Description
Submitting Author: Sam Pottinger (@sampottinger)
All current maintainers: @sampottinger, @gizarp
Package Name: afscgap
One-Line Description of Package: Community contributed Python-based tools for working with public bottom trawl surveys data from the NOAA Alaska Fisheries Science Center Groundfish Assessment Program (NOAA AFSC GAP).
Repository Link: https://github.com/SchmidtDSE/afscgap
Version submitted: 0.0.7
Editor: @ocefpaf
Reviewer 1: @7yl4r
Reviewer 2: @ayushanand18
Archive:
JOSS DOI:
Version accepted: 0.0.7
Date accepted (month/day/year): 05-30-2023
Code of Conduct & Commitment to Maintain Package
- I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
- I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.
Description
Python-based tool set for interacting with bottom trawl surveys from the Ground Fish Assessment Program (GAP). This provides information about where certain species were seen and when under what conditions, information useful for research in ocean health.
It offers:
- Pythonic access to the official NOAA AFSC GAP API service.
- Memory-efficient tools for inference of the "negative" observations not provided by the API service but required for some common types of analysis.
- Visualization tools for quickly exploring and creating comparisons within the dataset that provide an "on-ramp" to deeper analysis. The visual analytics components aim to serve both expert programmers and audiences with limited programming experience.
Note that GAP is an excellent dataset produced by the Resource Assessment and Conservation Engineering (RACE) Division of the Alaska Fisheries Science Center (AFSC) as part of the National Oceanic and Atmospheric Administration's Fisheries organization (NOAA Fisheries). See also the RACEBASE NOAA InPort entry.
Additional information at https://pyafscgap.org/.
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 visualization
- Workflow automation
- Citation management and bibliometrics
- Scientific software wrappers
- Database interoperability
related to data viz category: see presubmission
Domain Specific & Community Partnerships
- Geospatial
- Education
- Pangeo
Community Partnerships
If your package is associated with an
existing community please check below:
- Pangeo
- My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook
- For all submissions, explain how the 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):
This library supports retrieval of data from the official NOAA AFSC GAP REST API service but, as that service on its own is often not sufficient due to certain data limitations, it also offers zero catch inference as required for a number of common types of investigations (hence data processing). Finally, given the needs of the community and the vast breadth of the dataset, it offers a community application to explore the data which, in turn, can generate Python code to help users get started with continued analysis within their own scripts.
- Who is the target audience and what are scientific applications of this package?
This project largely benefits scientific researchers in the ocean health space as this dataset is useful for fisheries management, biodiversity research, and marine science more generally. An example of what this analysis may look like is provided in our example notebook hosted on mybinder.org.
- Are there other Python packages that accomplish the same thing? If so, how does yours differ?
We are not aware of other Python packages working with the AFSC GAP dataset.
- If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or
@tagthe editor you contacted:
Please see our presubmission.
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.
See NOAA InPort entry. We are primarily using Distribution 6 in the API form. As described, these data are available with the following access constraints: "There are no legal restrictions on access to the data. They reside in public domain and can be freely distributed."
- uses an OSI approved license.
See BSD License within repo.
- contains a README with instructions for installing the development version.
See project README.md.
- includes documentation with examples for all functions.
See project website and usage section of project website's documentation microsite.
- contains a tutorial with examples of its essential functions and uses.
See tutorial notebook on mybinder.
- has a test suite.
See main tests for package and supplemental tests for website (backend tests and frontend tests).
- has continuous integration setup, such as GitHub Actions CircleCI, and/or others.
See CI and CD (library, documentation).
Publication Options
- Do you wish to automatically submit to the Journal of Open Source Software? If so:
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.
See example notebook and app intro.
- 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.
In addition to query compilation / emulation, we are hopeful that the negative record inference and complementing visualization tool are enough to escape the thin API client status. Coming in at over 10k lines of code, we would seem to fit some of the "hard" criteria that the journal puts forward. Though this library happily provides Pythonic access to an API service, we believe that this contribution as a whole goes beyond a thin client by providing one of the only public mechanisms for conducting investigation requiring negative catch data and provides unique tools for comparative analysis within with the dataset.
- The package contains a
paper.mdmatching JOSS's requirements with a high-level description in the package root or ininst/.
See https://github.com/SchmidtDSE/afscgap/blob/main/inst/paper.md (or PDF preview at https://github.com/SchmidtDSE/afscgap/blob/main/inst/paper.pdf).
- The package is deposited in a long-term repository with the DOI:
We have submitted to Code Ocean. Please confirm if this will suffice. See 10.24433/CO.4905407.v1 / https://codeocean.com/capsule/4905407/tree/v1
Note: Do not submit your package separately to JOSS
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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.
We would be delighted to have your pull requests! 🎉
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.
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