Adopting open-science infrastructure in the ENIGMA-Parkinson’s Disease consortium: A case study
Bibliographic record
Abstract
The adoption of open science principles for data has been instrumental in enabling the creation and reuse of large datasets and efficient collaboration, but requires a dedicated open data sharing infrastructure. Although clinical neuroscience research increasingly involves large-scale collaboration on controlled, sensitive data, open science principles can bring key benefits in this setting. The unique privacy and access requirements of clinical research create challenges that require a specialized data infrastructure, focused on local governance. Here we present a narrative account of a pilot project to establish such an infrastructure for the data of the ENIGMA Parkinson’s Disease working group by adopting the decentralized Nipoppy and Neurobagel tool stack. Our collaboration has led to the adoption of community data standards among international sites of the working group and has launched an open portal that enables the cross-site discovery of cohorts based on harmonized metadata. We discuss our approach to this collaboration, the social and technical challenges we have encountered and how we addressed them in the hope that groups in similar situations may benefit from our experiences.
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.
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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| 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, 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".