Funders' Expectations for Open Science in Cardiovascular Research: A Scoping Review of the Largest Cardiovascular Research Funders
Bibliographic record
Abstract
Open science practices, including data sharing, open access, and prospective study registration, have been increasingly recognized to improve transparency, reproducibility, and accessibility in research, yet their uptake and implementation by cardiovascular research funders is unclear. We conducted a scoping review of publicly available policies, guidance, and grant instructions from 12 members of the Global Cardiovascular Research Funders Forum to assess expectations, monitoring, and support for open science in cardiovascular research. We included 105 documents from 9 funders; no relevant documents were identified for 3 funders. Data sharing (67%) and open access (58%) were the most common mandates by funders, followed by prospective registration (50%) and patient and public involvement (50%). Requirements for other practices, including code sharing, use of reporting guidelines, preprints, and open peer review, were uncommon. Monitoring compliance was inconsistent, with many funders not specifying any mechanisms, even for widely required practices. Where available, support was most often provided through financial assistance, guidance, or infrastructure, particularly for open access, data sharing, and patient and public involvement. These findings suggest that while cardiovascular funders are engaging with open science, policies remain uneven in scope, monitoring, and support. Coordinated efforts to strengthen and harmonize open science expectations, particularly around compliance monitoring and researcher training, will be essential to realizing the full potential of open science in cardiovascular research.
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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 | 0.569 | 0.721 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| 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; 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".