on Azure.
    • +
  • The POWER Project - Provides solar and meteorological data sets from NASA research for support of renewable energy, building energy efficiency and agricultural needs.
    • +
  • Earthdata Search - A web application developed by NASA EOSDIS to enable data discovery, search, comparison, visualization, and access across EOSDIS' Earth Science data holdings.
    • +
  • Planetary Computer Data Catalog - Combines a multi-petabyte catalog of global environmental data with intuitive APIs and a flexible scientific environment.
    • +
  • Global Energy Monitor - Studies the evolving international energy landscape, creating databases, reports, and interactive tools that enhance understanding.
    • +
  • STAC Index - Find STAC Catalogs, Collections, APIs, Software and Tools.
    • +
  • Eumap - Comprises environmental, land cover, terrain, climatic, soil and vegetation layers covering the continental Europe at relatively fine spatial resolutions.
    • +
  • Open Data Science Europe Metadata Catalog - Building the Open Data Science Europe Portal, a freely-accessible viewer containing gridded layers for Europe at spatial resolutions of 30-m or better.
    • +
  • EarthDataLab.jl - Julia interface for Reading from the Earth System Datacube.
    • +
  • The CEDA Archive - We host over 18 Petabytes of data from climate models, satellites, aircraft, met observations, and other sources.
    • +
  • Climate Data Dashboard of the ESA Climate Change Initiative - Access global climate data produced through the ESA's Climate Change Initiative.
    • +
  • USGS - A python module for interfacing with the US Geological Survey's API.
    • +
  • getSpatialData - Making it easy to query, preview, download and preprocess multiple kinds of satellite datasets via R.
    • +
  • Smart Data Models - This Models describe the main entities involved with smart applications that deal with environmental issues.
    • +
  • epair - Use R to get data from the Environmental Protection Agency API.
    • +
  • Siphon - A collection of Python utilities for retrieving atmospheric and oceanic data from remote sources, focusing on being able to retrieve data from Unidata data technologies.
    • +
  • NEON - The National Ecological Observatory Network provides open data to understand changing ecosystems.
    • +
  • SynopticPy - Gives you access to real-time and historical surface-based weather and environmental observations for thousands of stations.
    • +
  • NASA CMR STAC - NASA's Common Metadata Repository is a metadata catalog of NASA Earth Science data.
    • +
  • satlas - Aims to provide open AI-generated geospatial data that is highly accurate, available globally, and updated on a frequent (monthly) basis.
    • +
  • env_canada - Provides access to various data sources published by Environment and Climate Change Canada.
    • +
  • DataONE - A community driven project providing access to data across multiple member repositories, supporting enhanced search and discovery of Earth and environmental data.
    • +
  • rdataone - R package for reading and writing data at DataONE data repositories.
    • +
  • eeExtra - A ninja python package that unifies the Google Earth Engine ecosystem.
    • +
  • rgee - An R binding package for calling Google Earth Engine API from within R.
    • +
  • earthaccess - Search, download or stream NASA Earth science data with just a few lines of code.
    • +
  • EODAG - Gives you an easy way to access products from more than 10 earth observation providers, with more than 50 different product types (Sentinel 1, Sentinel 2, Sentinel 3, Landsat, etc.) that can be searched and downloaded.
    • +
  • leafmap - A Python package for geospatial analysis and interactive mapping in a Jupyter environment.
    • +
  • ecocomDP - A dataset design pattern for harmonizing ecological community surveys in a research question agnostic format from source datasets published across multiple repositories.
    • +
  • FedData - R package implementing functions to automate downloading geospatial data available from several federated data sources in hydology, soil, climatology, cropland or landcover.
    • +
  • pydap - An implementation of the Opendap/DODS protocol, written from scratch in pure Python, that allows researchers to access NASA Earth science data from a variety of programs.
    • +
  • GCIS - As a centralized catalog of all materials and data used for Global Change Information System products and climate assessments.
    • +
  • ECHO - Contains Jupyter notebooks for processing the EPA's Environmenal Compliance History Online (ECHO) data from multiple EPA program in emissions, violations, inspections, and enforcement for the Clean Air Act, Clean Water Act, and Resouce Conservation and Recovery Act.
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  • msc_pygeoapi - The Meteorological Service of Canada Python API provides up-to-date information on past, present and future weather conditions, climate, water and environmental information over Canada and the world.
    • +
  • Clay Foundation Model - Clay is a foundational model of Earth using a vision transformer architecture adapted to understand geospatial and temporal relations on Earth Observation data.
    • +
  • NYC Environment and Health Data Portal - Shows how environments affect health in NYC, by publishing data and information that explain the connection.
    • +
  • Environmental Outcomes Platform - An end-to-end solution for acquiring, managing, and interacting with environmental information to empower council services both internally and externally.
    • +
  • PrithWe - A platform dedicated to helping individuals and businesses calculate and reduce their carbon footprint, providing tools and resources to promote sustainability and environmental awareness in everyday practices.
    • +
  • Environmental Data Science book - A community driven project providing access to multiple Jupyter notebooks showcasing open environmental science.
    • +
  • VEDA Dashboard - Developed collaboratively and mostly reusing existing open-source components, VEDA consolidates GIS delivery mechanisms, processing platforms, analysis services, and visualization tools and provides an ecosystem of open tools for addressing Earth science research and application needs through the public-facing VEDA Dashboard.
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  • oss4climate - A scraping tool for open-source code (GitLab & GitHub) and associated search engine implementation to facilitate discovery of open-source code to address climate challenges.
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Environmental Satellites

