Introduction to Data Models Special Collection

Innovations such as data harmonization (Niemi, Näppilä & Järvelin, 2009), interoperability frameworks (Hughes et al., 2016), and feature extraction tools (Bhattacharyya, Organisciak & Downie, 2015) 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 (Kwasnikowska, Moreau & Bussche, 2015) and the Research Object Model (Belhajjame et al., 2015), as well as ontologies such as the the Semantic Web for Earth and Environmental Terminology (SWEET) and the Gene Ontology. However, the formal literature of data science often glosses over or excludes the design work that goes into developing and implementing data models (Simsion, 2007). 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. The Data Science Journal will curate a new special collection of papers that are thematically aligned with these issues in order to fill a needed gap in the data modeling literature. Our intention is to publish research and practices papers that shed light on new methods of design and development, as well as reviews of recently released data models. In the following sections we give a brief overview of four initial contributions to this the data modeling special collection and highlight their unique contribution to the field. We then describe some potential topics of interest for future submissions to the special collection, as well as future directions in data modeling research more generally.