Open Data in the Era of the GDPR: Lessons from the Human Cell Atlas
Bibliographic record
Abstract
The Human Cell Atlas (HCA) is striving to build an open community that is inclusive of all researchers adhering to its principles and as open as possible with respect to data access and use. However, open data sharing can pose certain challenges. For instance, being a global initiative, the HCA must contend with a patchwork of local and regional privacy rules. A notable example is the implementation of the European Union General Data Protection Regulation (GDPR), which caused some concern in the biomedical and genomic data-sharing community. We examine how the HCA's large, international group of researchers is investing tremendous efforts into ensuring appropriate sharing of data. We describe the HCA's objectives and governance, how it defines open data sharing, and ethico-legal challenges encountered early in its development; in particular, we describe the challenges prompted by the GDPR. Finally, we broaden the discussion to address tools and strategies that can be used to address ethical data governance.
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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 | 0.040 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".