Consent Codes: Upholding Standard Data Use Conditions
Bibliographic record
Abstract
in the datasets of the main public genome archives (NCBI dbGaP and EMBL-EBI/CRG EGA). OverviewRestrictions on data use may be necessary in order to respect the consent of research participants and patients.As members of the Global Alliance for Genomics and Health (Global Alliance), we examined the variation in data use conditions that are based on consent provisions for genomics datasets in research and clinical settings based on the following: (1) a review of guidance from the National Institutes of Health; (2) a survey of data use conditions for data accessed within a large research institute, the Broad Institute of MIT and Harvard, and for data held within the European Genome-phenome Archive (EGA) of the European Bioinformatics Institute (EMBL-EBI) and Centre for Genomic Regulation (CRG); and (3) consultation with the international scientific community via the Global Alliance Data Working Group and with the Matchmaker Exchange Project.Based on our study, we propose a structure for recording data use "categories" and "requirements" with a view to support maximum data use and integration.
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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 | 0.310 | 0.546 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.031 | 0.026 |
| Insufficient payload (model declined to judge) | 0.063 | 0.045 |
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".