Summary and Setup

This short course teaches tools and practices for producing and sharing quality, sustainable and FAIR (Findable, Accessible, Interoperable and Reusable) research software to support open and reproducible research. The course can be delivered over 2 full or 4 half days.

Target audience

Post-graduate students, early career researchers or junior Research Software Engineers (RSEs) who are starting their research or software projects, have foundational knowledge of Python, version control and using software tools from command line shell, and want to develop software to support their research using established best practices
Researchers or scientists who had foundational software training before but wish to refresh, reinforce or improve their skills and practices in the wider context of FAIR scientific practice and sharing and writing software for open and reproducible research

Check out a few example learner profiles, to see if this course is a right fit for you.

Prerequisite

Prerequisites

Foundational knowledge of the following is required to be able to understand code examples used in the course:

Python used to write scientific code
Version control with Git
Working in a command line interface (shell)

Attending a Software Carpentry workshop or a similar course will help you gain the skills and experience needed.

Please also make sure you have all the required software installed before attending this course.

Learning objectives

After attending this training, you will be able to:

List challenges typically faced by researchers developing software and managing data for modern computational, reproducible research, including those commensurate with the FAIR (Findable, Accessible, Interoperable, Reusable) principles.
Build on top of your existing knowledge of Python, Git and command line computing to enhance your research software development workflow with some good open and reproducible research software practices around structuring, writing, documenting, testing, sharing and reusing code (including software licensing and citation).

Acknowledgements

This course was originally developed by the UK’s Software Sustainability Institute and funded by the UK Reproducibility Network (UKRN). See CITATION.cff for the full list of authors.

Software Setup

Before starting the course, make sure you have the following tools installed and working:

Visual Studio Code (VS Code) — your main workspace and terminal
Git + Git Bash — for version control and running commands
Python 3 (via uv) — for running and managing code
GitHub account — for saving and sharing work
Spacewalks data and example code — used in the exercises

These steps work on Windows, macOS, and Linux.
Windows users will use Git Bash inside VS Code instead of PowerShell.

What’s New

Callout

Updated authentication and environment setup

GitHub authentication is simpler — sign in through your browser using Git Credential Manager (no SSH keys or tokens needed).
Python installation uses uv, a modern, fast installer that avoids manual setup.
VS Code is your single workspace — all setup steps run inside its built-in terminal.

Get Started

Follow the step-by-step Installation Instructions
to install everything and verify your setup before the workshop.

If you’ve used older Git or Python workflows, note that these instructions reflect the current recommended Carpentries practices for reproducible, low-friction setup.