Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3389/fdata.2022.883341 | FRONTIERS IN BIG DATA |
Keywords | DocType | Volume |
nanopublications, data stewardship, FAIR guiding principles, machine learning, FAIR Digital Twin, FAIR Digital Object, Knowlet, augmented reasoning | Journal | 5 |
ISSN | Citations | PageRank |
2624-909X | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erik Schultes | 1 | 0 | 0.34 |
| Marco Roos | 2 | 0 | 0.34 |
| Luiz Olavo Bonino da Silva Santos | 3 | 0 | 0.34 |
| Giancarlo Guizzardi | 4 | 1456 | 132.59 |
| Jildau Bouwman | 5 | 0 | 0.34 |
| Thomas Hankemeier | 6 | 0 | 0.34 |
| Arie Baak | 7 | 0 | 0.34 |
| Barend Mons | 8 | 430 | 33.31 |