A comparative analysis of the requirements for the use of data in biobanks based in Finland, Germany, the Netherlands, Norway and the United Kingdom
Bibliographic record
Abstract
To understand the causes of disease and improve diagnosis and treatment regimes, biomedical researchers need access to large numbers of well-characterized data and samples. Over the past decade, biobanks have been established across Europe to collect and manage access to data and samples. The challenge that we face is how to develop the tools and collaborations to enable researchers to access samples and data from a network of biobanks, rather than applying to individual biobanks. One of the perceived stumbling blocks to achieving this is represented by the different legal requirements in each country. The aim of the BioSHaRE-European Union (EU) project is to address these challenges by developing tools and methods for researchers to access and use pooled data from different cohort and biobank studies. The purpose of this article is to identify and compare the key legal requirements regarding research use of data across biobanks based in Finland, Germany, the Netherlands, Norway and the UK. Our investigation starts with the analysis of the key differences for the use of data between these countries. As a result, we identified three key areas where legal requirements differ across the five BioSHaRE-EU jurisdictions, namely, in the definition of personal data, the requirements regarding pseudonymization and processing for medical research purposes. This article provides an overview of these differences and describes them in the light of the proposed EU regulation on data protection.
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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 | 0.205 | 0.387 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".