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

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Publication date
14 January 2026
Publisher
Scholarly Commons @ Case Western Reserve University

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

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