-
Notifications
You must be signed in to change notification settings - Fork 19
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #5709 from openjournals/joss.06450
Merging automatically
- Loading branch information
Showing
6 changed files
with
743 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,259 @@ | ||
<?xml version="1.0" encoding="UTF-8"?> | ||
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1" | ||
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" | ||
xmlns:rel="http://www.crossref.org/relations.xsd" | ||
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" | ||
version="5.3.1" | ||
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd"> | ||
<head> | ||
<doi_batch_id>20240730123728-6bd882411a1322ab21620b12f1aea7f028e42cfd</doi_batch_id> | ||
<timestamp>20240730123728</timestamp> | ||
<depositor> | ||
<depositor_name>JOSS Admin</depositor_name> | ||
<email_address>admin@theoj.org</email_address> | ||
</depositor> | ||
<registrant>The Open Journal</registrant> | ||
</head> | ||
<body> | ||
<journal> | ||
<journal_metadata> | ||
<full_title>Journal of Open Source Software</full_title> | ||
<abbrev_title>JOSS</abbrev_title> | ||
<issn media_type="electronic">2475-9066</issn> | ||
<doi_data> | ||
<doi>10.21105/joss</doi> | ||
<resource>https://joss.theoj.org</resource> | ||
</doi_data> | ||
</journal_metadata> | ||
<journal_issue> | ||
<publication_date media_type="online"> | ||
<month>07</month> | ||
<year>2024</year> | ||
</publication_date> | ||
<journal_volume> | ||
<volume>9</volume> | ||
</journal_volume> | ||
<issue>99</issue> | ||
</journal_issue> | ||
<journal_article publication_type="full_text"> | ||
<titles> | ||
<title>SeqMetrics: a unified library for performance metrics | ||
calculation in Python</title> | ||
</titles> | ||
<contributors> | ||
<person_name sequence="first" contributor_role="author"> | ||
<given_name>Fazila</given_name> | ||
<surname>Rubab</surname> | ||
<ORCID>https://orcid.org/0009-0004-9040-3459</ORCID> | ||
</person_name> | ||
<person_name sequence="additional" | ||
contributor_role="author"> | ||
<given_name>Sara</given_name> | ||
<surname>Iftikhar</surname> | ||
<ORCID>https://orcid.org/0000-0001-7446-6805</ORCID> | ||
</person_name> | ||
<person_name sequence="additional" | ||
contributor_role="author"> | ||
<given_name>Ather</given_name> | ||
<surname>Abbas</surname> | ||
<ORCID>https://orcid.org/0000-0002-0031-745X</ORCID> | ||
</person_name> | ||
</contributors> | ||
<publication_date> | ||
<month>07</month> | ||
<day>30</day> | ||
<year>2024</year> | ||
</publication_date> | ||
<pages> | ||
<first_page>6450</first_page> | ||
</pages> | ||
<publisher_item> | ||
<identifier id_type="doi">10.21105/joss.06450</identifier> | ||
</publisher_item> | ||
<ai:program name="AccessIndicators"> | ||
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref> | ||
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref> | ||
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref> | ||
</ai:program> | ||
<rel:program> | ||
<rel:related_item> | ||
<rel:description>Software archive</rel:description> | ||
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.12958902</rel:inter_work_relation> | ||
</rel:related_item> | ||
<rel:related_item> | ||
<rel:description>GitHub review issue</rel:description> | ||
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6450</rel:inter_work_relation> | ||
</rel:related_item> | ||
</rel:program> | ||
<doi_data> | ||
<doi>10.21105/joss.06450</doi> | ||
<resource>https://joss.theoj.org/papers/10.21105/joss.06450</resource> | ||
<collection property="text-mining"> | ||
<item> | ||
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06450.pdf</resource> | ||
