Learning and teaching biological data science in the Bioconductor community
Computers and Society
2025-06-03 v2 Other Quantitative Biology
Applications
Abstract
Modern 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.
Keywords
Cite
@article{arxiv.2410.01351,
title = {Learning and teaching biological data science in the Bioconductor community},
author = {Jenny Drnevich and Frederick J. Tan and Fabricio Almeida-Silva and Robert Castelo and Aedin C. Culhane and Sean Davis and Maria A. Doyle and Ludwig Geistlinger and Andrew R. Ghazi and Susan Holmes and Leo Lahti and Alexandru Mahmoud and Kozo Nishida and Marcel Ramos and Kevin Rue-Albrecht and David J. H. Shih and Laurent Gatto and Charlotte Soneson},
journal= {arXiv preprint arXiv:2410.01351},
year = {2025}
}
Comments
16 pages, 2 figures, 1 table, 1 supplemental table; update after peer review; 16 pages, 1 figure, 1 table, 1 supplemental table