FDO-Ops: A Model for Machine-Actionable FAIR Digital Objects to Enhance Interoperability

Interoperability is the key to interacting with data across systems and technologies, thereby facilitating knowledge interpretation in different scientific domains. The FAIR Principles provide guidelines for this aspect, and Knowledge Organization Systems with associated technologies provide practical solutions. However, interoperability is rarely fully achieved in reality due to limited capabilities with respect to machine actionability of these technologies. FAIR Digital Objects, when associated with operations, can fill this gap. We introduce FDO-Ops, a model and framework that includes a specification of operations and a procedure for associating and executing them on FAIR Digital Objects. On this basis, we derive a generic description of the machine-actionable entity a FAIR Digital Object constitutes. To demonstrate our approach, we provide a prototype implementation of FDO-Ops that we test with several use cases with data from the domains of energy research and digital humanities. Our evaluation indicates that machine-actionable FAIR Digital Objects based on FDO-Ops, used in conjunction with existing technologies, greatly enhance the interoperability of the digital resources they represent at the technical and syntactic levels. In addition, they offer perspectives for improving semantic, legal, and organizational interoperability.