Bibliographic record
Abstract
This is the Data Management Plan for "EUbOPEN -- Enabling & Unlocking Biology in the OPEN". EUbOPEN is an IMI-funded public-private partnership made up of 22 partners with a view to assembling open access chemogenomic libraries, chemical probes, primary patient cell-based assays and develop infrastructure and technologies to support this work. A major output of EUbOPEN are datasets relating to the physical reagents and molecules that are generated as part of this work, which includes their characterisation and recommended use. This Data Management Plan describes how EUbOPEN will capture, manage, curate and disseminate its data and associated metadata throughout the lifespan of the project. It is a living document and will be routinely reviewed, amended and enhanced as the project evolves. This initial version has been agreed by the project and made available within EUbOPEN as deliverable D10.8. Updated versions will be made available on an annual basis or whenever there is an agreed significant change to the data management strategy.
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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.227 | 0.319 |
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".