Closed editorialbot closed 1 year ago
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
Software report:
github.com/AlDanial/cloc v 1.88 T=0.05 s (871.0 files/s, 101093.5 lines/s)
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Language files blank comment code
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Python 25 783 816 2363
TeX 1 18 0 203
YAML 6 40 24 160
Markdown 3 68 0 144
Jupyter Notebook 1 0 345 99
reStructuredText 6 21 52 27
DOS Batch 1 8 1 26
make 1 4 7 9
TOML 1 1 0 4
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SUM: 45 943 1245 3035
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gitinspector failed to run statistical information for the repository
Wordcount for paper.md
is 572
Reference check summary (note 'MISSING' DOIs are suggestions that need verification):
OK DOIs
- 10.1038/s41597-021-01109-0 is OK
- 10.1140/epjc/s10052-023-11633-5 is OK
- 10.1103/PhysRevD.107.076017 is OK
- 10.5281/zenodo.6975118 is OK
- 10.5281/zenodo.2603256 is OK
- 10.5281/zenodo.3164691 is OK
- 10.1103/PhysRevLett.123.041801 is OK
- 10.21468/SciPostPhys.7.1.014 is OK
- 10.1088/1361-6633/ac36b9 is OK
MISSING DOIs
- 10.1103/physrevd.108.036025 may be a valid DOI for title: Fast Point Cloud Generation with Diffusion Models in High Energy Physics
INVALID DOIs
- None
:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
@editorialbot add @jpata as reviewer
@jpata added to the reviewers list!
@smsharma, @jpata, @saforem2 Thanks for agreeing to review this submission! This is the review thread for the paper. All of our communications will happen here from now on. :+1:
As you can see above, you each should use the command @editorialbot generate my checklist
to create your review checklist. @editorialbot commands need to be the first thing in a new comment.
As you go over the submission, please check any items that you feel have been satisfied (and if you leave notes on each item that's even better). There are also links to the JOSS reviewer guidelines. I find it particularly helpful to also use the JOSS review criteria and review checklist docs as supplement/guides to the reviewer checklist @editorialbot will make for you.
The JOSS review is different from most other journals. Our goal is to work with the authors to help them meet our criteria instead of merely passing judgment on the submission. As such, reviewers are encouraged to submit issues and pull requests on the software repository. When doing so, please mention openjournals/joss-reviews#5789
so that a link is created to this Issue thread (and I can keep an eye on what is happening). Please also feel free to comment and ask questions on this thread. In my experience, it is better to post comments/questions/suggestions as you come across them instead of waiting until you've reviewed the entire package.
We aim for reviews to be completed within about 4 weeks. Please let me know if either of you require some more time (that's perfectly okay). We can also use @editorialbot to set automatic reminders if you know you'll be away for a known period of time.
Please feel free to ping me (@matthewfeickert) if you have any questions/concerns.
@editorialbot add @saforem2 as reviewer
@saforem2 added to the reviewers list!
@jpata, @saforem2 as the review will be checklist based please make sure to create your specific checklist by requresting @editorialbot to make it for you with
@editorialbot generate my checklist
Please review the reviewer guidelines (https://joss.readthedocs.io/en/latest/reviewer_guidelines.html) and feel free to ask questions at any time during the review process.
Sounds good. I've been OOO for a bit but will be back tomorrow.
I've been OOO for a bit but will be back tomorrow.
No worries. Today is a holiday in the US anyway, so I wasn't even expecting you to have seen this until later on in the week. :+1:
As all reviewers have their checklist generated now I'll have @editorialbot give us reminders in 3 weeks to follow up on the state of the review.
@editorialbot remind @smsharma in 3 weeks
Reminder set for @smsharma in 3 weeks
@editorialbot remind @jpata in 3 weeks
Reminder set for @jpata in 3 weeks
@editorialbot remind @saforem2 in 3 weeks
Reminder set for @saforem2 in 3 weeks
@matthewfeickert I declare a conflict of interest, as I have joint papers in the last four years with one of the coauthors (Javier Duarte).
From my point of view, the topic in the paper under review here is sufficiently different that I would be able to offer an impartial review, but let me know what is the editorial boards view on this.
I declare a conflict of interest, as I have joint papers in the last four years with one of the coauthors (Javier Duarte).
Thanks @jpata for disclosing this. Indeed, your nice work with Javier does put you in conflict given
As a reviewer (or editor), COIs are your present or previous association with any authors of a submission: recent (past four years) collaborators in funded research or work that is published
We appreciate you briniging this up early!
@editorialbot remove @jpata from reviewers
@jpata removed from the reviewers list!
Note we still have 2 reviewers so the review can continue uninterrupted.
:wave: @smsharma, please update us on how your review is going (this is an automated reminder).
:wave: @jpata, please update us on how your review is going (this is an automated reminder).
:wave: @saforem2, please update us on how your review is going (this is an automated reminder).
the reminder to me is probably a bug 🙂
the reminder to me is probably a bug 🙂
Indeed, it is. I don't think that there's a way to remove a reminder at the moment given the list of editorialbot commands.
Should be good to go!
JetNet provides an intuitive interface for accessing jet datasets and evaluating / computing losses when using them for machine learning applications in collider physics. The package has already impacted the field, having been used in several publications, and is starting to become a standard for benchmarking machine learning algorithms on a range of tasks involving structured particle physics datasets. It also lowers the barrier to entry for machine learning practitioners from adjacent fields looking to work with these kinds of datasets.
