FAIRplus use case IMI EUbOPEN: Open by design saves time
Bibliographic record
Abstract
EUbOPEN is an international consortium of 22 partners from academia and industry, funded by IMI/IHI. The goal of EUbOPEN is to create a library of compounds binding to 1,000 proteins. These ~5,000 compounds will be well characterized for their ability to interact with human proteins within their native environment, the cell. EUbOPEN is fully committed to Open Science and thus aims to publish all its generated data open and fully accessible to everyone. Therefore, the EUbOPEN project has been working with experts from the FAIRplus project from the start. The IMI FAIRplus project aims to develop tools and guidelines for making life science data FAIR (Findable, Accessible, Interoperable, Reusable). In the past year, the so-called ‘squad teams’ from FAIRplus, consisting of experts working in universities and pharmaceutical companies, have been actively working to FAIRify data sets from large IMI projects such as EUbOPEN, APPROACH, eTOX, and COMBINE. The developed tools and methods are subsequently added as ‘recipes’ to the FAIR Cookbook, enabling projects and companies with similar FAIR data challenges to apply this consolidated know-how to increase the FAIRness of their data.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.077 | 0.049 |
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".