The global emergence of epidemiological biobanks: opportunities and challenges
Bibliographic record
Abstract
This chapter discusses the emergence of biobanks around the world. Specifically, it considers the scientific role of biobanks and the scientific and ethico-legal challenges of biobanks. The science of biobanking faces a number of important challenges. However, two in particular would appear to be fundamental. From the perspective of the science, the primary challenge is to increase the quantity, quality, and utility of the information that will ultimately be stored as data and samples in the biobanks being set up today. On the ethico-legal side, the challenge is to ensure that everybody (governments, nongovernmental organizations, policy makers, funders, researchers, the general public, and study participants) understands what modern biobanking is really about, and that legal systems and ethical review mechanisms as applied to biobanks are therefore enabling and fit-for-purpose. Regulatory and governance systems must promote good practices — that facilitate effective science — without imposing risk or unnecessary cost on willing and consenting participants, and must enhance the prospect of legitimate information flow around the world.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".