Data Versioning and Provenance Plan
Know which data produced which result.
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[VERIFY] is an editorial flag, not a blank to fill. Wherever the prompt shows [VERIFY], check that item against a reliable source before you use it.
Plan data versioning for my project. Data sources, sizes and update frequency: [DATA_SOURCES]. Team and tools: [TEAM_TOOLS]. Reproducibility needs: [NEEDS]. Propose a scheme: immutable raw data, dated snapshots, checksums, a manifest with source and extraction date, and links from each result to the data version and code commit. Compare options (file naming, a data version control tool, database snapshots) in a table with effort, cost, and fit. Provide a template for a data manifest and a README, and a procedure for updating data. Include privacy notes: do not store personal data in version control without review. Add a short example manifest with five fields filled in for an invented dataset, and a procedure for checking a checksum before analysis. Include a retention plan for old snapshots and a note on legal and privacy limits on keeping personal data, to be checked [VERIFY] for [JURISDICTION]. Add a worked folder layout and manifest for an invented project: raw/ (read-only, dated), interim/, processed/ with version identifiers, and manifests/ holding one file per snapshot. Show the manifest fields filled in for a snapshot (source, extraction date and query, row count, checksum, schema version, person responsible, licence or permission) and the one-line procedure that verifies the checksum before analysis. Label it an illustration. Add a results-to-data link convention: every output table or figure records the data version and code commit in its footer or metadata. Provide a comparison of a manual approach and a tooling approach after twelve months of expected growth, and a decision rule for when the manual approach should be replaced. Close with a one-page rule card: never edit raw files, name every snapshot with its date, record the checksum, link results to versions, and ask before storing personal data. Also add a short note on how to record transformations applied between raw and analysis-ready data so the chain can be audited.
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