Skip to content

Commit

Permalink
Merge pull request #4386 from openjournals/joss.05099
Browse files Browse the repository at this point in the history 
Merging automatically
  • Loading branch information
@editorialbot
editorialbot committed Jul 6, 2023
2 parents 755bc43 + 2f6731a commit df904a0
Show file tree
Hide file tree
Showing 3 changed files with 782 additions and 0 deletions.
260 changes: 260 additions & 0 deletions joss.05099/10.21105.joss.05099.crossref.xml
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,260 @@
<?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>20230706T155724-945d6fac2343c42fc28b6f52ae11cba266ddac85</doi_batch_id>
<timestamp>20230706155724</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>2023</year>
</publication_date>
<journal_volume>
<volume>8</volume>
</journal_volume>
<issue>87</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>ProgPy: Python Packages for Prognostics and Health
Management of Engineering Systems</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Christopher</given_name>
<surname>Teubert</surname>
<ORCID>https://orcid.org/0000-0001-6788-4507</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Katelyn</given_name>
<surname>Jarvis</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Matteo</given_name>
<surname>Corbetta</surname>
<ORCID>https://orcid.org/0000-0002-7169-1051</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Chetan</given_name>
<surname>Kulkarni</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Matthew</given_name>
<surname>Daigle</surname>
</person_name>
</contributors>
<publication_date>
<month>07</month>
<day>06</day>
<year>2023</year>
</publication_date>
<pages>
<first_page>5099</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.05099</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.8097013</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/5099</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.05099</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.05099</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.05099.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="2022_nasa_progpy">
<article_title>ProgPy packages</article_title>
<author>Teubert</author>
<cYear>2022</cYear>
<unstructured_citation>Teubert, C., Jarvis, K., Corbetta,
M., Kulkarni, C., &amp; Daigle, M. (2022). ProgPy packages (Version
1.4). https://nasa.github.io/progpy</unstructured_citation>
</citation>
<citation key="goebel2017prognostics">
<volume_title>Prognostics: The science of making
predictions</volume_title>
<author>Goebel</author>
<cYear>2017</cYear>
<unstructured_citation>Goebel, K., Daigle, M. J., Saxena,
A., Roychoudhury, I., Sankararaman, S., &amp; Celaya, J. (2017).
Prognostics: The science of making predictions. Createspace Independent
Pub.</unstructured_citation>
</citation>
<citation key="teubert2017generic">
<article_title>A generic software architecture for
prognostics (GSAP)</article_title>
<author>Teubert</author>
<journal_title>International Journal of Prognostics and
Health Management</journal_title>
<issue>2</issue>
<volume>8</volume>
<doi>10.36001/ijphm.2017.v8i2.2618</doi>
<cYear>2017</cYear>
<unstructured_citation>Teubert, C., Daigle, M. J.,
Sankararaman, S., Goebel, K., &amp; Watkins, J. (2017). A generic
software architecture for prognostics (GSAP). International Journal of
Prognostics and Health Management, 8(2).
https://doi.org/10.36001/ijphm.2017.v8i2.2618</unstructured_citation>
</citation>
<citation key="2016_nasa_prog_model_library">
<article_title>Prognostics models matlab
library</article_title>
<author>Daigle</author>
<cYear>2016</cYear>
<unstructured_citation>Daigle, M. (2016). Prognostics models
matlab library.
https://github.com/nasa/PrognosticsModelLibrary</unstructured_citation>
</citation>
<citation key="2016_nasa_prog_algs_library">
<article_title>Prognostics algorithm matlab
library</article_title>
<author>Daigle</author>
<cYear>2016</cYear>
<unstructured_citation>Daigle, M. (2016). Prognostics
algorithm matlab library.
