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Data quality & governance audit
Free audit to identify and prioritize data completeness, lineage, access-control, metadata, and governance gaps that threaten reproducibility.
Data quality & governance audit
Practical, step-by-step review to find and prioritize the data issues that break analysis, block reuse, and undermine reproducibility.
Why this audit matters
Research depends on trustworthy data. Missing values, undocumented transformations, unclear ownership, or weak access controls can produce wrong conclusions and costly rework. This audit helps you move from suspicion to a clear, prioritized plan for remediation so teams can trust datasets, reproduce results, and share data safely.
What you will understand and accomplish
Using the included checklist, review template, and audit toolkit you will:
- Assess dataset completeness, provenance/lineage, and metadata quality; identify gaps that block reproducibility.
- Evaluate access controls, role-based permissions, and data-sharing practices for risk and compliance needs.
- Document ownership, retention, and stewardship responsibilities so fixes can be tracked and verified.
- Prioritize issues by impact on analysis and reproducibility and create a focused remediation plan your team can act on.
Who benefits
This audit is useful for principal investigators, data stewards, lab managers, analysts, research software engineers, QA teams, and small research organizations or departments in universities, healthcare, biotech, manufacturing R&D, and nonprofits that need reproducible results and safer data sharing.
What’s included and how to use it
The resource bundle contains a checklist for data-quality reviews, a review template to capture findings and owners, and an audit toolkit with suggested remediation categories. Start by running the checklist against a high-value dataset (e.g., a recent analysis or a dataset scheduled for publication), record findings, assign owners, and use the template to produce a prioritized action plan.
For teams using THE, consider saving audit responses with the platform’s form and JSON storage capability so results become searchable evidence in your research domain and feed follow-up huddles or improvement cycles.
How this fits with Data management & FAIR practices
This audit is a practical complement to FAIR-oriented data lifecycle work: use it to diagnose where datasets fall short on being findable, accessible, interoperable, or reusable, then link audit outcomes to your metadata, curation, and governance efforts. It does not replace institutional legal or privacy reviews—use audit findings to inform those processes.
Ready to begin? Start the free audit to inspect a dataset, capture findings, and build a prioritized remediation plan your team can act on.
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