Why Cochrane should prioritise sharing data
Bibliographic record
Abstract
Why Cochrane should prioritise sharing data Open sharing is vital for collaboration, innovation, and reproducibility: Cochrane could show leadership.Packer 1 discusses that the one who submits a research for public good should be ready to receive a request for data sharing for examination and re-analysis and tax payers assume that a national agency is checking such data and analysis.Here we discuss Cochrane's practice on data sharing.Open science, as endorsed by the G7, 2 includes sharing data, computer code and materials.It is essential for reproducibility, collaboration, and innovation.We support the work of Cochrane, but are concerned Cochrane is not sharing all its reviews' data.These data should be fully accessible for re-use by third parties.Cochrane, a non-profit private company 3 and registered charity, produces and maintains systematic reviews in health and social care.Its work is undertaken by a global network of thousands of people, 4 and its support largely comes from public funding.5 Most people producing Cochrane reviews are volunteers, not specifically funded for this work 6 7 and Cochrane encourages 'crowdsourcing' of work.[8][9][10] Cochrane Editorial bases help volunteers obtain study reports and manually extract the wealth of data needed to generate systematic reviews.[11][12][13] Cochrane teams use RevMan software 14 to produce files in standard format (XML), storing information on the studies, their methods and results for publication in the Cochrane Library.
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 | 0.198 | 0.671 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.145 | 0.121 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".