FAIR Digital Twins for Data-Intensive Research

Erik Schultes*, Marco Roos, Luiz Olavo Bonino da Silva Santos, Giancarlo Guizzardi, Jildau Bouwman, Thomas Hankemeier, Arie Baak, Barend Mons

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
53 Downloads (Pure)


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.

Original languageEnglish
Article number883341
JournalFrontiers in Big Data
Publication statusPublished - 11 May 2022


  • augmented reasoning
  • data stewardship
  • FAIR Digital Object
  • FAIR Digital Twin
  • FAIR guiding principles
  • Knowlet
  • machine learning
  • nanopublications


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