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用于数据密集型研究的FAIR数字孪生体。

FAIR Digital Twins for Data-Intensive Research.

作者信息

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

机构信息

Leiden Institute for FAIR and Equitable Science, Leiden, Netherlands.

GO FAIR Foundation, Leiden, Netherlands.

出版信息

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

DOI:10.3389/fdata.2022.883341
PMID:35647536
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9130601/
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.

摘要

尽管目前支持完全编排的数字孪生（DT）的所有技术组件都已存在，但仍缺少对更广义的数字孪生概念进行概念性澄清和分析，使其具有FAIR性，即通用的机器可操作性。本方法概述是朝着这一澄清迈出的第一步。我们回顾了先前开发的语义工件，以及它们如何用于构建一个在此称为FAIR数字孪生（FDT）的高阶数据模型。我们提出了一种架构设计，用于构建、存储和重用支持数据密集型研究的FDT，强调设计时的隐私保护以及它们在符合GDPR的开放科学中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa49/9130601/2154ebf2523f/fdata-05-883341-g0013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa49/9130601/57b40d269548/fdata-05-883341-g0005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa49/9130601/d34fc8b45598/fdata-05-883341-g0007.jpg
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