EOSC-Life EOSC repository deployment for project demonstrators
Bibliographic record
Abstract
This deliverable addresses WP1’s objectives related to publication of data and data resources in cloud repositories by making available resources in EOSC-Life’s registries; the implementation of FAIR services and standards, by development of FAIR registries in collaboration with WP6; and evolution of repository infrastructure by making available registry entries deriving from WP3 demonstrators. Use cases for EOSC-Life registries have been defined for general and clinical research data use and the ‘EOSC-Life’ collection in FAIRsharing has been populated and released comprising more than 100 data resources and standards. A clinical MetaData Repository has also been developed to address challenges of clinical data sharing in Europe. We have engaged with EOSC, sister projects such as FAIRsFAIR and ENVRI to consider the features of registries, cross registry interoperability and use cases which link EOSC projects. We will continue to update the registries as the project progresses and as new dataset and resources are made available and will engage with pan-EOSC registry planning based on our work to date.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".