Title
FAIR Digital Twins for Data-Intensive Research
Author
Schultes, E.
Roos, M.
Bonino da Silva Santos, L.O.
Guizzardi, G.
Bouwman, J.
Hankemeier, T.
Baak, A.
Mons, B.
Publication year
2022
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. Copyright © 2022 Schultes, Roos, Bonino da Silva Santos, Guizzardi, Bouwman, Hankemeier, Baak and Mons.
Subject
Augmented reasoning
Data stewardship
FAIR Digital Object
FAIR Digital Twin
FAIR guiding principles
Knowlet
Machine learning
Nanopublications
To reference this document use:
http://resolver.tudelft.nl/uuid:f7ee7d66-ca02-4cdd-8677-ad5c063d6fb0
DOI
https://doi.org/10.3389/fdata.2022.883341
TNO identifier
980785
ISSN
2624-909X
Source
Frontiers in Big Data, 5 (5)
Document type
article