Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data Article Swipe
Kareem S. Aggour
,
Vijay S. Kumar
,
Vipul Gupta
,
Alfredo Gabaldon
,
Paul Cuddihy
,
Varish Mulwad
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40192-024-00348-4
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40192-024-00348-4
The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40192-024-00348-4
- https://link.springer.com/content/pdf/10.1007/s40192-024-00348-4.pdf
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
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Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR DataWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-04-09Full publication date if available
- Authors
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Kareem S. Aggour, Vijay S. Kumar, Vipul Gupta, Alfredo Gabaldon, Paul Cuddihy, Varish MulwadList of authors in order
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https://doi.org/10.1007/s40192-024-00348-4Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s40192-024-00348-4.pdfDirect link to full text PDF
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hybridOpen access status per OpenAlex
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| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I4210134512 |
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