This work was originally created by Mike Croucher from RSE-Sheffield under a Creative Commons Attribution Share Alike 4.0 International. It was subsequently adapted by Malika Ihle from Reproducible Research Oxford and Anne-Kathrin Kleine from LMU Munich.
- You should have an understanding of how to use R, Git, and GitHub
- Introduction to computational reproducibility
- Introduction to the Social Sciences Reproduction Platform
- The BITSS Resource Library contains resources for learning, teaching, and practicing research transparency and reproducibility, including curricula, slide decks, books, guidelines, templates, software, and other tools
- The GitHub repository Awesome Reproducible Research provides a curated list of reproducible research case studies, projects, tutorials, and media
- Template README
- FAIR software
In this self-paced tutorial, you will learn how to create Reproduction Packages. We will cover the following topics:
- What is data reproducibility?
- The role of Reproduction Packages for reproducibility of results in the Social Sciences
- The core elements of Reproduction Packages
- R and relevant R packages
- Git and GitHub
- Remote websites for data and code publication/ hosting
- The principles of data management
- De-identifying confidential or sensitive information
- Annotating data and creating a codebook
- Annotating data cleaning scripts and data analysis code
- How much time and funding should I allocate to creating Reproduction Packages?
- Example time plan
- Step-by-step creating a Reproduction Package
The material is self-paced and includes a worked-example at the end. It is necessary that you work through the sections in order.