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  • satpy - A python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats.
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  • goes2go - Download and process GOES-16 and GOES-17 data from NOAA's archive on AWS using Python.
    • +
  • landsatxplore - Provides an interface to the EarthExplorer portal to search and download Landsat Collections scenes through a command-line interface or a Python API.
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  • sentinelhub-py - Download and process satellite imagery in Python using Sentinel Hub services.
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  • sentinelsat - Makes searching, downloading and retrieving the metadata of Sentinel satellite images from the Copernicus Open Access Hub easy.
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  • MODIS - Provides automated access to the global online data archives LP DAAC, LAADS and NSIDC as well as processing capabilities such as file conversion, mosaicking, subsetting and time series.
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  • MODIStsp - An R package for automatic download and preprocessing of MODIS Land Products Time-Series.
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  • MODISTools - Interface to the MODIS Land Products Subsets Web Services.
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  • pyModis - A Python library to download and process MODIS data from NASA server.
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  • Satip - A library for satellite image processing providing all of the functionality necessary for retrieving, and storing EUMETSAT data.
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  • openEO - Provides intuitive programming libraries to process a wide variety of earth observation datasets.
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  • xarray-sentinel - Easily access and explore the SAR data products of the Copernicus Sentinel-1 satellite mission in Python.
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  • noaa-apt - Receive and decode images from weather satellites via radio signals.
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  • harp - Data harmonization toolset for scientific earth observation data.
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  • ccplot - Command-line application for visualizing data from CloudSat and CALIPSO satellites.
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  • goestools - Tools to work with signals and files from GOES satellites.
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  • eodash - Combines the resources, technical knowledge and expertise of three partner agencies ESA, JAXA, and NASA to strengthen our global understanding of global environmental changes and other societal challenges impacting our planet.
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  • SlideRule Earth - Process ICESat2 datasets in the cloud through REST API calls to SlideRule web services.
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  • SpaceLiDAR - A Julia toolbox for ICESat, ICESat-2 and GEDI data to quickly search, download, and load filtered point data with relevant attributes from the .h5 granules of each data product.
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  • SNAP - The common software platform for the three Sentinel Toolboxes which are developed by the European Space Agency (ESA) for the scientific exploitation of the Sentinel-1, Sentinel-2 and Sentinel-3 missions.
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  • EOReader - A remote-sensing opensource python library reading optical and SAR constellations, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.
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  • LandsatTS - An R package to facilitate retrieval, cleaning, cross-calibration, and phenological modeling of Landsat time-series data.
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  • rsat - Dealing with Multiplatform Satellite Images from Landsat, MODIS, and Sentinel.
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  • EnPT - An automated pre-processing pipeline for the new EnMAP hyperspectral satellite data.
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  • BlackMarblePy - A Python package that provides a simple way to use nighttime lights data from NASA's Black Marble project that provides a product suite of daily, monthly and yearly global nighttime lights.
    • +
  • cbers-2-stac - Create and keep up-to-date a STAC static catalog and API for CBERS-4/4A and Amazonia-1 images on AWS.
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  • cbers4asat - Python library to search and process images from the CBERS-04A and AMAZONIA-1 satellites, which provide remote sensing data for the observation and monitoring of deforestation, particularly in the Amazon region, and diversified agriculture throughout the national territory, with a high repetition rate, in order to act in synergy with existing environmental programmes.
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Taxonomy and Ontology

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  • Digital Twins Definition Language ontology for Energy Grid - A global standard for energy grid assets management, power system operations modeling and physical energy commodity market.
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  • The Open Energy Ontology - A domain ontology of the energy-system modeling context.
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  • ECTO - Modular environmental exposures ontology.
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  • ENVO - A community-driven ontology for the representation of environments.
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  • EML - An open source, community oriented project dedicated to providing a high-quality metadata specification for describing data relevant to diverse disciplines that involve observational research like ecology, earth, and environmental science.
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  • taxonomy4good - A sustainability lexicon that provides the freedom to create custom taxonomies in addition to listed taxonomies.
    • +
  • ecoCore - A standard vocabulary for the ecological community, a need expressed repeatedly over the past few years at workshops focused on ecological, environmental, and population-based semantics.
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  • Climate categories - Commonly used codes, categories, terminologies, and nomenclatures used in climate policy analysis in a nice Python package.
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  • BattINFO - Consists of a list of entities representing concepts used in batteries and electrochemistry.
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  • Plant-Pollinator Interactions Vocabulary - Plant-Pollinator Interactions is a standardized vocabulary maintained by the Brazilian Network on Plant-Pollinator Interactions.
    • +
  • Plant Trait Ontology - A controlled vocabulary of describe phenotypic traits in plants.
    • +
  • FOODON - Provides a neutral and ontology-driven standard for government agencies, industry, nonprofits and consumers to name and reference food products and their components throughout the food supply chain.
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  • BuildingMOTIF - Enabling the semantic interoperability of advanced building technologies needed to fully decarbonise buildings.
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  • CEON - The Circular Economy Ontology Network provides a shared vocabulary in the form of a network of ontologies to support efficient decentralized sharing of industry data in circular economies.
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Curated Lists

