GO FAIR Implementation Network on Cross-Domain Interoperability of Heterogeneous Research Data
Peter Mutschke - GESIS – Leibniz Institute for the Social Sciences
Abstract:
A crucial obstacle to Open Science lies in the proliferation of domain-specific, disconnected “data silos”. Despite the existence of widely accepted standards for data representation and linking (see W3C/RDA), the data in these silos are often described using heterogeneous and often unstandardized metadata and vocabularies which cannot be easily linked with each other. This makes discovery and reuse of research data across community borders challenging tasks. A particular problem lies in the complexities of interoperability, whose different layers, ranging from encoding up to structural and semantic specifications of data, are yet to be fully understood. In the broad debate about FAIR, guidelines and recommendations are only just starting to discuss how to operationalize FAIR in research data infrastructures. Reference models that guide data providers in how best to represent their data in ways that capture the meaning of the data while ensuring interoperability without information loss are extremely rare. There is also a lack of understanding about how best to navigate between different levels of granularity in domain-specific data documentation schemes and how to map between different knowledge organization systems. Moreover, reference models need to be generic enough to be adaptable to different scientific domains. This is especially needed when it comes to linking data from different communities. The GO FAIR Implementation Network presented focuses on the “I” in FAIR, interoperability. Our focus is on addressing the specific question of semantic interoperability. The network aims at bringing together experts from the Semantic Web community, data providers and use case partners from different domains in order to apply, develop and evaluate methods, tools and guidelines for implementing and assessing the semantic interoperability of heterogeneous research (meta)data across discipline borders. The network will be research-driven from the beginning, i.e. it will be based on cross-community research use cases.