This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. The course also introduces tools relevant to reproducibility including analysis notebooks, package managers, git and GitHub.

This course will demonstrate how to:

• Understand the fundamental concepts of reproducibility vs replicability.
• Write a basic analysis that could be passed to another person/computer and be re-ran to obtain the same output.
• Write analysis notebook that adheres to best practices for reproducibility and readability.
• Obtain confidence to learn and apply additional reproducibility tools to an analysis.

Quizzes are included in the course to assess learner's comprehension and hands-on activities guide learners in how to apply these concepts to their own analyses.

This course is written for individuals who:

• Have some familiarity with R or Python - have written some scripts.
• Have not had formal training in computational methods.
• Have limited or no familiar with GitHub, Docker, or package management tools

Goal of this course:

Equip learners with reproducibility skills they can apply to their existing analyses scripts and projects. This course opts for an “ease into it” approach. We attempt to give learners doable, incremental steps to increase the reproducibility of their analyses.

What is not the goal:

This course is meant to introduce learners to the reproducibility tools, but it does not necessarily represent the absolute end-all, be-all best practices for the use of these tools. In other words, this course gives a starting point with these tools, but not an ending point. The advanced version of this course is the next step toward incrementally “better practices”.