Version control with Git and GitHub is now part of everyday scientific work, yet most researchers pick it up without a structured introduction to collaboration, review, and reproducible workflows. This organization hosts the material for a hands-on workshop on using Git and GitHub for collaboration in scientific work.
All material is published as Open Educational Resources and is free for any group to reuse.
Sign up (for this and the companion workshop): https://forms.gle/Zf5fPsPJqUxEoLgKA
Website: https://gitforsci-ghe.github.io/website/
A hands-on introduction to using Git and GitHub for collaboration in research, taught to PhD students and researchers. Participants work both through the RStudio interface and at the terminal. Everything runs on local installations, set up as pre-work before the workshop.
By the end of the workshop, participants will be able to:
- Create and clone repositories, and explain the difference between cloning and forking, enabling effective collaboration and local development workflows.
- Apply the standard Git workflow using the RStudio IDE, including pulling, staging, committing, and pushing changes, and resolve basic merge conflicts when they arise.
- Work with branches and pull requests: create and manage development branches (using both RStudio and the terminal), submit pull requests, and review and comment on others' pull requests to facilitate code review and collaborative development.
This workshop sets the foundations for working with AI-assisted, agentic coding tools in research, where every change is versioned and reviewable.
| Repository | Holds |
|---|---|
| website | Course website for this iteration |
| setup | Pre-workshop setup instructions |
| washinvestments | Team exercise template: a Quarto manuscript on WASH investment trends |
This organisation hosts the Summer 2026 iteration of Git for Scientists (canonical website: https://gitforsci.github.io/website/). If you use or adapt these materials, please cite this iteration snapshot:
Schöbitz, L. (2026). Git for Scientists (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.21704114
The canonical course carries its own DOI: 10.5281/zenodo.21703566.
Funding and support: Swiss Reproducibility Network (SwissRN) local node at ETH Zurich & Data Stewardship Network at ETH Zurich, funded by the swissuniversities Open Science Programme & Global Health Engineering group at ETH Zurich