The Canadian Open Neuroscience Platform – An Open Science Framework for the Neuroscience Community
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
Large-scale data-centric challenges faced by neuroscientists, such as improving reproducibility and data reuse, could be overcome by adopting open science practises. The Canadian Open Neuroscience Platform (CONP) takes a multi-faceted approach to enabling open neuroscience, aiming to make research, data, and tools accessible to everyone, with the ultimate objective of accelerating discovery. Central to the tailor-made CONP infrastructure is its Portal, where datasets and analysis tools can be shared in accordance with FAIR principles. Another key piece of CONP infrastructure is NeuroLibre, a preprint server for interactive, fully reproducible scientific notebooks that embed text, figures, and code. To encourage responsible sharing, the CONP has constructed governance frameworks and toolkits that strike a balance between safeguarding the rights of data subjects and promoting widespread public benefit from scientific advancement. The CONP is also focussed on supporting the next generation of neuroscientists through its scholar and training program. The collective experience of our engaged community and leaders has generated a platform that supports multiple facets of open neuroscience, a unique approach within the neuroscience landscape. Together, the various elements of the platform serve the CONP’s vision for promoting open neuroscience and yielding the associated benefits for individual researchers and the wider community.
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 | 0.132 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.059 | 0.005 |
| Scholarly communication | 0.109 | 0.003 |
| Open science | 0.202 | 0.173 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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