Research data systems have matured greatly over the last decade - partly in response to the growing complexity, amount, and heterogeneity of research data. Innovations such as data harmonization, interoperability frameworks, and feature extraction tools are greatly improving the capabilities of research communities to access and manipulate data in computing systems. Underpinning these new systems-level features and functionalities are a number of robust conceptual, logical, and physical data models. These include dataand curation-oriented models such as the Open Provenance Model and the Research Object Model, as well as ontologies of observable phenomena and objects such as the the Semantic Web for Earth and Environmental Terminology (SWEET) ontologies and the Gene Ontology.
Unfortunately, the formal literature of data science often glosses over or excludes the design work that goes into developing and implementing these models. As a result it is often unclear how or why design decisions were made, or what advances and new techniques have been developed for data modeling and knowledge representation as they are applied to research data. This special issue seeks contributions from the Data Science community on the development, implementation, and evolution of data models and ontologies - including the use of knowledge representation languages like RDF and OWL in advancing the capabilities of research data systems. We welcome submissions that report on empirical research that is completed or in progress, as well as pieces that can clearly articulate grand challenges and opportunities for advancing our current understanding of data models, research data curation systems, and knowledge.