{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T14:49:18Z","timestamp":1790779758986,"version":"4.1.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009514","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000}}],"reference-count":61,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T00:00:00Z","timestamp":1634601600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["757763"],"award-info":[{"award-number":["757763"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002661","name":"Fonds De La Recherche Scientifique - FNRS","doi-asserted-by":"crossref","award":["T.0220.20"],"award-info":[{"award-number":["T.0220.20"]}],"id":[{"id":"10.13039\/501100002661","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003134","name":"Fonds pour la Formation \u00e0 la Recherche dans l\u2019Industrie et dans l\u2019Agriculture","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003134","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce\n                    <jats:italic>pyActigraphy<\/jats:italic>\n                    , a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009514","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T23:28:20Z","timestamp":1634772500000},"page":"e1009514","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":78,"title":["pyActigraphy: Open-source python package for actigraphy data visualization and 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