Best practices for versioning code, analysis notebooks, containers, and workflows to keep research auditable and reproducible.
A practical, copy-ready notebook template with structured experiment metadata, dataset/version provenance, preprocessing and feature records, training and hyperparameter sections, evaluation reporting, model artifact provenance, and a reproducibility checklist — plus guidance for lightweight tracking integrations.
Practical, actionable runbook showing how to version code, analysis notebooks, environments, containers, data, and artifacts so research is auditable and reproducible. Includes example git workflows, notebook practices, environment & container guidance, data-artifact patterns, provenance capture, CI checks, and a reproducibility checklist you can adopt.
Practical templates, patterns, and a checklist to run, version, audit, and scale containerized compute pipelines with workflow engines and captured provenance.