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neonutilities submission #213

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@cklunch

Description

@cklunch

Submitting Author: Claire Lunch (@cklunch)
All current maintainers: (@cklunch, @bhass-neon, @znickerson8)
Package Name: neonutilities
One-Line Description of Package: neonutilities is a package for accessing and wrangling data generated and published by the National Ecological Observatory Network.
Repository Link: https://github.com/NEONScience/NEON-utilities-python
Version submitted: v1.0.1
EiC: @cmarmo
Editor: @JuliMillan
Reviewer 1: @ethanwhite
Reviewer 2: @benjamindonnachie
Archive: DOI
JOSS DOI: TBD
Version accepted: v1.1.0
Date accepted (month/day/year): 05/ 10/2025


Code of Conduct & Commitment to Maintain Package

Description

The neonutilities Python package provides utilities for discovering, downloading, and working with data files published by the National Ecological Observatory Network (NEON). NEON data files can be downloaded from the NEON Data Portal or API. The neonutilities package includes wrapper functions for the API and functions to reformat and stack NEON tabular data for analysis. This is a Python-native adaptation of the heavily used neonUtilities R package.

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):

    • Who is the target audience and what are scientific applications of this package?

The target audience is scientists doing research using NEON data. The package enables programmatic workflows for data downloading, and provides a standardized way to merge the product-site-month data files NEON publishes, making them analysis-ready.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?

There is an incomplete package on PyPi here that was started in 2020 by a student at a coding camp. It doesn't appear to have been finished, and is not maintained. Some NEON users have developed their own code to do some of the functionality covered by neonutilities, but as far as I know none of them have shared it broadly.

  • 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:

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

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Footnotes

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

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