Open questions on granular, perspective-sensitive FAIRness assessment

Develop a granular and perspective-sensitive framework for assessing FAIRness (Findable, Accessible, Interoperable, Reusable) at multiple levels of information granularity in datasets structured as semantic units and nested FAIR Digital Objects; Determine which parts of a dataset are FAIR for which stakeholders and why; Devise methods to improve FAIRness within specified use scenarios; Establish principled rules for how FAIRness scores propagate through nested modular data and knowledge structures; Derive the relationship between FAIRness scores and a dataset’s granular complexity; Define procedures for calculating cumulated FAIRness across granularity levels with differing FAIRness; and Ascertain criteria for task-dependent sufficiency of FAIRness for a given task.

Background

The paper introduces the Grammar of FAIR, proposing semantic units and nested FAIR Digital Objects (FDOs) as granular, machine-actionable carriers of meaning. In this framework, FAIRness should be evaluated not only at the dataset level but also at the level of individual statement units and compound/nested units, reflecting differences in granular richness and depth.

Within this granular paradigm, the authors identify unresolved issues about how to operationalize FAIRness assessment: identifying which components are FAIR for which stakeholders, improving FAIRness for specific use scenarios, defining propagation of FAIRness across nested units, relating FAIRness to granular complexity, aggregating FAIRness across levels, and determining task-dependent sufficiency. Addressing these open questions is framed as necessary to realize the Internet of FAIR Data and Services (IFDS).