Q. D. T. ); Management for Synchrotron Science: Materials Data Science Ontology (M DS-Onto) for FAIR, Learning and Reasoning Roger H. French 0 1 2 3 Redad Mehdi 0 2 3 Van D. Tran 0 2 3 Gabriel O. Ponon 0 2 3 Jonah A. Bachman 0 2 3 Weiqiu Yue 0 1 2 Kyle R. Henrikson 0 2 3 Johan Buecker 0 2 3 Laura S. Bruckman 0 2 3 Frank Ernst 0 2 3 Neamul H. Khansur 0 2 3 Matthew A. Willard 0 2 3 Yinghui Wu 0 1 2 Quynh D. Tran 0 2 3 Erika I. Barcelos 0 2 3 Ozan Dernek 0 2 3 Generative AI 0 FAIRification, Low-level Ontology, Machine Learning, Machine Reasoning Distributed Computing High Perfor- Cleveland OH 44106 , USA) Computer and Data Sciences Department, Case Western Reserve University , 10900 Euclid Ave, Cleveland OH 44106 , USA) Materials Data Science for Stockpile Stewardship Center of Excellence, Case Western Reserve University , 10900 Euclid Ave Materials Science and Engineering Department, Case School of Engineering, Case Western Reserve University , 10900 Euclid Ave 2025 000 0 0002

The foundations of Materials Data Science (MDS) computing, including 1) the integration of distributed (e.g., Cloudera, Hadoop) and high performance computing, 2) neural network training engines (e.g., Nvidia AISC), and 3) RDF triplestore Graph Databases (e.g., Ontotext's GraphDB), mean that statistical and deep learning on sparse, massive, and historical datasets is becoming possible. Study Protocols are an essential feature of robust, data-centric, MDS studies, where data FAIRification is enabled by MDS-Onto [1] ontology and tools, transforming messy data into linked data [2,3] across Study Stages, including sample, tool, recipe, pre-processing, analysis, modeling and results publishing. These concepts, relationships and stages guide the research process allowing exchange of JSON-LD files and their deserialization with Apache Arrow into language-independent dataframes for analysis, modeling and reasoning.

 mance Computing Xray Difraction Xray Computed Tomography Declaration on -

https://mds3-coe.com/ (R. H. French); https://mds3-coe.com/ (R. Mehdi); https://mds3-coe.com/ (V. D. T. ); https://mds3-coe.com/ (G. O. P. ); https://mds3-coe.com/ (J. A. B. ); https://mds3-coe.com/ (. W. Yue); https://mds3-coe.com/ (K. R. H. ); https://mds3-coe.com/ (J. Buecker); https://mds3-coe.com/ (L. S. B. ); https://mds3-coe.com/ (F. E. ); https://mds3-coe.com/ (N. H. K. ); https://mds3-coe.com/ (M. A. W. ); https://mds3-coe.com/ (Y. W. ); https://mds3-coe.com/ (Q. D. T. ); https://mds3-coe.com/ (E. I. Barcelos); https://mds3-coe.com/ (O. Dernek)

CEUR Workshop

ISSN1613-0073 1. Citations

 1. B. P. Rajamohan et al., “Materials Data Science Ontology(MDS-Onto ) : Unifying Domain Knowledge in Materials and Applied Data Science,” Sci Data , vol. 12 , p. 628 , Apr . 2025 , doi: 10.1038/s41597-025-04938-5. 2. Van D. Tran et al., “FAIRLinked: Transform research data into FAIR-compliant RDF Data.” July 2025 . Available: https://pypi.org/project/FAIRLinked/ 3. Gabriel O. Ponon , et al., “ CEMENTO : A package to view and write ontologies directly from draw.io diagram files . ” July 27 , 2025 . Available: https://pypi.org/project/cemento/. 4. Finley Holt , et al., “FAIRshake: For the FAIR analysis of XRay Datasets .” Oct. 08 , 2024 . Available: https://pypi.org/project/FAIRshake/.