[REVIEW]: Machine Learning Validation via Rational Dataset Sampling with astartes

[editorialbot]

Submitting author: @JacksonBurns (Jackson Burns)
Repository: https:/JacksonBurns/astartes
Branch with paper.md (empty if default branch): joss-paper
Version: v1.1.3.post1
Editor: @arfon
Reviewers: @du-phan, @BerylKanali
Archive: 10.5281/zenodo.8147205

Status

Status badge code:

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Markdown: [![status](https://joss.theoj.org/papers/8a9cfc71d6f75410b06510a646d5f783/status.svg)](https://joss.theoj.org/papers/8a9cfc71d6f75410b06510a646d5f783)

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) by leaving comments 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 instructions & questions

@du-phan & @BerylKanali, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review.
First of all you need to run this command in a separate comment to create the checklist:

@editorialbot generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @arfon know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

@du-phan, please create your checklist typing: @editorialbot generate my checklist

@BerylKanali, please create your checklist typing: @editorialbot generate my checklist

[editorialbot]

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

[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.88  T=0.17 s (567.5 files/s, 230736.8 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
JavaScript                      12           2405           2470           9221
Python                          41            499            683           3771
SVG                              2              0              0           2672
Jupyter Notebook                 6              0          13325            956
CSS                              4            191             35            754
Markdown                        11            209              0            739
YAML                            10             32              8            385
TeX                              1             34             52            299
reStructuredText                 6             50             85             56
TOML                             1              7              0             40
DOS Batch                        1              8              1             26
make                             1              4              7              9
-------------------------------------------------------------------------------
SUM:                            96           3439          16666          18928
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 1917

[editorialbot]

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

[editorialbot]

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

OK DOIs

- 10.5281/zenodo.6618262 is OK

MISSING DOIs

- 10.1021/acs.chemmater.0c01907.s001 may be a valid DOI for title: Machine Learning for Materials Scientists: An Introductory Guide Toward Best Practices
- 10.1038/sdata.2014.22 may be a valid DOI for title: Quantum Chemistry Structures and Properties of 134 Kilo Molecules
- 10.1021/ci300415d may be a valid DOI for title: Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
- 10.1038/s41597-022-01529-6 may be a valid DOI for title: High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions
- 10.1021/acs.jpca.2c02614 may be a valid DOI for title: Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy
- 10.1039/d1cp04422b may be a valid DOI for title: Progress Towards Machine Learning Reaction Rate Constants
- 10.1039/c8me00012c may be a valid DOI for title: Can Machine Learning Identify the Next High-Temperature Superconductor? Examining Extrapolation Performance for Materials Discovery
- 10.1039/d2dd00039c may be a valid DOI for title: Random Projections and Kernelised Leave One Cluster Out Cross Validation: Universal Baselines and Evaluation Tools for Supervised Machine Learning of Material Properties
- 10.26434/chemrxiv-2022-m8l33-v2 may be a valid DOI for title: Construction of Balanced, Chemically Dissimilar Training, Validation and Test Sets for Machine Learning on Molecular Datasets
- 10.1039/d2sc06150c may be a valid DOI for title: Low-Cost Machine Learning Prediction of Excited State Properties of Iridium-Centered Phosphors
- 10.1063/5.0079574 may be a valid DOI for title: Quantum Chemistry-Augmented Neural Networks for Reactivity Prediction: Performance, Generalizability, and Explainability
- 10.1063/5.0059742 may be a valid DOI for title: Toward the Design of Chemical Reactions: Machine Learning Barriers of Competing Mechanisms in Reactant Space
- 10.2139/ssrn.4289793 may be a valid DOI for title: Machine Learning for Predicting the Viscosity of Binary Liquid Mixtures
- 10.26434/chemrxiv.12758498 may be a valid DOI for title: Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies
- 10.1021/jm9602928 may be a valid DOI for title: The Properties of Known Drugs. 1. Molecular Frameworks
- 10.1021/c160017a018 may be a valid DOI for title: The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service
- 10.1021/ci100050t may be a valid DOI for title: Extended-Connectivity Fingerprints
- 10.1021/acs.jcim.9b00237.s001 may be a valid DOI for title: Analyzing Learned Molecular Representations for Property Prediction

INVALID DOIs

- https://doi.org/10.1016/j.cpc.2022.108579 is INVALID because of 'https://doi.org/' prefix

[arfon]

@du-phan, @BerylKanali – just to confirm, there's no additional review to do here as you completed a review over on pyOpenSci.

[arfon]

@JacksonBurns – I'll need to do a quick check of your paper but in the interim, could you please take a look at the DOI suggestions from @editorialbot and see if any of them are matches? If they are, please add them to your BibTeX file.

[JacksonBurns]

@JacksonBurns – I'll need to do a quick check of your paper but in the interim, could you please take a look at the DOI suggestions from @editorialbot and see if any of them are matches? If they are, please add them to your BibTeX file.

@arfon done! I have confirmed that the paper still compiles, as well.

