CINECA D7.3 First recommendations for implementation in IT Framework (Incl. a Data Management Plan)
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, technical, and societal questions. This deliverable addresses these challenges regarding reuse of health/genomic data in CINECA from the ethical, legal, and societal issues (ELSI) perspective and in the light of Open Science and FAIR principles. In responding to the requirements for an appropriate IT and governance framework for CINECA and beyond, the research presented in this document builds on a review of GDPR provisions and their institutional sources of interpretation as well as national laws, the corresponding legal, ethical, and social science literature, as well as stakeholder engagement workshops with patient representatives, African researchers, and co-creative exercises with CINECA technical experts. The core of this deliverable are the ethical and legal recommendations that take societal implications into account for data access to European, Canadian, and African cohorts. The recommendations address four key areas: (1) Engagement and benefit sharing as prerequisites for data sharing, (2) Informed consent and reuse of data, (3) Safeguards and respect for privacy, and (4) Further uses and data-access. The deliverable concludes with an outlook on relevant projects such as the European Health Data Space (EHDS) and upcoming ELSI developments regarding data reuse and artificial intelligence (AI).
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 | 0.106 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.031 | 0.018 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.188 | 0.137 |
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".