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## Abstract
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## Abstract
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Haiqing Yin<sup>a</sup>, Bin Xu<sup>a</sup>, Xue Jiang<sup>a</sup>, Cong Zhang<sup>a</sup>, Ruijie Zhang<sup>a</sup>, Yongwei Wang<sup>a</sup>, Shuichi Iwata<sup>b</sup>, Xuanhui Qu<sup>a</sup>
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**Haiqing Yin<sup>a</sup>, Bin Xu<sup>a</sup>, Xue Jiang<sup>a</sup>, Cong Zhang<sup>a</sup>, Ruijie Zhang<sup>a</sup>, Yongwei Wang<sup>a</sup>, Shuichi Iwata<sup>b</sup>, Xuanhui Qu<sup>a</sup>**
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<sup>a</sup> University of Science and Technology Beijing,100083, China <br>
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_<sup>a</sup> University of Science and Technology Beijing,100083, China_ <br>
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<sup>b</sup> The University of Tokyo, Tokyo,113-8654, Japan
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_<sup>b</sup> The University of Tokyo, Tokyo,113-8654, Japan_
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The FAIR principles advocate for the findability, accessibility, interoperability, and reuse of digital assets. Data standards are one of the key elements for the data curation in the big data e-commerce and e-government. approaches to tackle the challenging issue of data sharing and integration. The use of standardized data formats (data standards) supports the curation and archiving, data sharing, databases integrated with software tools, and cross-database retrieval. Data standardization provides a structure for creating and maintaining data quality as well. Currently Mmaterials data standards are especially urgently necessary for machine learning and AI for novel materials design and production processing optimization via machine learning and artificial intelligence in an era of digital trans formation. In this text, materials data standardization activites bloom in China when materials gonome engineering related researches were financially supported from the national and the local in recent years. Now the effort on building the materials data stanadards in China is extensively discussed, covering the data standards system, general rules for data from the various kinds of data sources and data description for computation, high throughput experimentation, as well as the specific materials data systems. And the data standards towards the international collaboration and the data ecosystem in the future will be also proposed to reach the goal of capable to find, access, communicate and reuse data with none or minimal human intervention.
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The FAIR principles advocate for the findability, accessibility, interoperability, and reuse of digital assets. Data standards are one of the key elements for the data curation in the big data e-commerce and e-government. approaches to tackle the challenging issue of data sharing and integration. The use of standardized data formats (data standards) supports the curation and archiving, data sharing, databases integrated with software tools, and cross-database retrieval. Data standardization provides a structure for creating and maintaining data quality as well. Currently Mmaterials data standards are especially urgently necessary for machine learning and AI for novel materials design and production processing optimization via machine learning and artificial intelligence in an era of digital trans formation. In this text, materials data standardization activites bloom in China when materials gonome engineering related researches were financially supported from the national and the local in recent years. Now the effort on building the materials data stanadards in China is extensively discussed, covering the data standards system, general rules for data from the various kinds of data sources and data description for computation, high throughput experimentation, as well as the specific materials data systems. And the data standards towards the international collaboration and the data ecosystem in the future will be also proposed to reach the goal of capable to find, access, communicate and reuse data with none or minimal human intervention.
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## Biography
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## Biography
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> Materials Data Standards and the development in China
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> **Materials Data Standards and the development in China**
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Haiqing YIN is professor in University of Science and Technology Beijing (USTB), China. Her research focuses on material cross-scale design, database, machine learning and advanced materials on nickel-based superalloys, high-entropy alloys and boride cermets and steels, with over 100 papers published and more than 10 patents issued. She got her bachelor and master degree in Xi’an Jiaotong University, and PhD in USTB.
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Haiqing YIN is professor in University of Science and Technology Beijing (USTB), China. Her research focuses on material cross-scale design, database, machine learning and advanced materials on nickel-based superalloys, high-entropy alloys and boride cermets and steels, with over 100 papers published and more than 10 patents issued. She got her bachelor and master degree in Xi’an Jiaotong University, and PhD in USTB.
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