Domain-specific packages like this systematize the development and comparison of machine learning algorithms, which is super valuable, and also set an example for other domains where machine learning is starting to be widely applied. Congrats to the authors for a nice contribution, which I can easily recommend for publication in JOSS, considering the minor comments below.
I was able to install the package without problems using the instructions in the documentation. I was also able to run the unit tests using pytest
.
A couple suggestions in particular re: the unit test pipeline:
energyflow
) with pytest
, as part of the installation instructions. Usage instructions are provided through a combination of a Quickstart guide, API documentation, and a tutorial notebook. I was able to test out basic data loading, evals, and loss function computation using these.
A more centralized exposition of various capabilities through e.g. tutorial notebooks covering different usage aspects, cross-published in the docs, could be helpful for users looking to either use JetNet for a particular thing or to gain general familiarity, although the current documentation is certainly adequate.
Given the scope of the package, it is well-suited for external contributions and development milestones. More specific wording on contributions and expectations for contributing would be welcome. This could be as simple as an invitation to open an issue on GitHub for bugs or functionality requests, or a more comprehensive CONTRIBUTING.md
file.
Thank you @jpata, @saforem2, and @smsharma for the reviews! I've opened issues for the suggestions and we will address them shortly.
I believe all comments have been addressed, @matthewfeickert please let us know how to proceed.
Thanks @rkansal47 for implementing the suggested changes. I've now gone ahead and marked the remaining checklist items. Congrats again to the authors for a nice package!
@editorialbot generate pdf
:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
@editorialbot check references
Reference check summary (note 'MISSING' DOIs are suggestions that need verification):
OK DOIs
- 10.1038/s41597-021-01109-0 is OK
- 10.1140/epjc/s10052-023-11633-5 is OK
- 10.1103/PhysRevD.107.076017 is OK
- 10.5281/zenodo.6975118 is OK
- 10.5281/zenodo.2603256 is OK
- 10.5281/zenodo.3164691 is OK
- 10.1103/PhysRevLett.123.041801 is OK
- 10.21468/SciPostPhys.7.1.014 is OK
- 10.1088/1361-6633/ac36b9 is OK
MISSING DOIs
- 10.21468/scipostphys.15.4.130 may be a valid DOI for title: EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
- 10.1103/physrevd.108.036025 may be a valid DOI for title: Fast Point Cloud Generation with Diffusion Models in High Energy Physics
INVALID DOIs
- None
Thank you both very much for your reviews @saforem2 and @smsharma!
@rkansal47 I'll start the editoral pass this week and we can hopefully conclude things in the near future. :+1: Can you verify if the missing DOIs caught by @editorialbot are valid or not?
@editorialbot set <DOI here> as archive
@editorialbot set <version here> as version
@editorialbot generate pdf
@editorialbot check references
and ask author(s) to update as needed@editorialbot recommend-accept
@matthewfeickert I have verified the DOIs and updated the references.
@editorialbot check references
Reference check summary (note 'MISSING' DOIs are suggestions that need verification):
OK DOIs
- 10.1038/s41597-021-01109-0 is OK
- 10.1140/epjc/s10052-023-11633-5 is OK
- 10.1103/PhysRevD.107.076017 is OK
- 10.21468/SciPostPhys.15.4.130 is OK
- 10.1103/PhysRevD.108.036025 is OK
- 10.5281/zenodo.6975118 is OK
- 10.5281/zenodo.2603256 is OK
- 10.5281/zenodo.3164691 is OK
- 10.1103/PhysRevLett.123.041801 is OK
- 10.21468/SciPostPhys.7.1.014 is OK
- 10.1088/1361-6633/ac36b9 is OK
MISSING DOIs
- None
INVALID DOIs
- None
@editorialbot generate pdf
:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
@rkansal47 While going through the editorial review there were some minor things that I raised as https://github.com/jet-net/JetNet/issues/53 and https://github.com/jet-net/JetNet/issues/54. Otherwise things are looking very good.
Once those have been addressed, all that's left to do now before publication is to ensure that there is a long term public archive of the code that was reviewed. In this case the code was JetNet
v0.2.3
though there has been some development between then and the current HEAD
, so we'd suggest making a new release (as you see fit with respect to SemVer).
We'd suggest depositing the code either with Zenodo or with figshare to get an archive with a DOI. If you use Zenodo there is an (optional) GitHub integration that can create a Zenodo archive for you anytime you make a GitHub release of your code.
Howerver, I think you already know this as you have a Zenodo DOI in your README (Zendo is down for upgrades as I write this, otherwise I would actually check it)!
Once you make a new release of JetNet
and have a DOI for the archive please just report them here and I'll have @editorialbot update the version listed in the review and add the archive.
Let me know if you have any questions. :+1:
Submitting author: !--author-handle-->@rkansal47<!--end-author-handle-- (Raghav Kansal) Repository: https://github.com/jet-net/JetNet Branch with paper.md (empty if default branch): JOSS-2023 Version: v0.2.4 Editor: !--editor-->@matthewfeickert<!--end-editor-- Reviewers: @smsharma, @saforem2 Archive: 10.5281/zenodo.10044601
Status
Status badge code:
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
@smsharma, 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:
The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @matthewfeickert 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
📝 Checklist for @smsharma
📝 Checklist for @jpata
📝 Checklist for @saforem2