About this Course

Reproducibility of data analyses can be enhanced through the use of tools designed to manage the complexity involved in any data analysis designed to address an important scientific question. We focus on a few software tools that aid in project organization, collaboration, auditability of analyses, and maintaining the integrity of data and code. In this course, we view a data analysis as a complex system with many integrated parts that together produce analytic results. The tools we focus on here allow data analysts to diagnose unexpected results, quickly identify problems with data and code, and provide a basis for managing the dynamic nature of data analysis.

This initiative is funded by the following grant: R25GM141505 from the National Institute of General Medical Sciences (NIGMS). Except where otherwise indicated, the contents of this course are available for use under the Creative Commons Attribution 4.0 license. You are free to adapt and share the work, but you must give appropriate credit, provide a link to the license, and indicate if changes were made. Sample attribution: Tools for Reproducible Workflows in R by Fred Hutchinson Data Science Lab and University of Texas, Austin (CC-BY 4.0). You can download the illustrations by clicking here.

0.1 Available course formats

This course is available in multiple formats which allows you to take it in the way that best suites your needs. You can take it for certificate which can be for free or fee.

All illustrations CC-BY.
All other materials CC-BY unless noted otherwise.