changon / bst270-winter2022 Goto Github PK

Instructor: Heather Mattie ([email protected])

TAs: Beau Coker ([email protected]), Linglin Huang ([email protected])

January 10-14, 18-20, 9am-12pm

Heather's office hour: by appointment

TAs office hours: by appointment

Reproducible research has become increasingly important in the biomedical sciences. The science community has recognized reproducibility is a growing challenge in basic, clinical and population sciences. Experimental design, data provenance, analytic methods and tools, and reporting science play a critical role in the biomedical research ecosystem to ensure scientific rigor, robustness and transparency. Statistical and computational methods and tools are fundamental for making scientific results reproducible.

The central theme of the course will be to meet these scientific needs of reproducible science through training in reproducible research. The topics covered in this course include the fundamentals of reproducible science, case studies in reproducible research, data provenance, statistical methods for reproducible science, and computational tools for reproducible science. This is a blended course where students are introduced to course content online through videos and reading assignments, and then shown how to use the tools and methods described in the videos to conduct reproducible research. The online videos can be found on edx.org - the course title is Principles, Statistical and Computational Tools for Reproducible Science. In class we will attempt to reproduce the results of a recently published paper and then critique its reproducibility. Students will also complete an individual project.

1. Christopher Gandrud (2015), Reproducible Research with R and RStudio, 2nd Ed.
2. The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences, 1st Ed.