</item> | ||
</collection> | ||
</doi_data> | ||
<citation_list> | ||
<citation key="Gleckler2008"> | ||
<article_title>Performance metrics for climate | ||
models</article_title> | ||
<author>Gleckler</author> | ||
<journal_title>Journal of Geophysical Research: | ||
Atmospheres</journal_title> | ||
<issue>D6</issue> | ||
<volume>113</volume> | ||
<doi>10.1029/2007JD008972</doi> | ||
<cYear>2008</cYear> | ||
<unstructured_citation>Gleckler, P. J., Taylor, K. E., & | ||
Doutriaux, C. (2008). Performance metrics for climate models. Journal of | ||
Geophysical Research: Atmospheres, 113(D6). | ||
https://doi.org/10.1029/2007JD008972</unstructured_citation> | ||
</citation> | ||
<citation key="Botchkarev"> | ||
<article_title>Performance metrics (error measures) in | ||
machine learning regression, forecasting and prognostics: Properties and | ||
typology</article_title> | ||
<author>Botchkarev</author> | ||
<journal_title>Interdisciplinary Journal of Information, | ||
Knowledge, and Management</journal_title> | ||
<volume>14</volume> | ||
<doi>10.28945/4184</doi> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Botchkarev, A. (2019). Performance | ||
metrics (error measures) in machine learning regression, forecasting and | ||
prognostics: Properties and typology. Interdisciplinary Journal of | ||
Information, Knowledge, and Management, 14, 45–76. | ||
https://doi.org/10.28945/4184</unstructured_citation> | ||
</citation> | ||
<citation key="chollet2015keras"> | ||
<article_title>Keras</article_title> | ||
<author>Chollet</author> | ||
<cYear>2015</cYear> | ||
<unstructured_citation>Chollet, F., & others. (2015). | ||
Keras. https://github.com/fchollet/keras; | ||
GitHub.</unstructured_citation> | ||
</citation> | ||
<citation key="detlefsen2022torchmetrics"> | ||
<article_title>TorchMetrics - measuring reproducibility in | ||
PyTorch</article_title> | ||
<author>Detlefsen</author> | ||
<journal_title>Journal of Open Source | ||
Software</journal_title> | ||
<issue>70</issue> | ||
<volume>7</volume> | ||
<doi>10.21105/joss.04101</doi> | ||
<cYear>2022</cYear> | ||
<unstructured_citation>Detlefsen, N. S., Borovec, J., | ||
Schock, J., Jha, A. H., Koker, T., Di Liello, L., Stancl, D., Quan, C., | ||
Grechkin, M., & Falcon, W. (2022). TorchMetrics - measuring | ||
reproducibility in PyTorch. Journal of Open Source Software, 7(70), | ||
4101. https://doi.org/10.21105/joss.04101</unstructured_citation> | ||
</citation> | ||
<citation key="pedregosa2011scikit"> | ||
<article_title>Scikit-learn: Machine learning in | ||
Python</article_title> | ||
<author>Pedregosa</author> | ||
<journal_title>the Journal of machine Learning | ||
research</journal_title> | ||
<volume>12</volume> | ||
<cYear>2011</cYear> | ||
<unstructured_citation>Pedregosa, F., Varoquaux, G., | ||
Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., | ||
Prettenhofer, P., Weiss, R., Dubourg, V., & others. (2011). | ||
Scikit-learn: Machine learning in Python. The Journal of Machine | ||
Learning Research, 12, 2825–2830. | ||
http://jmlr.org/papers/v12/pedregosa11a.html</unstructured_citation> | ||
</citation> | ||
<citation key="Kratzert2022"> | ||
<article_title>NeuralHydrology — a Python library for deep | ||
learning research in hydrology</article_title> | ||
<author>Kratzert</author> | ||
<journal_title>Journal of Open Source | ||
Software</journal_title> | ||
<issue>71</issue> | ||
<volume>7</volume> | ||
<doi>10.21105/joss.04050</doi> | ||
<cYear>2022</cYear> | ||
<unstructured_citation>Kratzert, F., Gauch, M., Nearing, G., | ||
& Klotz, D. (2022). NeuralHydrology — a Python library for deep | ||
