- Day 1 – Writing Reproducible Code: Organize research projects with clean, modular code; refactor scripts into reusable functions; and introduce simple pipelines that make analyses easier to rerun and extend.
- Day 2 – Collaboration & Environments: Use Git and GitHub for transparent, collaborative research, and create reproducible Python and R environments that run consistently across machines and systems, even containers.
- Day 3 – AI‑Accelerated Research: Apply AI tools (e.g., Copilot, Jan.AI) to speed up coding, debugging, documentation, and reproducibility checks.
- From Workshop to Practice: Connect concepts to real research workflows, identify reproducibility risks in your own projects, and leave with practical next steps you can apply immediately.
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