Programming Resources: Reproducible Data Science

This (free online) book contains a collection of 31 case studies of reproducible research workflows, written by academic researchers in the data-intensive sciences. Each case study describes how the author combined specific tools, ideas, and practices in order to complete a real-world research project. Emphasis is placed on the practical aspects of how the author organized his or her research to make it as reproducible as possible.

This article covers 10 rules for making your research computationally reproducible including tips for version control, data storage, and managing your scripts.

A collaborative and community-driven guide to designing reproducible research workflows including version control, code licensing, containers, data management, and more.