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Materials Data Science Ontology(MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science

Journal Article · · Scientific Data (Online)
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  1. Case Western Reserve Univ., Cleveland, OH (United States); Materials Data Science for Stockpile Stewardship Center of Excellence, Cleveland, OH (United States)
  2. Materials Data Science for Stockpile Stewardship Center of Excellence, Cleveland, OH (United States); Case Western Reserve Univ., Cleveland, OH (United States)
  3. Materials Data Science for Stockpile Stewardship Center of Excellence, Cleveland, OH (United States)
  4. Materials Data Science for Stockpile Stewardship Center of Excellence, Cleveland, OH (United States); Univ. of Central Florida, Orlando, FL (United States)
  5. Case Western Reserve Univ., Cleveland, OH (United States)
  6. Univ. of Central Florida, Orlando, FL (United States)
  7. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
  8. Argonne National Laboratory (ANL), Argonne, IL (United States)
  9. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  10. Case Western Reserve Univ., Cleveland, OH (United States); Materials Data Science for Stockpile Stewardship Center of Excellence, Cleveland, OH (United States); Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER), Lubbock, TX (United States)
  11. Materials Data Science for Stockpile Stewardship Center of Excellence, Cleveland, OH (United States); Case Western Reserve Univ., Cleveland, OH (United States); Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER), Lubbock, TX (United States)
+ Show Author Affiliations
Ontologies have gained popularity in the scientific community as a way to standardize terminologies in organizations’ data. Although certain cohorts have created frameworks with rules and guidelines on creating ontologies, there exist significant variations in how Materials Science ontologies are currently developed. We seek to provide guidance in the form of a unified automated framework for developing interoperable and modular ontologies for Materials Data Science that simplifies the ontology terms matching by establishing a semantic bridge up to the Basic Formal Ontology(BFO). This framework provides key recommendations on how ontologies should be positioned within the semantic web, what knowledge representation language is recommended, and where ontologies should be published online to boost their findability and interoperability. Two fundamental components of the MDS-Onto framework are the bilingual package called FAIRmaterials for ontology creation and FAIRLinked, for FAIR data creation. To showcase the practical capabilities of FAIRmaterials, we present two exemplar domain ontologies of MDS-Onto: Synchrotron X-Ray Diffraction and Photovoltaics.
Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States). Advanced Photon Source (APS); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
Grant/Contract Number:
89233218CNA000001; AC02-06CH11357; AC52-07NA27344; EE0009347; EE0009353; NA0004104
OSTI ID:
2569621
Alternate ID(s):
OSTI ID: 2575588
Report Number(s):
LA-UR--24-28348; LLNL--JRNL-868140; 190049
Journal Information:
Scientific Data (Online), Journal Name: Scientific Data (Online) Journal Issue: 1 Vol. 12; ISSN 2052-4463
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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