Access representation ontology developed for project's cohorts D3.5
Bibliographic record
Abstract
Access, reuse and integration of biomedical datasets is critical to advance genomics research and realise benefits to human health. However, obtaining human controlled-access data in a timely fashion can be challenging, as neither the access requests nor the data uses conditions are standardised: their manual review and evaluation by a Data Access Committee (DAC) to determine whether access should be granted or not can significantly delay the process, typically by at least 4 to 6 weeks once the dataset of interest has been identified. To address this, we have contributed to the development of the Data Use Ontology (DUO), which was approved as a Global Alliance for Genomics and Health (GA4GH) standard and has been used in over 200,000 annotations worldwide. DUO is a machine readable structured vocabulary that contains "Permission terms" (which describe data use permissions) and "Modifier terms" (which describe data use requirements, limitations or prohibitions) and it has already been implemented in some CINECA cohort and cohort data sharing resources (e.g. EGA, H3Africa, synthetic datasets); additional cohorts are in the process of reviewing data access policies with a view of applying DUO terms to their datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".