learning research in hydrology. Journal of Open Source Software, 7(71), | ||
4050. https://doi.org/10.21105/joss.04050</unstructured_citation> | ||
</citation> | ||
<citation key="hydroeval2021"> | ||
<article_title>Hydroeval: An evaluator for streamflow time | ||
series in Python</article_title> | ||
<author>Hallouin</author> | ||
<doi>10.5281/zenodo.2591217</doi> | ||
<cYear>2021</cYear> | ||
<unstructured_citation>Hallouin, T. (2021). Hydroeval: An | ||
evaluator for streamflow time series in Python. GitHub. | ||
https://doi.org/10.5281/zenodo.2591217</unstructured_citation> | ||
</citation> | ||
<citation key="wade2018hydroerr"> | ||
<article_title>Hydrostats: A Python package for | ||
characterizing errors between observed and predicted time | ||
series</article_title> | ||
<author>Roberts</author> | ||
<journal_title>Hydrology</journal_title> | ||
<volume>5</volume> | ||
<doi>10.3390/hydrology5040066</doi> | ||
<cYear>2021</cYear> | ||
<unstructured_citation>Roberts, W., Williams, G., Jackson, | ||
E., Nelson, J., & Ames, D. (2021). Hydrostats: A Python package for | ||
characterizing errors between observed and predicted time series. | ||
Hydrology, 5. | ||
https://doi.org/10.3390/hydrology5040066</unstructured_citation> | ||
</citation> | ||
<citation key="Wang2020"> | ||
<article_title>Watchman: Monitoring dependency conflicts for | ||
Python library ecosystem</article_title> | ||
<author>Wang</author> | ||
<journal_title>Proceedings of the ACM/IEEE 42nd | ||
international conference on software engineering</journal_title> | ||
<doi>10.1145/3377811.3380426</doi> | ||
<isbn>9781450371216</isbn> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>Wang, Y., Wen, M., Liu, Y., Wang, Y., | ||
Li, Z., Wang, C., Yu, H., Cheung, S.-C., Xu, C., & Zhu, Z. (2020). | ||
Watchman: Monitoring dependency conflicts for Python library ecosystem. | ||
Proceedings of the ACM/IEEE 42nd International Conference on Software | ||
Engineering, 125–135. | ||
https://doi.org/10.1145/3377811.3380426</unstructured_citation> | ||
</citation> | ||
<citation key="Mukherjee2021"> | ||
<article_title>Fixing dependency errors for Python build | ||
reproducibility</article_title> | ||
<author>Mukherjee</author> | ||
<journal_title>Proceedings of the 30th ACM SIGSOFT | ||
international symposium on software testing and analysis</journal_title> | ||
<doi>10.1145/3460319.3464797</doi> | ||
<isbn>9781450384599</isbn> | ||
<cYear>2021</cYear> | ||
<unstructured_citation>Mukherjee, S., Almanza, A., & | ||
Rubio-González, C. (2021). Fixing dependency errors for Python build | ||
reproducibility. Proceedings of the 30th ACM SIGSOFT International | ||
Symposium on Software Testing and Analysis, 439–451. | ||
https://doi.org/10.1145/3460319.3464797</unstructured_citation> | ||
</citation> | ||
<citation key="paszke2019pytorch"> | ||
<article_title>PyTorch: An imperative style, | ||
high-performance deep learning library</article_title> | ||
<author>Paszke</author> | ||
<journal_title>Advances in neural information processing | ||
systems</journal_title> | ||
<volume>32</volume> | ||
<doi>10.48550/arXiv.1912.01703</doi> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Paszke, A., Gross, S., Massa, F., | ||
Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, | ||
N., Antiga, L., & others. (2019). PyTorch: An imperative style, | ||
high-performance deep learning library. Advances in Neural Information | ||
Processing Systems, 32. | ||
https://doi.org/10.48550/arXiv.1912.01703</unstructured_citation> | ||
</citation> | ||
</citation_list> | ||
</journal_article> | ||
</journal> | ||
</body> | ||
</doi_batch> |
Binary file not shown.
Oops, something went wrong.