FAIR Digital Twins for Data-Intensive Research

Abstract

Although all the technical components supporting fully orchestrated Digital Twins (DT) currently exist, what remains missing is a conceptual clarification and analysis of a more generalized concept of a DT that is made FAIR, that is, universally machine actionable. This methodological overview is a first step toward this clarification. We present a review of previously developed semantic artifacts and how they may be used to compose a higher-order data model referred to here as a FAIR Digital Twin (FDT). We propose an architectural design to compose, store and reuse FDTs supporting data intensive research, with emphasis on privacy by design and their use in GDPR compliant open science.

Hourglass-based interoperability through nanopublications in VODAN-A

“…Using nanopublications as minimum units of information containing assertions about an object, allows for the creation of linked data units and, as such, visually representable graphs with a multitude of analysis possibilities [20].…”

FAIR for digital twins

“…A prime example is search. Major search engines rely on large graphs containing semantically connected information to interpret and thus better respond to user queries 14 . Similarly, specialized systems like Google's Dataset Search 15 build on semantic annotations-in this case using the schema.org vocabulary 16 [28]-to provide access to a wealth of information.…”

“…While these criteria are more and more embraced within the scientific community, also in the context of digital twins [14], their transfer to industry has not yet received similar attention. Exceptions start to emerge, e.g., in a blog post about FAIR principles for a digital twin that is "a virtual representation of the data of something in the real world" [15] or with the recent creation of a Semantic Industry (SemInd) W3C Community Group [16].…”

“…org/ 13. Of course, existing systems can check for unknown entries and handle this as an error, but the JSON(-LD) standard itself offers backward compatibility 14. https:// blog.…”

FAIR digital objects, persistent identifiers and machine actionability

“…These building blocks are important for data and information to be "Fully AI-ready" (another interpretation of the FAIR acronym) where the concept of machine actionability is central. A 2022 paper entitled "FAIR Digital Twins for Data-Intensive Research" by Schultes et al [19] proposes a modular approach to building systems based on FAIR Digital Objects where best practices adoption around identifiers and associated metadata will help.…”