How Many Health Research Biobanks Are There?
Bibliographic record
Abstract
Introduction: It is important for many research stakeholders to know how many biobanks exist. There are several potential data sources that might be expected to provide biobank numbers, such as institutions, research funders, and literature databases (e.g., PubMed), but in practice this information is rarely available and is hard to find. However, the maturation of several online health research biobank locators (also known as directories and catalogs) that relate to 12 countries and/or states has now provided some initial data to address the question of how many health research biobanks exist in relation to population size. Methods: We have analyzed four biobank locators: the Biobanking and Biomolecular Resources Research Infrastructure—European Research Infrastructure Consortium directory, the Canadian Tissue Repository Network locator, the Australian New South Wales Australia Health Pathology locator, and the UK Clinical Research Collaboration Tissue Directory. Results: We conclude that across these locators, and in those regions with potential for high research capacity as indicated by comparable gross domestic products, there are 11–30 health research biobanks/million population (2 large biobanks with >1000 samples and a further 9–28 are medium–small biobanks). Conclusion: Many locators were established primarily to increase utilization of biobanks. However, locators may be more useful in tracking the numbers of biobanks and in assisting funders and institutions to monitor research strategy and prevent unnecessary duplication of biobank resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".