Skip to content

Commit

Permalink
Merge pull request #4599 from openjournals/joss.05738
Browse files Browse the repository at this point in the history 
Merging automatically
  • Loading branch information
@editorialbot
editorialbot authored Sep 22, 2023
2 parents 2d0d98a + 80860e1 commit a4c686b
Show file tree
Hide file tree
Showing 4 changed files with 952 additions and 0 deletions.
348 changes: 348 additions & 0 deletions joss.05738/10.21105.joss.05738.crossref.xml
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,348 @@
<?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>20230922T094014-20e5b9aff4ba2bd865091bed9316899ad0a8f989</doi_batch_id>
<timestamp>20230922094014</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</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>09</month>
<year>2023</year>
</publication_date>
<journal_volume>
<volume>8</volume>
</journal_volume>
<issue>89</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>pactus: A Python framework for trajectory
classification</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>G.</given_name>
<surname>Viera-López</surname>
<ORCID>https://orcid.org/0000-0002-9661-5709</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>J. J.</given_name>
<surname>Morgado-Vega</surname>
<ORCID>https://orcid.org/0000-0001-6067-9172</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>A.</given_name>
<surname>Reyes</surname>
<ORCID>https://orcid.org/0000-0001-7305-4710</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>E.</given_name>
<surname>Altshuler</surname>
<ORCID>https://orcid.org/0000-0003-4192-5635</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Yudivián</given_name>
<surname>Almeida-Cruz</surname>
<ORCID>https://orcid.org/0000-0002-2345-1387</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Giorgio</given_name>
<surname>Manganini</surname>
<ORCID>https://orcid.org/0000-0002-5394-4094</ORCID>
</person_name>
</contributors>
<publication_date>
<month>09</month>
<day>22</day>
<year>2023</year>
</publication_date>
<pages>
<first_page>5738</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.05738</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.8352324</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/5738</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.05738</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.05738</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.05738.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="reyes2023yupi">
<article_title>Yupi: Generation, tracking and analysis of
trajectory data in python</article_title>
<author>Reyes</author>
<journal_title>Environmental Modelling &amp;
Software</journal_title>
<volume>163</volume>
<doi>10.1016/j.envsoft.2023.105679</doi>
<issn>1364-8152</issn>
<cYear>2023</cYear>
<unstructured_citation>Reyes, A., Viera-López, G.,
Morgado-Vega, J. J., &amp; Altshuler, E. (2023). Yupi: Generation,
tracking and analysis of trajectory data in python. Environmental
Modelling &amp; Software, 163, 105679.
https://doi.org/10.1016/j.envsoft.2023.105679</unstructured_citation>
</citation>
<citation key="zheng2009mining">
<article_title>Mining interesting locations and travel
sequences from GPS trajectories</article_title>
<author>Zheng</author>
<journal_title>Proceedings of the 18th international
conference on world wide web</journal_title>
<doi>10.1145/1526709.1526816</doi>
<cYear>2009</cYear>
<unstructured_citation>Zheng, Y., Zhang, L., Xie, X., &amp;
Ma, W.-Y. (2009). Mining interesting locations and travel sequences from
GPS trajectories. Proceedings of the 18th International Conference on
World Wide Web, 791–800.
https://doi.org/10.1145/1526709.1526816</unstructured_citation>
</citation>
<citation key="zheng2008understanding">
<article_title>Understanding mobility based on GPS
data</article_title>
<author>Zheng</author>
<journal_title>Proceedings of the 10th international
conference on ubiquitous computing</journal_title>
<doi>10.1145/1409635.1409677</doi>
<cYear>2008</cYear>
<unstructured_citation>Zheng, Y., Li, Q., Chen, Y., Xie, X.,
&amp; Ma, W.-Y. (2008). Understanding mobility based on GPS data.
