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BlockingPy submission #232

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14 of 32 tasks
T-Strojny opened this issue Jan 9, 2025 · 2 comments
Open
14 of 32 tasks

BlockingPy submission #232

T-Strojny opened this issue Jan 9, 2025 · 2 comments

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@T-Strojny
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T-Strojny commented Jan 9, 2025

Submitting Author: (@T-Strojny)
All current maintainers: @T-Strojny
Package Name: BlockingPy
One-Line Description of Package: Blocking records for record linkage and deduplication with Approximate Nearest Neighbor algorithms.;
Repository Link: https://github.com/ncn-foreigners/BlockingPy
Version submitted: v0.1.7
EiC: @coatless
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD


Code of Conduct & Commitment to Maintain Package

Description

  • Include a brief paragraph describing what your package does: BlockingPy is a package that speeds up record linkage and deduplication tasks by using Approximate Nearest Neighbor (ANN) algorithms to create blocks with candidate record pairs. When linking or deduplicating large datasets, comparing all possible record pairs becomes computationally infeasible. BlockingPy solves this by using ANN algorithms to quickly identify similar records while significantly reducing the number of required comparisons.

Scope

  • Please indicate which category or categories.
    Check out our package scope page to learn more about our
    scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

    • Data retrieval
    • Data extraction
    • Data processing/munging
    • Data deposition
    • Data validation and testing
    • Data visualization1
    • Workflow automation
    • Citation management and bibliometrics
    • Scientific software wrappers
    • Database interoperability

Domain Specific

  • Geospatial
  • Education

Community Partnerships

If your package is associated with an
existing community please check below:

  • For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):
    • Data processing/munging : BlockingPy transforms raw data into feature vectors and applies ANN algorithms and graphs to reduce the comparison space which enables scalable record linkage and deduplication.

    • Who is the target audience and what are scientific applications of this package?
      BlockingPy is targeted for data scientists, researchers, and analysts working with large datasets that require record matching or deduplication and need a scalable approach.

    • Are there other Python packages that accomplish the same thing? If so, how does yours differ?
      There are many packages around Record Linkage, however ours specializes in the blocking task and uses novel approach which is the use of ANN algorithms.

    • If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:
      No inquiry was made

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

  • does not violate the Terms of Service of any service it interacts with.
  • uses an OSI approved license.
  • contains a README with instructions for installing the development version.
  • includes documentation with examples for all functions.
  • contains a tutorial with examples of its essential functions and uses.
  • has a test suite.
  • has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

Publication Options

JOSS Checks
  • The package has an obvious research application according to JOSS's definition in their submission requirements. Be aware that completing the pyOpenSci review process does not guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS.
  • The package is not a "minor utility" as defined by JOSS's submission requirements: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria.
  • The package contains a paper.md matching JOSS's requirements with a high-level description in the package root or in inst/.
  • The package is deposited in a long-term repository with the DOI:

Note: JOSS accepts our review as theirs. You will NOT need to go through another full review. JOSS will only review your paper.md file. Be sure to link to this pyOpenSci issue when a JOSS issue is opened for your package. Also be sure to tell the JOSS editor that this is a pyOpenSci reviewed package once you reach this step.

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

  • Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Confirm each of the following by checking the box.

  • I have read the author guide.
  • I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

Please fill out our survey

P.S. Have feedback/comments about our review process? Leave a comment here

Editor and Review Templates

The editor template can be found here.

The review template can be found here.

Footnotes

  1. Please fill out a pre-submission inquiry before submitting a data visualization package.

@coatless
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coatless commented Jan 21, 2025

Editor in Chief checks

Hi there! Thank you for submitting your package for pyOpenSci
review. Below are the basic checks that your package needs to pass
to begin our review. If some of these are missing, we will ask you
to work on them before the review process begins.

Please check our Python packaging guide for more information on the elements
below.

  • Installation The package can be installed from a community repository such as PyPI (preferred), and/or a community channel on conda (e.g. conda-forge, bioconda).
    • The package imports properly into a standard Python environment import package.
  • Fit The package meets criteria for fit and overlap.
  • Documentation The package has sufficient online documentation to allow us to evaluate package function and scope without installing the package. This includes:
    • User-facing documentation that overviews how to install and start using the package.
    • Short tutorials that help a user understand how to use the package and what it can do for them.
    • API documentation (documentation for your code's functions, classes, methods and attributes): this includes clearly written docstrings with variables defined using a standard docstring format.
  • Core GitHub repository Files
    • README The package has a README.md file with clear explanation of what the package does, instructions on how to install it, and a link to development instructions.
    • Contributing File The package has a CONTRIBUTING.md file that details how to install and contribute to the package.
    • Code of Conduct The package has a CODE_OF_CONDUCT.md file.
    • License The package has an OSI approved license.
      NOTE: We prefer that you have development instructions in your documentation too.
  • Issue Submission Documentation All of the information is filled out in the YAML header of the issue (located at the top of the issue template).
  • Automated tests Package has a testing suite and is tested via a Continuous Integration service.
  • Repository The repository link resolves correctly.
  • Package overlap The package doesn't entirely overlap with the functionality of other packages that have already been submitted to pyOpenSci.
  • Archive (JOSS only, may be post-review): The repository DOI resolves correctly.
  • Version (JOSS only, may be post-review): Does the release version given match the GitHub release (v1.0.0)?

  • Initial onboarding survey was filled out
    We appreciate each maintainer of the package filling out this survey individually. 🙌
    Thank you authors in advance for setting aside five to ten minutes to do this. It truly helps our organization. 🙌


Editor comments

BlockingPy is in pristine shape for moving forward with a review! Nice work on getting it packaged for Python and implemented. Happy to see both mlpack and the original note on the R blocking package being emphasized.

@lwasser lwasser moved this from pre-review-checks to seeking-editor in peer-review-status Jan 21, 2025
@T-Strojny
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Author

That's great to hear! Thank you for the feedback.

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