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Reproducibility checklist

Before releasing or assigning a module, verify:

  • The engineering question, variables, units, assumptions, and validity limits are stated.
  • Data provenance, rights, schema, and split rules are documented.
  • A baseline case runs from a documented command in a clean environment.
  • Dependencies and supported Python/framework versions are stated.
  • Random seeds and model configuration are recorded where relevant.
  • Outputs are traceable to code, data version, and parameters.
  • Tests or smoke checks cover critical calculations and data schemas.
  • Training, validation, and test data are not inadvertently mixed.
  • Failure cases and known limitations are shown.
  • Sensitive, student, confidential, or restricted material is excluded.