FFMDFPA: a FAIRification framework for materials data with no-code flexible semi-structured parser and application programming interfaces

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dc.contributor.authorHe, Ben_AU
dc.contributor.authorGong, Zen_AU
dc.contributor.authorAvdeev, Men_AU
dc.contributor.authorShi, Sen_AU
dc.date.accessioned2024-02-29T23:15:23Zen_AU
dc.date.available2024-02-29T23:15:23Zen_AU
dc.date.issued2023-08-28en_AU
dc.date.statistics2024-02-27en_AU
dc.description.abstractThe FAIR Data Principles are guidelines to ensure Findability, Accessibility, Interoperability, and Reusability of digital resources, which are essential to accelerate data-driven materials science. Despite the development and growing adoption of the FAIR principles, appropriate implementation solutions and software to make data FAIR are still sparse, particularly in standardization of heterogeneous data and subsequent data access. Here, we introduce a FAIRification Framework for Materials Data with No-Code Flexible Semi-Structured Parser and API (FFMDFPA) (API, application programming interface) for raw data processing. Using a template-based parser, FFMDFPA can extract and transform semistructured data in various text formats, providing the flexibility to extend data manipulation without coding. Additionally, FFMDFPA provides a standardized API with efficient query syntax that facilitates seamless data sharing. Taking various text files generated by computational software as examples, we demonstrate the potential utility of FFMDFPA. This work offers important insights toward efficient utilization and reuse of materials data, and the data semantic manipulation implemented in the parser and API can be extended to textual data, which has implications for future data FAIRification. © American Chemical Societyen_AU
dc.description.sponsorshipThis work is supported by the National Key Research and Development Program of China (No. 2021YFB3802100) and the National Natural Science Foundation of China (No. 92270124). We appreciate the High Performance Computing Center of Shanghai University, and Shanghai Engineering Research Center of Intelligent Computing System (No. 19DZ2252600).en_AU
dc.format.mediumPrint-Electronicen_AU
dc.identifier.citationHe, B., Gong, Z., Avdeev, M., & Shi, S. (2023). FFMDFPA: a FAIRification framework for materials data with no-code flexible semi-structured parser and application programming interfaces. Journal of Chemical Information and Modeling, 63(16), 4986-4994. doi:10.1021/acs.jcim.3c00836en_AU
dc.identifier.issn1549-9596en_AU
dc.identifier.issn1549-960Xen_AU
dc.identifier.issue16en_AU
dc.identifier.journaltitleJournal of Chemical Information and Modelingen_AU
dc.identifier.pagination4986-4994en_AU
dc.identifier.urihttp://dx.doi.org/10.1021/acs.jcim.3c00836en_AU
dc.identifier.urihttps://apo.ansto.gov.au/handle/10238/15508en_AU
dc.identifier.volume63en_AU
dc.languageEnglishen_AU
dc.language.isoenen_AU
dc.publisherAmerican Chemical Societyen_AU
dc.subjectDataen_AU
dc.subjectMaterialsen_AU
dc.subjectProgrammingen_AU
dc.subjectInformationen_AU
dc.subjectResearch programsen_AU
dc.subjectData processingen_AU
dc.titleFFMDFPA: a FAIRification framework for materials data with no-code flexible semi-structured parser and application programming interfacesen_AU
dc.typeJournal Articleen_AU
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