Running a reproducible research project 2401-CS-RaRRP-s2

The goal of scientific research is to build reliable knowledge. To approach this, scientists should be able to repeat the analysis and replicate findings of other researchers. The individual research should be transparent and well documented so it can be verified by other scientists. Reproducibility stands for “obtaining consistent results using the same input data, computational steps, methods, and code, and conditions of analysis” [NAS, 2018]. The goal of this course is to guide students through essential research practices that will increase reproducibility in their research. The course will cover the following topics:

1. Introduction to reproducibility: reasons of reproducibility crisis, motivations of reproducible research, and good reproducible research practices;
2. Research preregistration: introduction to concepts of pre-registration and pre-registered reports.
3. Sharing and organizing data: setting up data management plan, structuring and naming project files, sharing data in open repositories; Brain Imaging Data Structure (BIDS) formatting, and controlling versions of data with DataLad.
4. Sharing and organizing analysis code: creating a reproducible data analysis workflow, code version control (Git/GitHub), containers (Docker), and good practices for coding and documenting your analysis;
5. Writing a reproducible manuscript.