As neuroscience evolves with increasingly large data sets, complex analyses, and bigger teams, building robust, reproducible, and scalable analysis workflows becomes essential in order to develop large computational science projects and keep them sustainable over time.

In this workshop, you will gain hands-on experience in crafting modular code scripts and command-line tools for efficient data processing, and how to cleanly combine them into a complete analysis pipeline, using Python’s snakemake package. We will explore tools such as Conda package manager to ensure consistent computational environments, vital for sharing code and ensuring computational reproducibility, and Git, GitHub, and DVC for collaborating and sharing code and data across teams.

By the end of this workshop, you’ll be able to develop and manage complex data analysis pipelines, ensuring that your projects are not only advanced in their execution but also sustainable in the long term - your future self will thank you for it!

Learning and participation

Topics

  • Organizing Code into Scripts, CLIs, and Modules
  • Version Controlling Code via Git and GitHub
  • Creating Data Analysis Workflows with Snakemake

Intended participants

  • Researchers and students from all universities are welcome.
  • Participants of all educational backgrounds are welcome.

Useful resources for preparation

Certification

Students who attend at least 75% of the course will receive a participation certificate by email at the end of the course.