Policy-aware data lakes: a flexible approach to achieve legal interoperability for global research collaborations
Bibliographic record
Abstract
A popular model for global scientific repositories is the data commons, which pools or connects many datasets alongside supporting infrastructure. A data commons must establish legally interoperability between datasets to ensure researchers can aggregate and reuse them. This is usually achieved by establishing a shared governance structure. Unfortunately, governance often takes years to negotiate and involves a trade-off between data inclusion and data availability. It can also be difficult for repositories to modify governance structures in response to changing scientific priorities, data sharing practices, or legal frameworks. This problem has been laid bare by the sudden shock of the COVID-19 pandemic. This paper proposes a rapid and flexible strategy for scientific repositories to achieve legal interoperability: the policy-aware data lake. This strategy draws on technical concepts of modularity, metadata, and data lakes. Datasets are treated as independent modules, which can be subject to distinctive legal requirements. Each module must, however, be described using standard legal metadata. This allows legally compatible datasets to be rapidly combined and made available on a just-in-time basis to certain researchers for certain purposes. Global scientific repositories increasingly need such flexibility to manage scientific, organizational, and legal complexity, and to improve their responsiveness to global pandemics.
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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 | 0.112 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.020 | 0.069 |
| Open science | 0.010 | 0.042 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".