{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:08Z","timestamp":1740185108383,"version":"3.37.3"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T00:00:00Z","timestamp":1540166400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004807","name":"DFG","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004807","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Research Training Group"},{"name":"GRK","award":["1906\/1"],"award-info":[{"award-number":["1906\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Live cell imaging plays a pivotal role in understanding cell growth. Yet, there is a lack of visualization alternatives for quick qualitative characterization of colonies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>SeeVis is a Python workflow for automated and qualitative visualization of time-lapse microscopy data. It automatically pre-processes the movie frames, finds particles, traces their trajectories and visualizes them in a space-time cube offering three different color mappings to highlight different features. It supports the user in developing a mental model for the data. SeeVis completes these steps in 1.15\u2009s\/frame and creates a visualization with a selected color mapping.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/ghattab\/seevis\/<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty889","type":"journal-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T19:19:19Z","timestamp":1539976759000},"page":"1802-1804","source":"Crossref","is-referenced-by-count":2,"title":["SeeVis\u20143D space-time cube rendering for visualization of microfluidics image data"],"prefix":"10.1093","volume":"35","author":[{"given":"Georges","family":"Hattab","sequence":"first","affiliation":[{"name":"International Research Training Group \u2018Computational Methods for the Analysis of the Diversity and Dynamics of Genomes\u2019, Faculty of Technology, Bielefeld University, Bielefeld, Germany"},{"name":"Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim W","family":"Nattkemper","sequence":"additional","affiliation":[{"name":"Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,10,22]]},"reference":[{"key":"2023013107533867900_bty889-B1","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1186\/s12859-015-0759-x","article-title":"CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data","volume":"16","author":"Bray","year":"2015","journal-title":"BMC Bioinform."},{"key":"2023013107533867900_bty889-B2","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1006\/jcis.1996.0217","article-title":"Methods of digital video microscopy for colloidal studies","volume":"179","author":"Crocker","year":"1996","journal-title":"J. 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