Comments (4)
As I understand it, we're encouraged to have these discussions as we go, rather than waiting for the review to conclude.
That is also how I understand it yes, so feel free to interact with me or push changes while I'm busy, I'll then try and look at the changes when I have time :)
As for the clarifying replies, these are all really good points, and I would consider adding these in some integrated way to the paper a really good way to resolve my comments. For me this is mostly about highlighting the work you have done already
We have also been cautious in specifically saying what other packages can't do, or can't do as effectively, both to avoid implied criticism of others' work and also for clarity. We have avoided 'stating the negative' about what other packages don't do. No other package meets all the elements of our statement of need.
That is a sensible position to have. I am still on the opinion that pointing out the "weaknesses" in prior work and how this compares to your own is part of the academic process and helps newcomers to your package make an informed decision on whether your package is the right one for them, and that this can be done without being demeaning or overly negative (though that is certainly hard). I don't want to push you to make claims you're not comfortable making, but if possible I'd suggest to give this another pass. I don't consider it a hard requirement for the publication though.
from scores.
I'm happy with the updated submission, I think it highlights the strengths of scores
a lot better. Well done :)
from scores.
Thanks for these comments. As I understand it, we're encouraged to have these discussions as we go, rather than waiting for the review to conclude. We'd be very happy to amend our statement of need to address these comments. We agree the paper needs to make its points clearly. It's very helpful to get a new perspective.
I wanted to provide a brief clarifying reply, to begin answering your questions as you proceed with the rest of the review.
- Scores uses xarray, which is the most commonly used package to support the labelled, n-dimensional data utilised by the meteorological and Earth system community. Verification/stats/metrics packages that are used commonly in data science often only support 1-D data, tabular data, or unlabelled n-dimensional data (see "Data Handling" in statement of need).
- Scores handles dimensionality preferably to most other meteorological packages, and weighting (such as latitude-weighting) preferably to many general data science packages
- Scores provides some novel metrics, developed by our team, which aren't in any other package.
- Commonly-used scores are also provided for completeness and consistency, so
scores
can be used standalone, and also to meet frequent user requests for their inclusion. - It would typically take several packages to achieve most of what scores provides - and even then would not provide all the key benefits of scores.
We will take some more time to consider how to improve the paper to incorporate your feedback and respond further when we have done so.
We have also been cautious in specifically saying what other packages can't do, or can't do as effectively, both to avoid implied criticism of others' work and also for clarity. We have avoided 'stating the negative' about what other packages don't do. No other package meets all the elements of our statement of need.
from scores.
We have updated the paper (paper.md) on branch 528-joss-paper-submission so it is now ready for further review. We have also made some updates to the documentation on the develop branch which has not made its way yet to main (see https://scores.readthedocs.io/en/latest)
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Related Issues (20)
- [JOSS] Instructions for downloading example data HOT 1
- [JOSS] General explanation of reduce/preserve HOT 3
- [JOSS] Minimal pandas support HOT 4
- [JOSS] Implementation of weights is occasionally unclear HOT 1
- rename correlation HOT 1
- Badges, CI and forks HOT 2
- roc_curve_data API rendering in readthedocs HOT 1
- Add threshold weighted scores
- Go through and add thumbnails to each tutorial gallery entry
- Kling-Gupta efficiency and percentage bias HOT 12
- Add link to JOSS article towards start of README HOT 5
- Brier Score - References in API & Tutorial
- Relative Economic value
- Badges for PyPI and Conda
- twcrps for ensembles
- Update Maintainers Notes
- Mathjax: x and y vs forecast and observed
- reprojection tutorial
- scipy isotonic regression
- Add JOSS markdown badge
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