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[PRE REVIEW]: GeneralizIT: A Python Solution for Generalizability Theory Computations #7574
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Five most similar historical JOSS papers: GridTest: testing and metrics collection for Python simChef: High-quality data science simulations in R cmstatr: An R Package for Statistical Analysis of Composite Material measr: Bayesian psychometric measurement using Stan simstudy: Illuminating research methods through data generation |
👋 @adriennekline - Thanks for your submission. It's about 2.5x longer than recommended for a JOSS paper (250-1000 words). Please reduce your paper's length, perhaps moving content to the repository or documentation. You can use the command editorialbot commands need to be the first entry in a new comment. In addition, you could work on the possibly missing DOIs that editorialbot suggests, but note that some may be incorrect. Please feel free to make changes to your .bib file, then use the command You can also use the command |
@editorialbot Hi @danielskatz We've shortened the paper to reflect the most critical information. We don't currently have any DOI other than that provided via Arxiv for the preprint. |
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Five most similar historical JOSS papers: simChef: High-quality data science simulations in R measr: Bayesian psychometric measurement using Stan EFAtools: An R package with fast and flexible implementations of exploratory factor analysis tools DscoreApp: An user-friendly web application for computing the Implicit Association Test D-score UncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelines |
@danielskatz is it with JOSS for editorial review now? I've never submitted here before so am new to the process. Thanks! |
We've moved it to a more appropriate track, and the track editor will tell you about the next steps soon |
👋 @adriennekline - could you give me a little more background on the development history of your package? Your commit history looks pretty recent and I wanted to get a better idea of when you started this package and how long it has been public. Thanks! |
Hi @crvernon - certainly! This is a fairly new repository, but based on fundamental statistical tools (generalizability theory) that have previously existing in proprietary platforms : spss, sad, etc. but not in Python until now. The work on the code was started several months ago by myself and completed by my masters students with my supervision at northwestern University in the engineering dept. Hope that helps! Happy to answer more Qs too! |
Hi @crvernon, Does this make sense? Happy to answer any other Q's you may have. |
@editorialbot query scope Hi @adriennekline, thank you for your feedback. I believe I will run this through scope review with our larger editorial board since the development time has been very short. I will get back to you ASAP with a response. Thank you! |
Submission flagged for editorial review. |
thanks @crvernon There is no mention of 'duration' of repo being a prerequisite in the submission process. |
Hi @adriennekline it is listed here: https://joss.readthedocs.io/en/latest/submitting.html#substantial-scholarly-effort Specifically,
and also in the requirement bullet points here:
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@editorialbot reject 👋 @adriennekline - after further review with our editorial board, we have decided that your submission does not meet the scope requirements necessary to be reviewed by JOSS. Thank you for your submission and we wish you the best with your future endeavors. |
Paper rejected. |
HI @crvernon can you kindly provide some more information as to why this does not within JOSS's scope? Is it the duration of the repo? This constitutes open source software and a python package intended for broad statistical implementations, and would otherwise seem to fit within the scope. |
Submitting author: @adriennekline (Adrienne Kline)
Repository: https://github.com/tylerjsmith111/GeneralizIT
Branch with paper.md (empty if default branch): main
Version: 0.0.3
Editor: Pending
Reviewers: Pending
Managing EiC: Daniel S. Katz
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