001034332 001__ 1034332
001034332 005__ 20250625202305.0
001034332 0247_ $$2doi$$a10.1007/s00521-024-10674-5
001034332 0247_ $$2ISSN$$a0941-0643
001034332 0247_ $$2ISSN$$a1433-3058
001034332 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-07117
001034332 037__ $$aFZJ-2024-07117
001034332 082__ $$a004
001034332 1001_ $$0P:(DE-Juel1)185910$$aIhsan, Ahmad Zainul$$b0$$ufzj
001034332 245__ $$aModeling dislocation dynamics data using semantic web technologies
001034332 260__ $$aLondon$$bSpringer$$c2025
001034332 3367_ $$2DRIVER$$aarticle
001034332 3367_ $$2DataCite$$aOutput Types/Journal article
001034332 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1750830234_23132
001034332 3367_ $$2BibTeX$$aARTICLE
001034332 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001034332 3367_ $$00$$2EndNote$$aJournal Article
001034332 520__ $$aThe research in Materials Science and Engineering focuses on the design, synthesis, properties, and performance of materials. An important class of materials that is widely investigated are crystalline materials, including metals and semiconductors. Crystalline material typically contains a specific type of defect called “dislocation”. This defect significantly affects various material properties, including bending strength, fracture toughness, and ductility. Researchers have devoted a significant effort in recent years to understanding dislocation behaviour through experimental characterization techniques and simulations, e.g., dislocation dynamics simulations. This paper presents how data from dislocation dynamics simulations can be modelled using semantic web technologies through annotating data with ontologies. We extend the dislocation ontology by adding missing concepts and aligning it with two other domain-related ontologies (i.e., the Elementary Multi-perspective Material Ontology and the Materials Design Ontology), allowing for efficiently representing the dislocation simulation data. Moreover, we present a real-world use case for representing the discrete dislocation dynamics data as a knowledge graph (DisLocKG) which can depict the relationship between them. We also developed a SPARQL endpoint that brings extensive flexibility for querying DisLocKG.
001034332 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001034332 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001034332 7001_ $$0P:(DE-Juel1)190520$$aFathalla, Said$$b1$$ufzj
001034332 7001_ $$0P:(DE-Juel1)186075$$aSandfeld, Stefan$$b2$$eCorresponding author
001034332 773__ $$0PERI:(DE-600)1480526-1$$a10.1007/s00521-024-10674-5$$p11737-11753$$tNeural computing & applications$$v37$$x0941-0643$$y2025
001034332 8564_ $$uhttps://juser.fz-juelich.de/record/1034332/files/s00521-024-10674-5.pdf$$yOpenAccess
001034332 8767_ $$d2025-01-06$$eHybrid-OA$$jDEAL
001034332 909CO $$ooai:juser.fz-juelich.de:1034332$$popenaire$$popen_access$$pOpenAPC_DEAL$$pdriver$$pVDB$$popenCost$$pdnbdelivery
001034332 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185910$$aForschungszentrum Jülich$$b0$$kFZJ
001034332 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190520$$aForschungszentrum Jülich$$b1$$kFZJ
001034332 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186075$$aForschungszentrum Jülich$$b2$$kFZJ
001034332 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001034332 9141_ $$y2025
001034332 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001034332 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
001034332 915pc $$0PC:(DE-HGF)0002$$2APC$$aDFG OA Publikationskosten
001034332 915pc $$0PC:(DE-HGF)0113$$2APC$$aDEAL: Springer Nature 2020
001034332 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2023-10-21
001034332 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001034332 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNEURAL COMPUT APPL : 2022$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2023-10-21$$wger
001034332 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001034332 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bNEURAL COMPUT APPL : 2022$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-21
001034332 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-21
001034332 920__ $$lyes
001034332 9201_ $$0I:(DE-Juel1)IAS-9-20201008$$kIAS-9$$lMaterials Data Science and Informatics$$x0
001034332 980__ $$ajournal
001034332 980__ $$aVDB
001034332 980__ $$aUNRESTRICTED
001034332 980__ $$aI:(DE-Juel1)IAS-9-20201008
001034332 980__ $$aAPC
001034332 9801_ $$aAPC
001034332 9801_ $$aFullTexts