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name: refactoring-04-data-io-validation description: Use when improving data loading, validation, and IO boundaries in Python research code.

Refactoring 04: Data IO and Validation

Goal

Make data handling reliable with clear schemas, validated inputs, and consistent IO boundaries.

Sequence

• Order: 04
• Previous: refactoring-03-config-reproducibility
• Next: refactoring-05-testing-regression

Workflow

• Create a single data access layer (loaders, paths, caching) used by all entrypoints.
  • Success: Entrypoints share one data loading API.
• Define expected schemas (columns, dtypes, shapes) and validate inputs early.
  • Success: Invalid inputs are rejected with clear errors.
• Add light weight checksums or version tags to datasets where practical.
  • Success: Dataset versions are recorded and comparable.
• Keep preprocessing steps deterministic and logged.
  • Success: Preprocessing outputs are repeatable and traceable.
• Separate raw, intermediate, and final outputs with clear folder names.
  • Success: Output folders are consistent and documented.

Guardrails

• Avoid changing dataset content unless explicitly requested.
• Keep validation checks fast and focused on critical assumptions.
• Do not duplicate IO logic across scripts and notebooks.