Abstract
The concept of FAIR Digital Objects represents a foundational step towards realizing machine-actionable, interoperable data infrastructures across scientific and industrial domains. As digital spaces become increasingly heterogeneous, scalable mechanisms for data processing and interpretability are essential. This paper provides a comparative analysis of various typing mechanisms to associate FAIR Digital Objects with their operations, addressing the pressing need for a structured approach to manage data interactions within the FAIR Digital Objects ecosystem. By defining and examining three core models of typing mechanisms—record typing, profile typing, and attribute typing—this work evaluates each model’s quantitative quality indicators, using mathematical measures, and qualitative aspects. In particular, models are quantitatively evaluated with respect to their simplicity, efficiency, and flexibility, as well as being qualitatively assessed with respect to granularity, required client knowledge, and versatility, thereby shedding light on their strengths, limitations, and interoperability. With this assessment, our objective is to offer insights for the adoption of FDO frameworks that enhance data automation and promote the seamless exchange of digital resources across domains.