Drnevich, JennyTan, Frederick J.Almeida-Silva, FabricioCastelo Valdueza, RobertCulhane, Aedín C.Davis, SeanDoyle, Maria A.Geistlinger, LudwigGhazi, Andrew R.Holmes, SusanLahti, LeoMahmoud, AlexandruNishida, KozoRamos, MarcelRue-Albrecht, KevinShih, David J. H.Gatto, LaurentSoneson, Charlotte2025-06-032025-06-032025Drnevich J, Tan FJ, Almeida-Silva F, Castelo R, Culhane AC, Davis S, et al. Learning and teaching biological data science in the Bioconductor community. PLoS Comput Biol. 2025 Apr 22;21(4):e1012925. DOI: 10.1371/journal.pcbi.10129251553-734Xhttp://hdl.handle.net/10230/70600Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project-an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field.application/pdfeng© 2025 Drnevich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1371/journal.pcbi.1012925WorkshopsHuman learningInstructorsLanguageEcosystemsGenome analysisGeographic distributionProgramming languagesinfo:eu-repo/semantics/openAccess