A cross-sectional audit and survey of Open Science and Data Sharing practices at The Montreal Neurological Institute-Hospital
Bibliographic record
Abstract
Background: Open science is a movement and set of practices to conduct research more transparently. The adoption of open science has been recognized to support innovation, equity, and transparency. The Montreal Neurological Institute-Hospital (Neuro) has committed to becoming an ‘open science’ institute, the first of its kind in Canada. Here we report on an audit of open data practices in Neuro publications and on a survey of Neuro-based researchers’ barriers and facilitators to data sharing. Methods: In the first study, we retrieved 313 unique publications and collated all Neuro publications from 2019 and extracted information from each article pertaining to data sharing and other open science practices. We included all empirical papers and pre-prints that were reported in English. In the second study, one hundred twenty-four participants (out of 553) completed the survey, with a response rate of 22.42%. We surveyed all Neuro researchers. For the audit, we examined data sharing and open science practices. For the survey, we asked participants questions about their data sharing practices and perceptions. Results: We found that 66.5% of these publications (n=208) included a data sharing statement. Overall, 74.5% (n=155) of articles had data that was publicly available. When examining broader open science practices, rates of compliance tended to be lower. For example, 94.9% (n=297) of publications failed to register a protocol. Among participants who had published a first or last authored paper in the past year, most participants, 53 of 74 (71.62%), reported that they had openly shared their research data. Less than half of the participants, 37.50% (n=45), reported having engaged in training related to data sharing within the last 12 months. Conclusion: We found that half of all publications included in the audit shared data. Participants indicated an appetite for resources for learning about data-sharing signaling a willingness to perform better.
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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 | 0.039 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".