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https://www.frontiersin.org/articles/10.3389/fdata.2022.883341/full http://schema.org/contentUrl https://www.frontiersin.org/articles/10.3389/fdata.2022.883341/full https://www.frontiersin.org/articles/10.3389/fdata.2022.883341/full http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://www.frontiersin.org/articles/10.3389/fdata.2022.883341/full http://schema.org/dateCreated 2023-01-11 08:09:52.086059+00:00 https://www.frontiersin.org/articles/10.3389/fdata.2022.883341/full http://schema.org/dateModified 2023-01-11 08:09:53.313682+00:00 https://www.frontiersin.org/articles/10.3389/fdata.2022.883341/full http://schema.org/description 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). 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