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

TITLE=FAIR Digital Twins for Data-Intensive Research

JOURNAL=Frontiers in Big Data

VOLUME=5

YEAR=2022

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.883341

DOI=10.3389/fdata.2022.883341

ISSN=2624-909X

ABSTRACT=<p>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.</p>