Rigor and Reproducibility

The National Institutes of Health (NIH) is undertaking the promotion of principles important for enhancing research reproducibility, including evaluation of foundational research underlying a project (i.e., scientific premise), rigorous experimental design and data interpretation, consideration of relevant biological variables such as sex, authentication of key biological and/or chemical resources, data and material sharing, record keeping, and transparency in reporting. The Chemistry–Biology Interface Training program, which receives funding from the NIH, also considers rigor and reproducibility in research as one its core missions in training young scientists. To that end, trainees are required to show proof of having taken a statistics class in their advanced undergraduate or graduate courses, or to participate in a module or short course designed to teach principles and practices of statistics and research reproducibility.

Here we list some resources on campus that help fill this requirement.

Quantitative Biosciences Initiative: https://qbi.wisc.edu

Full-semester courses:

BMI*/Stats 541 (Introduction to Biostatistics, 3 credits) *BMI=Biostatistics and Medical Informatics

Stats 371 (Introductory Applied Statistics for the Life Sciences, 3 credits)

Short courses:

R Programming and Concepts (Taught at the Social Sciences Computing Cooperative)

Practical Statistics for Analytical Chemists 

Online modules:

HarvardX: Principles, Statistical and Computational Tools for Reproducible Data Science

Other NIH-approved training modules can be found here