[REVIEW]: ECNet: Large scale machine learning projects for fuel property prediction

[whedon]

Submitting author: @tjkessler (Travis Kessler)
Repository: https://github.com/TJKessler/ECNet
Version: v1.2.4.dev1
Editor: @arokem
Reviewer: @krother
Archive: 10.5281/zenodo.910866

Status

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Reviewers and authors:

Please avoid lengthy details of difficulties in the review thread. Instead, please create a new issue in the target repository and link to those issues (especially acceptance-blockers) in the review thread below. (For completists: if the target issue tracker is also on GitHub, linking the review thread in the issue or vice versa will create corresponding breadcrumb trails in the link target.)

Reviewer questions

@krother, please carry out your review in this issue by updating the checklist below (please make sure you're logged in to GitHub). The reviewer guidelines are available here: http://joss.theoj.org/about#reviewer_guidelines. Any questions/concerns please let @arokem know.

Conflict of interest

• As the reviewer I confirm that I have read the JOSS conflict of interest policy and that there are no conflicts of interest for me to review this work.

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 repository url?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Version: Does the release version given match the GitHub release (v1.2.4.dev1)?
• Authorship: Has the submitting author (@tjkessler) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?

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 function 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

• Authors: Does the paper.md file include a list of authors with their affiliations?
• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• References: Do all archival references that should have a DOI list one (e.g., papers, datasets, software)?

[whedon]

Hello human, I'm @whedon. I'm here to help you with some common editorial tasks for JOSS. @krother it looks like you're currently assigned as the reviewer for this paper 🎉.

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If you haven't already, you should seriously consider unsubscribing from GitHub notifications for this (https://github.com/openjournals/joss-reviews) repository. As as reviewer, you're probably currently watching this repository which means for GitHub's default behaviour you will receive notifications (emails) for all JOSS reviews 😿

To fix this do the following two things:

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2. You may also like to change your default settings for this watching repositories in your GitHub profile here: https://github.com/settings/notifications

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

@whedon commands

[krother]

Thanks for the quick and precise repsonse. All issues resolved. Good job!

[krother]

@arokem I suggest to publish this article.

[tjkessler]

@krother - thank you for taking the time to review ECNet!

[arokem]

Thank you both for the quick turnaround here. @tjkessler : Your next step is to make an archive from the current version (e.g., using Zenodo) and to post the DOI of the archive here.

[tjkessler]

@arokem - here is the archive DOI: 10.5281/zenodo.893638

[arfon]

@tjkessler - Could you move the references you currently have in the paper.md file into a paper.bib file and cite them directly please? (You can read how to do that here)

[tjkessler]

@arfon - all references in paper.md are now included in paper.bib.

[arfon]

Thanks @tjkessler - this is nearly right but not quite there. Please remove the content below the #References section in your paper.md (https://github.com/TJKessler/ECNet/blob/master/paper/paper.md#references) and cite the entries in your paper.bib as described here. That way, the citations are compiled into the References section when the PDF is created by Pandoc.

[tjkessler]

@arfon - I think I got it this time, let me know if it is still off.

[arfon]

@arfon - I think I got it this time, let me know if it is still off.

Nearly! Please merge ecrl/ecnet#7. Then, could you make an archive of the reviewed software in Zenodo/figshare/other service and update this thread with the DOI of the archive? I can then move forward with accepting the submission.

[tjkessler]

@arfon - ECNet#7 has been merged, here is the updated DOI: 10.5281/zenodo.910866

[arfon]

@whedon set 10.5281/zenodo.910866 as archive

[whedon]

OK. 10.5281/zenodo.910866 is the archive.

[arfon]

@krother - many thanks for your review here and to @arokem for editing this submission ✨

@tjkessler - your paper is now accepted into JOSS and your DOI is http://dx.doi.org/10.21105/joss.00401 ⚡️ 🚀 💥