Incorporating ELSI as a core support for international genomic data access and sharing
Bibliographic record
Abstract
Health data collected in cohort studies are valuable sources for knowledge generation and the advance of biomedical research. However, the use of these data for research projects beyond the initial purpose raises several ethical, legal, and societal questions regarding the sensitivity of genomic data sourced from vulnerable and ethnic groups, such as African genetic data and material. Federated data infrastructures have become a key approach to make population-scale genomic and biomolecular data accessible across international borders. While the FAIR principles have become a guiding technical resource for data sharing, legal and socio-ethical considerations are equally important for a fair data ecosystem for further uses of genomic data. The Horizon 2020 project CINECA aims to provide a federated cloud-based infrastructure for the discovery, access, and analysis of human genetic and phenotypic data, based on a virtual cohort of 1.4 million individuals from 10 cohorts in Europe, Canada, and Africa. Beside technical solutions, CINECA addresses and provides valuable experience and input on essential ethical, legal, and societal implications and requirements for transnational health data access, sharing and secondary processing for research purposes.
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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 | 0.067 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.036 |
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".