FAIRplus: D1.2 Selection criteria and guidelines for data sources from IMI projects and EFPIA internal databases
Bibliographic record
Abstract
Creating selection criteria and guidelines, to aid identification of IMI datasets with the potential to generate high societal impact upon FAIRification, is an important goal of FAIRplus. This deliverable report describes the process put in place to identify, evaluate and select projects. Two thirds of projects to be addressed by FAIRplus will cover societal priorities of H2020, namely; 1) promoting healthy ageing<sup><sup>[1]</sup></sup>; 2) addressing chronic diseases; 3) neurodegenerative diseases and 4) emergence of antibiotic resistance. In addition, we have identified cross-cutting projects “Cross” which provide tools such as cell lines, biomarkers and animal models, which enable research progression in the primary priority areas. In the first period, some 25 projects have been identified based on the application of the criteria and discussions are ongoing with these consortia representatives in order to provide a steady flow of datasets into FAIRplus. <sup><sup>[1]</sup></sup> see http://ec.europa.eu/programmes/horizon2020/en/h2020-section/health-demographic-change-and-wellbeing
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".