https://github.com/nasa/PrognosticsAlgorithmLibrary</unstructured_citation>
</citation>
<citation key="2016_nasa_prog_metrics_library">
<article_title>Prognostics metrics matlab
library</article_title>
<author>Daigle</author>
<cYear>2016</cYear>
<unstructured_citation>Daigle, M. (2016). Prognostics
metrics matlab library.
https://github.com/nasa/PrognosticsMetricsLibrary</unstructured_citation>
</citation>
<citation key="watkins2019prognostics">
<article_title>Prognostics as-a-service: A scalable cloud
architecture for prognostics: A scalable cloud architecture for
prognostics</article_title>
<author>Watkins</author>
<journal_title>Annual conference of the PHM
society</journal_title>
<volume>11</volume>
<cYear>2019</cYear>
<unstructured_citation>Watkins, J., Teubert, C., &amp;
Ossenfort, J. (2019). Prognostics as-a-service: A scalable cloud
architecture for prognostics: A scalable cloud architecture for
prognostics. Annual Conference of the PHM Society,
11.</unstructured_citation>
</citation>
<citation key="jarvis2022enabling">
<article_title>Enabling in-time prognostics with surrogate
modeling through physics-enhanced dynamic mode decomposition
method</article_title>
<author>Jarvis</author>
<journal_title>Annual conference of the PHM
society</journal_title>
<volume>14</volume>
<doi>10.36001/phmconf.2022.v14i1.3238</doi>
<cYear>2022</cYear>
<unstructured_citation>Jarvis, K., Corbetta, M., Teubert,
C., &amp; Schuet, S. (2022). Enabling in-time prognostics with surrogate
modeling through physics-enhanced dynamic mode decomposition method.
Annual Conference of the PHM Society, 14.
https://doi.org/10.36001/phmconf.2022.v14i1.3238</unstructured_citation>
</citation>
<citation key="Dataset_RWBattery">
<article_title>Randomized battery usage data
set</article_title>
<author>Bole</author>
<journal_title>NASA Ames Research Center, Moffett Field,
CA</journal_title>
<cYear>2014</cYear>
<unstructured_citation>Bole, B., Kulkarni, C., &amp; Daigle,
M. (2014). Randomized battery usage data set. In NASA Ames Research
Center, Moffett Field, CA. NASA Prognostics Data Repository.
https://www.nasa.gov/content/prognostics-center-of-excellence-data-set-repository</unstructured_citation>
</citation>
<citation key="Dataset_CMAPPS">
<article_title>Turbofan engine degradation
simulation</article_title>
<author>Saxena</author>
<journal_title>NASA Ames Research Center, Moffett Field,
CA</journal_title>
<cYear>2008</cYear>
<unstructured_citation>Saxena, A., &amp; Goebel, K. (2008).
Turbofan engine degradation simulation. In NASA Ames Research Center,
Moffett Field, CA. NASA Prognostics Data Repository.
https://www.nasa.gov/content/prognostics-center-of-excellence-data-set-repository</unstructured_citation>
</citation>
<citation key="Simantha">
<article_title>Simantha</article_title>
<author>Dadfarnia</author>
<journal_title>NIST</journal_title>
<cYear>2013</cYear>
<unstructured_citation>Dadfarnia, M., &amp; Drozdov, S.
(2013). Simantha. In NIST. National Institute of Standards; Technology
[Software].
https://github.com/usnistgov/simantha</unstructured_citation>
</citation>
<citation key="davidson_pilon_cameron_2022_7329096">
<article_title>Lifelines, survival analysis in
python</article_title>
<author>Davidson-Pilon</author>
<doi>10.5281/zenodo.7329096</doi>
<cYear>2022</cYear>
<unstructured_citation>Davidson-Pilon, C. (2022). Lifelines,
survival analysis in python (Version v0.27.4). Zenodo.
https://doi.org/10.5281/zenodo.7329096</unstructured_citation>
</citation>
<citation key="schreiber2017pomegranate">
<article_title>Pomegranate: Fast and flexible probabilistic
modeling in python</article_title>
<author>Schreiber</author>
<journal_title>The Journal of Machine Learning
Research</journal_title>
<issue>1</issue>
<volume>18</volume>
<cYear>2017</cYear>
<unstructured_citation>Schreiber, J. (2017). Pomegranate:
Fast and flexible probabilistic modeling in python. The Journal of
Machine Learning Research, 18(1), 5992–5997.</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading

0 comments on commit df904a0

Please sign in to comment.