Learning and teaching biological data science in the Bioconductor community
Jenny Drnevich, Frederick J. Tan, Fabricio Almeida-Silva, Robert Castelo, Aedin C. Culhane, Sean Davis, Maria A. Doyle, Ludwig Geistlinger, Andrew R. Ghazi, Susan Holmes, Leo Lahti, Alexandru Mahmoud, Kozo Nishida, Marcel Ramos, Kevin Rue-Albrecht, David J.H. Shih, Laurent Gatto
TL;DR
This paper reviews key resources and best practices within the Bioconductor community to enhance biological data science training for researchers and educators in data-intensive biological research.
Contribution
It provides a comprehensive overview of tools and methods in Bioconductor, facilitating improved education and training in biological data science.
Findings
Identifies essential resources for biological data analysis.
Highlights best practices for training in data science.
Serves as a reference for educators and learners.
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.
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