A European Survey on Biobanks: Trends and Issues
Bibliographic record
Abstract
Biobanks have recently gained great significance for research and personalised medicine, being recognised as a crucial infrastructure. At the same time, the widely varied practices in biobanking may also pose a barrier to cross-border research and collaboration by limiting access to samples and data. Nevertheless, the extent of the actual activities and the impact of the level of networking and harmonisation have not been fully assessed. To address these issues and to obtain missing knowledge on the extent of biobanking in Europe, the Institute for Prospective Technological Studies (IPTS) of the European Commission's Joint Research Centre, in collaboration with the European Science and Technology Observatory (ESTO), conducted a survey among European biobanks. In total, 126 biobanks from 23 countries responded to the survey. Most of them are small or medium-sized public collections set up either for population-based or disease-specific research purposes. The survey indicated a limited networking among the infrastructures. The large majority of them are stand-alone collections and only about half indicated to have a policy for cross-border sharing of samples. Yet, scientific collaborations based on the use of each biobank appear to be prominent. Significant variability was found in terms of consent requirements and related procedures as well as for privacy and data protection issues among the biobanks surveyed. To help promote networking of biobanks and thus maximise public health benefits, at least some degree of harmonisation should be achieved.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".