Converging on a Semantic Interoperability Framework for the European Data Space for Science, Research and Innovation (EOSC)
Bibliographic record
Abstract
This presentation is based on the paper "Converging on a Semantic Interoperability Framework for the European Data Space for Science, Research and Innovation (EOSC)" and passed at the 2nd Workshop on Ontologies for FAIR and FAIR Ontologies (Onto4FAIR), July 17-19, 2023 Sherbrooke, Québec Abstract Paper: Semantic interoperability (SI) is at the heart of the FAIR principles and the design of large-scale cross-disciplinary infrastructures. The European Open Science Cloud (EOSC) is a European-wide effort towards such an infrastructure, aiming to deepen regional research collaboration and realising a shared data space for science, research and innovation. In this context, the research community’s voice is represented by the EOSC Association (EOSC-A) and a number of advisory groups with a broad range of representatives from different stakeholder organisations. The advisory group on metadata and data quality has formed a task force focusing on developing and implementing recommendations for SI (EOSC SI Task Force) to converge on globally relevant and scalable SI solutions for EOSC. This paper provides context to SI in EOSC, the various components contributing to it, as well as some views on the socio-technical challenges to arriving at a consensus. In particular, the paper provides motivation for exploring the heterogeneity of SI solutions demonstrated across scientific communities and insight into the task force’s planned approach to conducting a survey to identify relevant components and structures. The paper is also an invitation to the global community to align and engage with the task force’s activities going forward.
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 | 0.109 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".