Is There a Duty to Share? Ethics of Sharing Research Data in the Context of Public Health Emergencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Making research data readily accessible during a public health emergency can have profound effects on our response capabilities. The moral milieu of this data sharing has not yet been adequately explored. This article explores the foundation and nature of a duty, if any, that researchers have to share data, specifically in the context of public health emergencies. There are three notable reasons that stand in opposition to a duty to share one’s data, relating to: (i) data property and ownership, (ii) just distribution of benefits and burdens and (iii) the contemporary ethos of science. We argue each reason can be successfully met with corresponding rationale in favour of data sharing. Further support for data sharing has been echoed in policies of health agencies, funding bodies and academic institutions; in documents on the ethical conduct of biomedical research; and in discussions on the nature of public health. From this, we ascertain that sharing data is the morally sound default position. This article then highlights the key roles reciprocity and solidarity play in supporting the practice of data sharing. We conclude with recommendations to regard public health research data as a common-pool resource in order to build a framework for stable data sharing management.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.389 | 0.390 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.027 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it