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

doi:10.3389/fdata.2022.883341

FAIR Digital Twins for Data-Intensive Research

Front Big Data. 2022 May 11:5:883341. doi: 10.3389/fdata.2022.883341. eCollection 2022.

Authors

Erik Schultes^{1

2}, Marco Roos^{1

3}, Luiz Olavo Bonino da Silva Santos⁴, Giancarlo Guizzardi^{4

5}, Jildau Bouwman^{1

6}, Thomas Hankemeier⁷, Arie Baak⁸, Barend Mons^{1

2

3

7}

Affiliations

¹ Leiden Institute for FAIR and Equitable Science, Leiden, Netherlands.
² GO FAIR Foundation, Leiden, Netherlands.
³ Human Genetics Department, Leiden University Medical Center, Leiden, Netherlands.
⁴ Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands.
⁵ Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
⁶ Netherlands Organisation for Applied Scientific Research, Zeist, Netherlands.
⁷ Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands.
⁸ Euretos, Utrecht, Netherlands.

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.

Keywords: FAIR Digital Object; FAIR Digital Twin; FAIR guiding principles; Knowlet; augmented reasoning; data stewardship; machine learning; nanopublications.