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

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 journal › Article › Academic › peer-review

23 Citations (Scopus)

232 Downloads (Pure)

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.

Original language	English
Article number	883341
Journal	Frontiers in Big Data
Volume	5
DOIs	https://doi.org/10.3389/fdata.2022.883341
Publication status	Published - 11 May 2022

Keywords

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

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10.3389/fdata.2022.883341Licence: CC BY

fdata-05-883341Final published version, 4.15 MBLicence: CC BY

Cite this

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title = "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.",

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note = "Funding Information: Part of the research leading to this design of FDTs was funded by the Leiden Center of Computational Oncology, an internal grant of the Leiden University Medical Center and NeXON (Next-Generation Ontology-Driven Conceptual Modeling) financed by UNIBZ-CRC. The design of novel nanopublications was partially funded under the project “Trusted World of Corona”, TKI-LSH Health\textasciitilde{}Holland grant number LSHM20070. Publisher Copyright: Copyright {\textcopyright} 2022 Schultes, Roos, Bonino da Silva Santos, Guizzardi, Bouwman, Hankemeier, Baak and Mons.",

year = "2022",

month = may,

day = "11",

doi = "10.3389/fdata.2022.883341",

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AU - Schultes, Erik

AU - Roos, Marco

AU - Bonino da Silva Santos, Luiz Olavo

AU - Guizzardi, Giancarlo

AU - Bouwman, Jildau

AU - Hankemeier, Thomas

AU - Baak, Arie

AU - Mons, Barend

N1 - Funding Information: Part of the research leading to this design of FDTs was funded by the Leiden Center of Computational Oncology, an internal grant of the Leiden University Medical Center and NeXON (Next-Generation Ontology-Driven Conceptual Modeling) financed by UNIBZ-CRC. The design of novel nanopublications was partially funded under the project “Trusted World of Corona”, TKI-LSH Health~Holland grant number LSHM20070. Publisher Copyright: Copyright © 2022 Schultes, Roos, Bonino da Silva Santos, Guizzardi, Bouwman, Hankemeier, Baak and Mons.

PY - 2022/5/11

Y1 - 2022/5/11

N2 - 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.

AB - 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.

KW - augmented reasoning

KW - data stewardship

KW - FAIR Digital Object

KW - FAIR Digital Twin

KW - FAIR guiding principles

KW - Knowlet

KW - machine learning

KW - nanopublications

UR - https://www.scopus.com/pages/publications/85130974323

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DO - 10.3389/fdata.2022.883341

M3 - Article

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SN - 2624-909X

VL - 5

JO - Frontiers in Big Data

JF - Frontiers in Big Data

M1 - 883341

ER -

FAIR Digital Twins for Data-Intensive Research

Abstract

Keywords

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