Open Sharing of Data at a Canadian Neuroscience Institute
Bibliographic record
Abstract
Open sharing of research data is crucial to advancing our understanding of the human brain and discovering treatments for neuropsychiatric disorders. It is critical, however, to balance these goals with participant rights to privacy and confidentiality. We discuss the ethics of practicing open neuroscience at Baycrest Academy for Research and Education (BARE), a small but prominent Canadian neuroscience research institute based in a geriatric hospital. Through a systematic documentary analysis of applications for ethical approval, consent forms and study protocols from 244 studies, we report on data sharing trends and participant consent rates at the institute. We found that overall, 52% of these studies planned to share data. Following a significant increase over recent years, over 60% of the 91 studies launched since 2023 have data sharing plans, of which 80% (44/55) are obtaining optional consent. Analysis of optional consent rates from 2912 participants enrolled in 33 studies revealed that over 98% agreed to sharing their research data for unspecified future use. In addition to setting a benchmark for open data sharing in Canadian neuroscience institutes, we provide actionable recommendations for institutes to support their researchers in sharing neuroscience data openly, ethically, and responsibly.
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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 | 0.169 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".