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Title: | Converging on a Semantic Interoperability Framework for the European Data Space for Science, Research and Innovation (EOSC) | Authors: | David, Romain Baumann, Kurt Le Franc, Yann Magagna, Barbara Vogt, Lars Wildmann, Heinrich Jouneau, Thomas Koivula, Hanna Madon, Bénédicte Nyberg Akerstrom, Wolmar Ojstersek, Milan Scharnhorst, Andrea Schubert, Chris Shi, Zhengdong Tanca, Letizia VANCAUWENBERGH, Sadia |
Issue Date: | 2023 | Publisher: | Zenodo | Source: | 2nd Workshop on Ontologies for FAIR and FAIR Ontologies (Onto4FAIR), Sherbrooke, Québec, Canada, May 2023 | Abstract: | 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. | Keywords: | Semantic interoperability;European Open Science Cloud;Crosswalk;Roadmap;Interoperable;FAIR Principles;definitions;linked data;machine actionability | Document URI: | http://hdl.handle.net/1942/41709 | Link to publication/dataset: | https://zenodo.org/record/8042997 | DOI: | 10.5281/zenodo.8042997 | Category: | C2 | Type: | Conference Material |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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SITeaserPaper_FOIS2023.pdf | Conference material | 751.7 kB | Adobe PDF | View/Open |
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