How do the FC4E components improve our workflow? Perspectives from our user communities.
Bibliographic record
Abstract
Presentation of Case Studies from the FAIRCORE4EOSC Project, presented at the FAIRFest in DenHaag, 20.- 21. February 2025. The FAIRCORE4EOSC project is developing and realising FAIR enhancing services for the European Open Science Cloud (EOSC). Leveraging existing technologies and services, nine new services aim to improve the discoverability and interoperability of an ever-increasing amount of research outputs. To ensure that these new services are co-designed and tailored to the needs of their future users, five case studies led by thematic and data infrastructure EOSC communities drove the development and testing of the new components. In the FAIRCORE4EOSC Case Study session, these case studies presented their work, showcasing the test implementation of the new services for FAIR data management to an interested audience and told about their lesson-learnt. Diverse approaches to enhancing data stewardship across various disciplines were highlighted and allowed fresh and exclusive insights into the practical data management work of several EOSC communities. The Case Study representatives Maxence Azzouz-Thuderoz (Mathematics), Joonas Nikkanen (European Integration of National-level Services), Chris Ariyo (Service Providers and Research Data Management Communities), Willem Elbers (Social Sciences & Humanities) and Beate Krüss (Climate Change) demonstrated, how the FAIRCORE4EOSC services can make life easier for research data managers and data stewards through enhanced machine-actionability, as well as improved findability and metadata quality for researchers and infrastructure managers in their respective fields.
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 | 0.073 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".