Effective data governance and management are necessary but challenging prerequisites for creating value from data assets. Findability, accessibility, interoperability, and reusability are guiding principles for data owners in managing and archiving datasets, known as the FAIR Principles. Dataspaces provide an infrastructure for heterogeneous, multi-source data integration and cross-organizational data sharing that would benefit from FAIR compliance. In this paper, we propose semantics as an approach to ensure data FAIRness, enabling machine-aided discovery and reuse of data in different formats and structures. We conduct a systematic literature review to translate the overarching principles into ten concrete methods that can be implemented using semantic technologies. In addition, we analyze three mature dataspace initiatives for their adherence to the FAIR Principles and describe their specific implementation. In summary, we argue that semantics provide a common and infrastructure-independent foundation for data management in emerging dataspaces.

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