Open data & open science
Best practices for documenting quality flags and exclusion criteria used in creating curated research datasets.
Clear, comprehensive documentation of quality flags and exclusion criteria is essential for reproducibility, transparency, and robust downstream analyses across diverse research domains and data curation workflows.
Published by
Justin Walker
August 12, 2025 - 3 min Read
In any data curation workflow, transparency about why records are flagged or removed is foundational. Documentation should clarify the provenance of each quality flag, including who assigned it, the criteria used, and any thresholds or rules that guided the decision. This record helps researchers understand which observations were considered suspect, erroneous, or outside the intended scope of a study. It also provides a baseline for auditing and reproducing data selections, ensuring that later analysts can trace the logic that shaped the final dataset. When flags are updated, a changelog detailing the rationale and timing improves interpretability and supports revision control across versions.
A robust documentation approach combines structured metadata with narrative context. Structured fields can capture flag type, severity, and associated confidence levels, while narrative notes describe edge cases, exceptions, and the human judgment involved. To maximize usability, maintain consistent terminology across datasets so that researchers can apply the same reasoning in disparate projects. Include examples illustrating typical flag scenarios and the corresponding exclusion criteria. This dual strategy—precise data fields plus readable explanations—facilitates both machine-readable processing and human evaluation, helping readers assess bias risks and reproduce selection workflows accurately.
Documenting the decision pathway from raw data to curated results.
When designing the framework, begin by enumerating all possible quality flags and exclusion criteria that might affect data suitability. Create a controlled vocabulary with explicit definitions, boundaries, and examples for each item. Assign a responsible owner for every flag category to ensure accountability and consistency in application. Document any heuristics or automated checks used to generate flags, including the algorithms, features considered, and performance metrics such as precision and recall. A well-specified framework prevents ad hoc decisions and supports scalable audits as datasets grow or evolve over time.
The next step is to codify the decision rules into reproducible workflows. Use version-controlled scripts or configuration files that encode when a record is flagged, flagged with what severity, or excluded outright. Include unit tests or validation runs that demonstrate expected outcomes for known edge cases. Record any manual reviews and the final disposition, ensuring a traceable lineage from raw data to the curated set. By integrating these components—definitions, rules, tests, and review records—teams can verify that exclusions reflect documented intent rather than subjective impressions.
Providing context about scope, limitations, and intended use of flags.
Exclusion criteria should be linked to measurable data properties whenever possible. For instance, PCR quality flags might reference thresholds in sequencing quality scores, while clinical datasets could rely on missingness patterns or inconsistent timestamps. When a criterion is not strictly quantitative, provide a principled rationale that connects it to study goals or domain knowledge. Cross-reference associated datasets and data producers so readers can assess compatibility and understand potential limitations. Clear links between data attributes and exclusion decisions enable researchers to reproduce or challenge the filtering logic with confidence.
It is important to disclose the scope and limitations of the flags themselves. Explain which data domains or subpopulations the quality checks were designed for, and which situations may require caution in interpretation. If flags are prone to false positives or negatives under certain conditions, describe these risks and any mitigations, such as supplementary checks or manual verification steps. Articulating these caveats helps downstream analysts decide whether the curated dataset is appropriate for their specific hypotheses or methods and fosters responsible use of the data.
Emphasizing reproducibility through versioning, archiving, and logs.
An accessible data dictionary is a practical vehicle for communicating flags and exclusions. Each entry should include the flag name, a concise definition, data fields involved, and examples that illustrate both typical and atypical cases. Include timestamps for flag creation and any subsequent updates, along with the responsible party. Provide links to related quality metrics, such as completeness or consistency scores, to help readers gauge overall data health. A well-maintained dictionary supports interoperability across projects, teams, and repositories, reducing ambiguity during data integration.
Versioning is central to maintaining trust in curated datasets. Each dataset release should carry a unique identifier, a summary of changes to flags and exclusion rules, and a rationale for updates. Archive prior versions so researchers can reproduce historical analyses and compare results over time. When possible, publish automated logs outlining how flags were derived in the latest release. Transparent versioning empowers reproducibility, enables meta-analyses of curation practices, and minimizes confusion about which rules governed a given analysis.
Testing, validation, and bias assessment as core practices.
Collaboration and communication across stakeholders strengthen documentation quality. Include data producers, curators, analysts, and domain experts in the discussion about which criteria matter most and how they should be implemented. Produce regular summaries that translate technical flag details into actionable guidance for non-specialist audiences. Encourage external validation by inviting researchers outside the immediate project to review the flag taxonomy and its practical implications. An inclusive approach ensures the documentation captures diverse perspectives and improves the robustness of the curated dataset.
Quality flags should be tested under realistic data conditions. Simulate datasets with varying noise, missing values, and edge-case patterns to observe how flags perform. Assess whether exclusions introduce systematic biases that could affect downstream conclusions. Document the results of these simulations, including any observed interactions between different flags and their cumulative effects. By subjecting the exclusion criteria to stress tests, teams reveal hidden vulnerabilities and strengthen the credibility of the curated resource.
Beyond technical accuracy, consider user accessibility and readability. Present flag definitions in plain language and support them with succinct examples. Provide visual aids such as dashboards or heatmaps that illustrate flag distributions across data slices. Ensure that documentation remains searchable and navigable, with cross-references connecting flags to the underlying attributes they affect. Accessibility also means offering guidance for new users on how to interpret flags and how to apply the documented exclusion criteria in their analyses.
Finally, weave a culture of continuous improvement into the documentation process. Set periodic review cycles to update definitions, thresholds, and exclusions as new data, methods, or domain insights emerge. Capture lessons learned from each release and incorporate them into training materials for future curation teams. By treating documentation as a living artifact, organizations promote long-term reliability and adaptability, reinforcing trust in curated datasets and their capacity to support rigorous scientific inquiry.