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  • Datasets APIs and open source projects related to Climate Change - A curated list of APIs, open data and ML/AI projects on climate change.
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  • Linux Foundation Energy Landscape - This landscape is intended as a map to explore the open source projects in the energy sector, and also shows the member companies of the LF Energy Foundation.
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  • Open Models - This page lists energy models published under open source licenses.
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  • Awesome Green Software - Research, tools, code, libraries and training to for building applications that emit less carbon into our atmosphere.
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  • Awesome Sustainability Jobs - A curated list of companies in the sustainability sector that have jobs for devs.
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  • Awesome Spectral Indices - A ready-to-use curated list of Spectral Indices for Remote Sensing applications.
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  • Awesome Vegetation Index - List of reference, applications of common Vegetation Indices for Multi-spectral, hyper-spectral and UAV images.
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  • awesome-transit - Community list of transit APIs, apps, datasets, research, and software.
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  • Awesome Coastal - A curated list of awesome resources for coastal engineers and scientists.
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  • Awesome Agriculture - Open source technology for agriculture, farming, and gardening.
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  • Cryosphere Software, Data and Tools - A prototype of a curated list of awesome data sources, models, tools and organizations related to the Cryosphere and its subspheres.
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  • Awesome Clean Tech - Clean technology is any process, product, or service that reduces negative environmental impacts through significant energy efficiency improvements, the sustainable use of resources, or environmental protection activities.
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  • Awesome Open Climate Science - Awesome Open Atmospheric, Ocean, and Climate Science.
    • +
  • Awesome Earth - "What can I do about the climate crisis?" Here are 326 things you can do.
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  • Awesome Open Geoscience - Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome.
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  • Code against Climate Change - A curated list of tech projects against climate change - hoping to inspire disruptive technological climate action.
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  • Awesome Arctic Data - A curated list on data sources related to climate change in the arctic.
    • +
  • Awesome Remote Sensing Change Detection - List of datasets, codes, and contests related to remote sensing change detection.
    • +
  • Awesome-forests - A curated list of ground-truth forest datasets for the machine learning and forestry community.
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  • awesome-earth-engine-apps - An awesome list of all available Google Earth Engine Apps and user-specific App Galleries.
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  • awesome-gee-community-datasets - Community Datasets & Data Commons in Google Earth Engine.
    • +
  • Urban & Regional Planning Resources - Community list of data & technology resources concerning the built environment and communities.
    • +
  • Open Source Python Packages in Hydrology - Holds a list of open source Python packages interesting to Hydrologists.
    • +
  • PO.DAAC - NASA Earth Observing System Data and Information System data center managed by the Earth Science Data and Information System Project.
    • +
  • Awesome Sentinel - A curated list of awesome tools, tutorials and APIs related to data from the Copernicus Sentinel Satellites.
    • +
  • geospatial-data-catalogs - A list of open geospatial datasets available on AWS, Earth Engine, Planetary Computer, and STAC Index.
    • +
  • Industrial Ecology Dashboard - A collection of open source projects relevant for industrial ecology practitioners.
    • +
  • Energy Systems and Informatics Collection by ACM SIGEnergy - A curated list of publicly available datasets, models, and software for researchers and professionals who study energy systems and informatics.
    • +
  • Awesome-Earth-Artificial-Intelligence - A curated list of tutorials, notebooks, software, datasets, courses, books, video lectures and papers specifically for Artificial Intelligence use cases in Earth Science.
    • +
  • Callisto-Dataset-Collection - A list of datasets aiming to enable Artificial Intelligence applications that use Earth Observation, satellite and other data.
    • +
  • Awesome ERDDAP - A curated list of awesome Environmental Research Division's Data Access Program (ERDDAP) projects and deployments.
    • +
  • Awesome Large Weather Models - A collection of articles on Large Weather Models (LWMs), to make it easier to find and learn.
    • +
  • ingestr - Provides functions to extract environmental point data from large global files or remote data servers and create time series at user-specified temporal resolution.
    • +
  • GitHub's Green Software Directory - A simple and easy-to-use resource that all developers can use to adopt green software tools.
    • +
  • OpenForest - An initiative to centralize open access forest monitoring datasets.
    • +
  • Awesome Green AI - A curated list of awesome Green AI resources and tools to reduce the environmental impacts of using and deploying AI.
    • +
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Contributors

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Artwork and License

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Animation created with An Animated Map of the Earth by Eleanor Lutz

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The artwork included in this repository are shared under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This work and all contributions to it are released into terms of the Creative Commons Attribution 4.0 International if not otherwise noted.

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CC-BY-4.0 license

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