Proceedings of the 10th International Conference on Ubiquitous
Computing, 312–321.
https://doi.org/10.1145/1409635.1409677</unstructured_citation>
</citation>
<citation key="zheng2010geolife">
<article_title>GeoLife: A collaborative social networking
service among user, location and trajectory.</article_title>
<author>Zheng</author>
<journal_title>IEEE Data Eng. Bull.</journal_title>
<issue>2</issue>
<volume>33</volume>
<cYear>2010</cYear>
<unstructured_citation>Zheng, Y., Xie, X., Ma, W.-Y., &amp;
others. (2010). GeoLife: A collaborative social networking service among
user, location and trajectory. IEEE Data Eng. Bull., 33(2),
32–39.</unstructured_citation>
</citation>
<citation key="Dua:2019">
<article_title>UCI machine learning
repository</article_title>
<author>Dua</author>
<cYear>2017</cYear>
<unstructured_citation>Dua, D., &amp; Graff, C. (2017). UCI
machine learning repository. University of California, Irvine, School of
Information; Computer Sciences.
http://archive.ics.uci.edu/ml</unstructured_citation>
</citation>
<citation key="rapp2009elk">
<volume_title>Elk, deer, and cattle: The starkey
project</volume_title>
<author>Rapp</author>
<cYear>2009</cYear>
<unstructured_citation>Rapp, V. (2009). Elk, deer, and
cattle: The starkey project. DIANE Publishing.</unstructured_citation>
</citation>
<citation key="landsea2013atlantic">
<article_title>Atlantic hurricane database uncertainty and
presentation of a new database format</article_title>
<author>Landsea</author>
<journal_title>Monthly Weather Review</journal_title>
<issue>10</issue>
<volume>141</volume>
<doi>10.1175/mwr-d-12-00254.1</doi>
<cYear>2013</cYear>
<unstructured_citation>Landsea, C. W., &amp; Franklin, J. L.
(2013). Atlantic hurricane database uncertainty and presentation of a
new database format. Monthly Weather Review, 141(10), 3576–3592.
https://doi.org/10.1175/mwr-d-12-00254.1</unstructured_citation>
</citation>
<citation key="ying2014overview">
<article_title>An overview of the china meteorological
administration tropical cyclone database</article_title>
<author>Ying</author>
<journal_title>Journal of Atmospheric and Oceanic
Technology</journal_title>
<issue>2</issue>
<volume>31</volume>
<doi>10.1175/JTECH-D-12-00119.1</doi>
<cYear>2014</cYear>
<unstructured_citation>Ying, M., Zhang, W., Yu, H., Lu, X.,
Feng, J., Fan, Y., Zhu, Y., &amp; Chen, D. (2014). An overview of the
china meteorological administration tropical cyclone database. Journal
of Atmospheric and Oceanic Technology, 31(2), 287–301.
https://doi.org/10.1175/JTECH-D-12-00119.1</unstructured_citation>
</citation>
<citation key="lu2021western">
<article_title>Western north pacific tropical cyclone
database created by the china meteorological
administration</article_title>
<author>Lu</author>
<journal_title>Advances in Atmospheric
Sciences</journal_title>
<issue>4</issue>
<volume>38</volume>
<doi>10.1007/s00376-020-0211-7</doi>
<cYear>2021</cYear>
<unstructured_citation>Lu, X., Yu, H., Ying, M., Zhao, B.,
Zhang, S., Lin, L., Bai, L., &amp; Wan, R. (2021). Western north pacific
tropical cyclone database created by the china meteorological
administration. Advances in Atmospheric Sciences, 38(4), 690–699.
https://doi.org/10.1007/s00376-020-0211-7</unstructured_citation>
</citation>
<citation key="da2019survey">
<article_title>A survey and comparison of trajectory
classification methods</article_title>
<author>Silva</author>
<journal_title>2019 8th brazilian conference on intelligent
systems (BRACIS)</journal_title>
<doi>10.1109/bracis.2019.00141</doi>
<cYear>2019</cYear>
<unstructured_citation>Silva, C. L. da, Petry, L. M., &amp;
Bogorny, V. (2019). A survey and comparison of trajectory classification
methods. 2019 8th Brazilian Conference on Intelligent Systems (BRACIS),
788–793.