Quite a handy bot! 🤖

[arfon]

@editorialbot check references

[editorialbot]

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

OK DOIs

- 10.1021/acs.chemmater.0c01907.s001 is OK
- 10.1038/sdata.2014.22 is OK
- 10.1021/ci300415d is OK
- 10.1038/s41597-022-01529-6 is OK
- 10.5281/zenodo.6618262 is OK
- 10.1021/acs.jpca.2c02614 is OK
- 10.1039/d1cp04422b is OK
- 10.1039/d1cp04422b is OK
- 10.1039/d2dd00039c is OK
- 10.26434/chemrxiv-2022-m8l33-v2 is OK
- 10.1039/d2sc06150c is OK
- 10.1063/5.0079574 is OK
- 10.1063/5.0059742 is OK
- 10.2139/ssrn.4289793 is OK
- 10.26434/chemrxiv.12758498 is OK
- 10.1021/jm9602928 is OK
- 10.1021/c160017a018 is OK
- 10.1021/ci100050t is OK
- 10.1021/acs.jcim.9b00237.s001 is OK
- 10.1016/j.cpc.2022.108579 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[arfon]

@editorialbot set 10.5281/zenodo.8147205 as archive

[editorialbot]

Done! archive is now 10.5281/zenodo.8147205

[arfon]

@editorialbot recommend-accept

[editorialbot]

Attempting dry run of processing paper acceptance...

[editorialbot]

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

OK DOIs

- 10.1021/acs.chemmater.0c01907.s001 is OK
- 10.1038/sdata.2014.22 is OK
- 10.1021/ci300415d is OK
- 10.1038/s41597-022-01529-6 is OK
- 10.5281/zenodo.6618262 is OK
- 10.1021/acs.jpca.2c02614 is OK
- 10.1039/d1cp04422b is OK
- 10.1039/d1cp04422b is OK
- 10.1039/d2dd00039c is OK
- 10.26434/chemrxiv-2022-m8l33-v2 is OK
- 10.1039/d2sc06150c is OK
- 10.1063/5.0079574 is OK
- 10.1063/5.0059742 is OK
- 10.2139/ssrn.4289793 is OK
- 10.26434/chemrxiv.12758498 is OK
- 10.1021/jm9602928 is OK
- 10.1021/c160017a018 is OK
- 10.1021/ci100050t is OK
- 10.1021/acs.jcim.9b00237.s001 is OK
- 10.1016/j.cpc.2022.108579 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[editorialbot]

👋 @openjournals/dsais-eics, this paper is ready to be accepted and published.

Check final proof 👉📄 Download article

If the paper PDF and the deposit XML files look good in openjournals/joss-papers#4751, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

[arfon]

@editorialbot accept

[editorialbot]

Doing it live! Attempting automated processing of paper acceptance...

[editorialbot]

Ensure proper citation by uploading a plain text CITATION.cff file to the default branch of your repository.

If using GitHub, a Cite this repository menu will appear in the About section, containing both APA and BibTeX formats. When exported to Zotero using a browser plugin, Zotero will automatically create an entry using the information contained in the .cff file.

You can copy the contents for your CITATION.cff file here:

CITATION.cff

cff-version: "1.2.0"
authors:
- family-names: Burns
  given-names: Jackson W.
  orcid: "https://orcid.org/0000-0002-0657-9426"
- family-names: Spiekermann
  given-names: Kevin A.
  orcid: "https://orcid.org/0000-0002-9484-9253"
- family-names: Bhattacharjee
  given-names: Himaghna
  orcid: "https://orcid.org/0000-0002-6598-3939"
- family-names: Vlachos
  given-names: Dionisios G.
  orcid: "https://orcid.org/0000-0002-6795-8403"
- family-names: Green
  given-names: William H.
  orcid: "https://orcid.org/0000-0003-2603-9694"
contact:
- family-names: Burns
  given-names: Jackson W.
  orcid: "https://orcid.org/0000-0002-0657-9426"
doi: 10.5281/zenodo.8147205
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Burns
    given-names: Jackson W.
    orcid: "https://orcid.org/0000-0002-0657-9426"
  - family-names: Spiekermann
    given-names: Kevin A.
    orcid: "https://orcid.org/0000-0002-9484-9253"
  - family-names: Bhattacharjee
    given-names: Himaghna
    orcid: "https://orcid.org/0000-0002-6598-3939"
  - family-names: Vlachos
    given-names: Dionisios G.
    orcid: "https://orcid.org/0000-0002-6795-8403"
  - family-names: Green
    given-names: William H.
    orcid: "https://orcid.org/0000-0003-2603-9694"
  date-published: 2023-11-05
  doi: 10.21105/joss.05996
  issn: 2475-9066
  issue: 91
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 5996
  title: Machine Learning Validation via Rational Dataset Sampling with
    astartes
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.05996"
  volume: 8
title: Machine Learning Validation via Rational Dataset Sampling with
  `astartes`

If the repository is not hosted on GitHub, a .cff file can still be uploaded to set your preferred citation. Users will be able to manually copy and paste the citation.

Find more information on .cff files here and here.

[editorialbot]

🐘🐘🐘 👉 Toot for this paper 👈 🐘🐘🐘

[editorialbot]

🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSS! 🚨🚨🚨

Here's what you must now do:

0. Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.05996 joss-papers#4752
1. Wait five minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.05996
2. If everything looks good, then close this review issue.
3. Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

[arfon]

@JacksonBurns – your paper is now accepted and published in JOSS ⚡🚀💥

[editorialbot]

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

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[JacksonBurns]

Thank you so much for the speedy review @arfon!