Access Governance for Biobanks: The Case of the BioSHaRE-EU Cohorts
Bibliographic record
Abstract
Currently, researchers have to apply separately to individual biobanks if they want to carry out studies that use samples and data from multiple biobanks. This article analyzes the access governance arrangements of the original five biobank members of the Biobank Standardisation and Harmonisation for Research Excellence in the European Union (BioSHaRE-EU) project in Finland, Germany, the Netherlands, Norway, and the United Kingdom to identify similarities and differences in policies and procedures, and consider the potential for internal policy "harmonization." Our analysis found differences in the range of researchers and organizations eligible to access biobanks; application processes; requirements for Research Ethics Committee approval; and terms of Material Transfer Agreements relating to ownership and commercialization. However, the main elements of access are the same across biobanks; access will be granted to bona fide researchers conducting research in the public interest, and all biobanks will consider the scientific merit of the proposed use and it's compatibility with the biobank's objectives. These findings suggest potential areas for harmonization across biobanks. This could be achieved through a single centralized application to a number of biobanks or a system of mutual recognition that places a presumption in favor of access to one biobank if already approved by another member of the same consortium. Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortia (BBMRI-ERIC), a European consortium of biobanks and bioresources with its own ethical, legal, and social implications (ELSI) common service, could provide a platform by developing guidelines for harmonized internal processes.
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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 | 0.169 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".