https://doi.org/10.1109/bracis.2019.00141</unstructured_citation>
</citation>
<citation key="xiao2017identifying">
<article_title>Identifying different transportation modes
from trajectory data using tree-based ensemble
classifiers</article_title>
<author>Xiao</author>
<journal_title>ISPRS International Journal of
Geo-Information</journal_title>
<issue>2</issue>
<volume>6</volume>
<doi>10.3390/ijgi6020057</doi>
<cYear>2017</cYear>
<unstructured_citation>Xiao, Z., Wang, Y., Fu, K., &amp; Wu,
F. (2017). Identifying different transportation modes from trajectory
data using tree-based ensemble classifiers. ISPRS International Journal
of Geo-Information, 6(2), 57.
https://doi.org/10.3390/ijgi6020057</unstructured_citation>
</citation>
<citation key="bae2022transformer">
<article_title>Transformer networks for trajectory
classification</article_title>
<author>Bae</author>
<journal_title>2022 IEEE international conference on big
data and smart computing (BigComp)</journal_title>
<doi>10.1109/bigcomp54360.2022.00074</doi>
<cYear>2022</cYear>
<unstructured_citation>Bae, K., Lee, S., &amp; Lee, W.
(2022). Transformer networks for trajectory classification. 2022 IEEE
International Conference on Big Data and Smart Computing (BigComp),
331–333.
https://doi.org/10.1109/bigcomp54360.2022.00074</unstructured_citation>
</citation>
<citation key="janczura2020classification">
<article_title>Classification of particle trajectories in
living cells: Machine learning versus statistical testing hypothesis for
fractional anomalous diffusion</article_title>
<author>Janczura</author>
<journal_title>Physical Review E</journal_title>
<issue>3</issue>
<volume>102</volume>
<doi>10.1103/physreve.102.032402</doi>
<cYear>2020</cYear>
<unstructured_citation>Janczura, J., Kowalek, P.,
Loch-Olszewska, H., Szwabiński, J., &amp; Weron, A. (2020).
Classification of particle trajectories in living cells: Machine
learning versus statistical testing hypothesis for fractional anomalous
diffusion. Physical Review E, 102(3), 032402.
https://doi.org/10.1103/physreve.102.032402</unstructured_citation>
</citation>
<citation key="li2022two">
<article_title>A two-stage semi-supervised high maneuvering
target trajectory data classification algorithm</article_title>
<author>Li</author>
<journal_title>Applied Sciences</journal_title>
<issue>21</issue>
<volume>12</volume>
<doi>10.3390/app122110979</doi>
<cYear>2022</cYear>
<unstructured_citation>Li, Q., He, X., Chen, K., &amp;
Ouyang, Q. (2022). A two-stage semi-supervised high maneuvering target
trajectory data classification algorithm. Applied Sciences, 12(21),
10979. https://doi.org/10.3390/app122110979</unstructured_citation>
</citation>
<citation key="shenk2021traja">
<article_title>Traja: A python toolbox for animal trajectory
analysis</article_title>
<author>Shenk</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>63</issue>
<volume>6</volume>
<doi>10.21105/joss.03202</doi>
<cYear>2021</cYear>
<unstructured_citation>Shenk, J., Byttner, W.,
Nambusubramaniyan, S., &amp; Zoeller, A. (2021). Traja: A python toolbox
for animal trajectory analysis. Journal of Open Source Software, 6(63),
3202. https://doi.org/10.21105/joss.03202</unstructured_citation>
</citation>
<citation key="joo2020navigating">
<article_title>Navigating through the r packages for
movement</article_title>
<author>Joo</author>
<journal_title>Journal of Animal Ecology</journal_title>
<issue>1</issue>
<volume>89</volume>
<doi>10.1111/1365-2656.13116</doi>
<cYear>2020</cYear>
<unstructured_citation>Joo, R., Boone, M. E., Clay, T. A.,
Patrick, S. C., Clusella-Trullas, S., &amp; Basille, M. (2020).
Navigating through the r packages for movement. Journal of Animal
Ecology, 89(1), 248–267.
https://doi.org/10.1111/1365-2656.13116</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading

0 comments on commit a4c686b

Please sign in to comment.