[REVIEW]: lpjmlkit: A toolkit for operating LPJmL and model-specific data processing about joss-reviews HOT 11 OPEN

Thanks a lot @graciellehigino and @tonyewong!

And yes, @tonyewong, you are right - it should not include too much of the API descriptions as mentioned here. I rather wanted to highlight the key features that define lpjmlkit, so one get a better understanding of what is an LPJmL kit and what is meant by that in R.
I will try to abstract that more and rather give example usages for ongoing research projects instead.

Regarding other models that can be cited - MAgPIE and LPJ-GUESS are both good examples, one from a different discipline, but same research institute (MAgPIE) and one from the same discipline but another research institute (LPJ-GUESS). Both examples have a similar story of how research has been conducted over the last decade and what tools have had to be developed to keep it state of the art. Therefore, I do not believe that adding more examples will contribute to a better understanding of this issue.

Can you also already tick the boxes that can easily be excluded from the scope of this work, such as data sharing and research on humans and animals? 🙂
Please let me know if there is anything else I can do to move this forward.

Many thanks in advance 🙏

from joss-reviews.

Hello humans, I'm @editorialbot, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

@editorialbot commands

For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

@editorialbot generate pdf

from joss-reviews.

Software report:

github.com/AlDanial/cloc v 1.88  T=0.05 s (1473.5 files/s, 287131.5 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
R                               57           1346           3333           7095
Markdown                         5            178              0            887
JavaScript                       2             21             38            502
JSON                             6              0              0            423
TeX                              1             17              0            268
YAML                             4             19             10            151
Rmd                              2            137            606            123
make                             1             11              7             27
-------------------------------------------------------------------------------
SUM:                            78           1729           3994           9476
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

from joss-reviews.

Wordcount for paper.md is 1896

from joss-reviews.

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.5194/gmd-12-5029-2019 is OK
- 10.1038/s41893-019-0465-1 is OK
- 10.1016/j.jhydrol.2003.09.029 is OK
- 10.1016/j.envsoft.2018.09.004 is OK
- 10.1111/j.1365-2486.2006.01305.x is OK
- 10.5194/gmd-11-429-2018 is OK
- 10.5194/gmd-11-2789-2018 is OK
- 10.5194/gmd-12-1299-2019 is OK
- 10.5194/gmd-12-2419-2019 is OK
- 10.5194/bg-19-957-2022 is OK
- 10.5194/esd-12-1037-2021 is OK
- 10.1371/journal.pbio.1001745 is OK
- 10.1046/j.1365-2486.2003.00569.x is OK
- 10.1038/s41597-022-01710-x is OK
- 10.5194/gmd-11-1343-2018 is OK
- 10.1046/j.1365-2486.2001.00383.x is OK

MISSING DOIs

- None

INVALID DOIs

- None

from joss-reviews.

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

from joss-reviews.

Review checklist for @tonyewong

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the https://github.com/PIK-LPJmL/lpjmlkit?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@jnnsbrr) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

from joss-reviews.

I had a read through the accompanying paper, which is well-written and helpful. I know JOSS papers are supposed to not include too much of the API descriptions, which instead should be in the software documentation on GitHub. I'm not sure but I'm curious whether some areas of the paper might be better left to more extensive documentation in GitHub? For example lines 78-89 or 93-101.

from joss-reviews.

Also related to the paper -- are there other models that can be cited, beyond MAgPIE and LPJ-GUESS, that have a similar focus? Are there other similarities/differences that can be noted in the paper?

from joss-reviews.

Thank you for your comments, @tonyewong !

@jnnsbrr feel free to make edits and reply to the comments anytime. [=

@egouldo did you have a chance to take a look at this submission yet? Let me know if you need anything! [=

from joss-reviews.

Hello all, and apologies for the (big!) delay! We had summer interns starting over the past couple weeks so things were chaotic. But I'm back, and alive, as far as I can tell...!

I'm working on installing and running some tests. Installing goes smoothly (great!) and I see where in the GitHub repository there is a tests directory with relevant output directories too. Are these what the vignettes are referring to in their relative file paths? Can some further instructions be added about how to run some example cases and benchmark tests?

It looks like everything we need is already in the GitHub repo, so this is probably pretty straightforward to do without the user needing to clone the GitHub.

from joss-reviews.