This course introduces more advanced tools to increase the reproducibility of data analyses; building upon the Intro to Reproducibility course. GitHub, Docker, Code Review, and GitHub actions are discussed.

This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It is the first course in a two part course on reproducibility. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses.

This course covers the pitfalls of informatics research and discusses best practices and tools to overcome the challenges of working with and managing multidisciplinary teams. It also covers guidelines to promote diversity and inclusion in your lab and research.

This course is designed to help researchers and investigators understand the key principles of data management from an ethics, privacy, security, usability and discoverability perspective.

Learn about the new NIH data sharing policy, places where you might want to share your particular kind of data, and how to deal with possible challenges associated with the policy.