Establishing a core set of open science practices in biomedicine: a modified Delphi study
Bibliographic record
Abstract
Abstract Background Mandates and recommendations related to embedding open science practices within the research lifecycle are increasingly common. Few stakeholders, however, are monitoring compliance to their mandates or recommendations. It is necessary to monitor the current state of open science to track changes over time and to identify areas to create interventions to drive improvements. Monitoring open science practices requires that they are defined and operationalized. Involving the biomedical community, we sought to reach consensus on a core set of open science practices to monitor at biomedical research institutions. Methods and Findings To establish consensus in a structured and systematic fashion, we conducted a modified 3-round Delphi study. Participants in Round 1 were 80 individuals from 20 biomedical research institutions that exhibit interest in or actively support open science. Participants were research administrators, researchers, specialists in dedicated open science roles, and librarians. In Rounds 1 and 2, participants completed an online survey evaluating a set of potential open science practices that could be important and meaningful to monitor in an automated institutional open science dashboard. Participants voted on the inclusion of each item and provided a rationale for their choice. We defined consensus as 80% agreement. Between rounds, participants received aggregated voting scores for each item and anonymized comments from all participants, and were asked to re-vote on items that did not reach consensus. For Round 3, we hosted two half- day virtual meetings with 21 and 17 participants respectively to discuss and vote on all items that had not reached consensus after Round 2. Ultimately, participants reached consensus to include a 19 open science practices. Conclusions A group of international stakeholders used a modified Delphi process to agree upon open science practices to monitor in a proposed open science dashboard for biomedical institutions. The core set of 19 open science practices identified by participants will form the foundation for institutional dashboards that display compliance with open science practices. They will now be assessed and tested for automatic inclusion in terms of technical feasibility. Using user-centered design, participating institutions will be involved in creating a dashboard prototype, which can then be implemented to monitor rates of open science practices at biomedical institutions. Our methods and approach may also transfer to other research settings–other disciplines could consider using our consensus list as a starting point for agreement upon a discipline-specific set of open science practices to monitor. The findings may also be of broader value to the development of policy, education, and interventions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchOpen science Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
| gpt | MetaresearchOpen science Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.173 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".