10. Reproducible Research#

Reproducibility ensures that research findings can be independently verified and built upon. This section presents core principles and practical strategies for making research code, data, and workflows reproducible.

Why Reproducibility Matters

  • Essential for scientific integrity and validation.

  • Enables collaboration and reuse across projects and labs.

  • Required by many journals, conferences, and funding agencies.

10.1. Key Principles of Reproducible Code#

10.2. Environment Reproducibility#

10.3. Data Versioning and Management#

10.4. Randomness and Seeds#

10.5. Documentation of Experiments#

10.6. Testing Reproducibility#

10.7. Output and Artifact Management#

10.8. Sharing and Archiving#

10.9. Summary Checklist#

  • All code is under version control

  • Dependencies are pinned and documented

  • Random seeds are set

  • Environment is isolated (virtual env or container)

  • Data and config files are versioned

  • Instructions exist to reproduce full pipeline

  • Outputs (models, figures) are archived with metadata