
The Cross-domain Interoperability Framework (CDIF) provides a semantic foundation for enabling Semantic Croissant across diverse research domains. CDIF defines a common, extensible knowledge graph model that harmonizes dataset structures, variables, and metadata using shared concepts and controlled vocabularies. By aligning Croissant descriptions with CDIF, datasets become machine-interpretable, domain-agnostic, and interoperable, supporting seamless integration across repositories, disciplines, and AI workflows. This approach strengthens FAIR compliance and enables scalable reuse of research data within EOSC and beyond.
Machine Learning, Artificial intelligence, Artificial Intelligence, Health Information Interoperability, Machine learning, Interoperability
Machine Learning, Artificial intelligence, Artificial Intelligence, Health Information Interoperability, Machine learning, Interoperability
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
