Informed Consent Contexts in a Multidisciplinary Research Data Repository
Bibliographic record
Abstract
Secondary use of research data requires an understanding of the contexts in which it was collected. While depositors are often encouraged to describe methodological and structural contexts in the form of metadata and documentation, ethical contexts have received much less attention. As open data mandates and an ethos of FAIR (findable, accessible, interoperable, reuseable) data proliferate across disciplines, participant consent for unknown future secondary uses of data is increasingly sought, even for minimal risk research. Terms of broad consent generally establish limitations on data reuse, but those limitations may not be clear when data are accessed via an open repository. The absence of these contexts increases the risk that secondary uses of data will be inconsistent with the expectations of original research participants and may place unnecessary burden on research ethics boards. This study examines the dataset records in a large, multidisciplinary data repository to determine the extent to which and how informed consent information is communicated to secondary users, and the degree to which conditions of access and use of data adhere to terms of informed consent. We identified all records published in Borealis: The Canadian Dataverse Repository between January 2022 and September 2024 containing individual-level human data. From those records, we analysed the frequency with which consent information was included and methods used to do so. We further compared terms of consent with the licensing, textual, and technological conditions placed on access and use of the data. Results indicate that informed consent contexts are infrequently provided alongside data and that access and use conditions align with terms of consent for a slim majority of the sample datasets. Based on these findings, we provide recommendations for the development of repository policy and guidelines that harmonise terms of consent and data use, the standardisation of language establishing access and use conditions, the adoption of metadata schema describing ethical contexts, and additional collaboration among data stewards and research ethics boards.
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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 | 0.346 | 0.448 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.026 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".