Toward ethical provenance tracking: The GA4GH model data access agreement (DAA)
Bibliographic record
Abstract
PURPOSE: Standardizing contractual clauses that govern data access enables research institutions to responsibly steward genomic and related health data while enabling its efficient downstream reuse. METHODS: We describe a document analysis study using both qualitative and comparative law analytical approaches to identify the most common categories of clauses from 29 different data access agreements used by human biomedical research consortia globally. We furthermore characterized the legal positions and standard practices for each common element of the agreement and synthesized across them to develop model clauses. A total of 3 discussion sessions were organized virtually to refine the clauses among members of the Ethical Provenance Subgroup of the Global Alliance for Genomics and Health. RESULTS: We developed 15 unique data access clauses corresponding to the most common legal elements identified in the sampled agreements. CONCLUSION: Model clauses can be used to drive administrative efficiencies and institutional compliance for managing access to human genomic data for research. Additional machine-readable consents and software solutions are needed to support traceable "ethical provenance" of human genomic data and communicate data use conditions throughout the data's life-cycle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.258 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".