‘Access Arrangements’ for Biobanks: A Fine Line between Facilitating and Hindering Collaboration
Bibliographic record
Abstract
This decade is witnessing the proliferation of large-scale population-based biobanks. Many biobanks have reached the stage of offering access to their collection of data and samples to the scientific community. This, however, requires that access arrangements be established to govern the relationship between biobanks and users. Access arrangements capture the convergence of all normative elements in the life cycle of a biobank: policies, laws, common practices, commitments made by the biobank to participants, the expectations of funders, and the needs of the scientific community. Furthermore, access arrangements shape new legal agreements between 'biobankers' and researchers to ensure appropriate, regulated and efficient use of biobank materials. This paper begins by examining the particularities of access arrangements, identifying the key elements of these new regulatory instruments. Second, the paper looks at various strategies used by biobanks to regulate access and surveys the underlying motivations of these strategies and the impact they can have on potential international collaboration. Third, an example of the challenges encountered in creating access policy is illustrated using the case of CARTaGENE, a biobank based in Montreal, Canada. Last, the paper presents how Public Population Project in Genomics (P(3)G) facilitates the work of biobankers and improves collaboration throughout the international human genomics research community.
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 | 0.181 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.071 |
| Scholarly communication | 0.039 | 0.038 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".