Rigor and reproducibility in biological research: collection, analysis and use of biological data

2 credits · Graduate

The deluge of data common in modern biological research poses new challenges for ensuring reproducibility and collaboration at many levels. High-powered computational and statistical tools are becoming as indispensable as pipettes and flasks, demanding a rethinking of what it means to be rigorous and reproducible in the generation, visualization, and interpretation of results. In this mini-course, we will explore key concepts in how data should be stored, analyzed, manipulated, and presented using computational tools such as the Python programming language and GitHub version control software at a hands-on level, guiding students through a mini research project. This is an introductory course that assumes no previous knowledge of programming or statistics, and is especially relevant for students or postdocs interested in developing a skill set for quantitative biology.