In protecting data provenance and integrity, which practice is essential?

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Maintaining data provenance and integrity hinges on keeping a detailed record of where data comes from and every step it undergoes, while also controlling who can access and alter the data. Audit trails capture who did what, when, and why, providing a transparent history of data handling. Version control preserves each change as a new saved state, so you can reconstruct the exact sequence of analyses and data versions used in any result. Restricting access to the original data helps prevent unauthorized edits or deletions, ensuring the data remains trustworthy and verifiable over time.

Sharing all data publicly isn't necessary for provenance and integrity and can raise privacy or misuse risks, even though transparency is valuable. Modifying datasets to fit hypotheses undermines integrity because it hides the true data and the actual results. Deleting raw data after publication erases the original evidence, making it impossible to verify findings or reproduce analyses.