Reproducible Semantic Data Management Workflow for Materials Data Science: Generating Knowledge Graphs with Robust FAIRifcation Pipelines

Combining data from multiple sources is crucial for efficient knowledge aggregation in materials data science. FAIR data from ontology and Linked Data principles enable this. Semantic data management streamlines data exchange and aggregation, ensuring information is available and extractable. FAIRLinked and GraphDB provide solutions for consolidating, hosting, and extracting meaningful insight from multimodal data.

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Published
2026-01-14
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Research Data Management Practices
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article
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Reproducible Semantic Data Management Workflow for Materials Data Science: Generating Knowledge Graphs with Robust FAIRifcation Pipelines

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Research Data Management Practices
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Reproducible Semantic Data Management Workflow for Materials Data Science: Generating Knowledge Graphs with Robust FAIRifcation Pipelines

Erika I. Barcelos, Roger H. French, Kyle R Henrikson, Meredith Francis, Quynh D. Tran, Isabella Giammattei, Laura S. Bruckman, Van D. Tran
article en

Abstract

Combining data from multiple sources is crucial for efficient knowledge aggregation in materials data science. FAIR data from ontology and Linked Data principles enable this. Semantic data management streamlines data exchange and aggregation, ensuring information is available and extractable. FAIRLinked and GraphDB provide solutions for consolidating, hosting, and extracting meaningful insight from multimodal data.

Industry, innovation and infrastructure
Openalex Percentile: Top 60%
Research Data Management Practices
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