{"title":"关于科研成果和专利的智能创新数据集","authors":"Xinran Wu, Hui Zou, Yidan Xing, Jingjing Qu, Qiongxiu Li, Renxia Xue, Xiaoming Fu","doi":"arxiv-2409.06936","DOIUrl":null,"url":null,"abstract":"Various stakeholders, such as researchers, government agencies, businesses,\nand laboratories require reliable scientific research outcomes and patent data\nto support their work. These data are crucial for advancing scientific\nresearch, conducting business evaluations, and policy analysis. However,\ncollecting such data is often a time-consuming and laborious task.\nConsequently, many users turn to using openly accessible data for their\nresearch. However, these open data releases may suffer from lack of\nrelationship between different data sources or limited temporal coverage. In\nthis context, we present a new Intelligent Innovation Dataset (IIDS dataset),\nwhich comprises six inter-related datasets spanning nearly 120 years,\nencompassing paper information, paper citation relationships, patent details,\npatent legal statuses, funding information and funding relationship. The\nextensive contextual and extensive temporal coverage of the IIDS dataset will\nprovide researchers with comprehensive data support, enabling them to delve\ninto in-depth scientific research and conduct thorough data analysis.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"53 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent Innovation Dataset on Scientific Research Outcomes and Patents\",\"authors\":\"Xinran Wu, Hui Zou, Yidan Xing, Jingjing Qu, Qiongxiu Li, Renxia Xue, Xiaoming Fu\",\"doi\":\"arxiv-2409.06936\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Various stakeholders, such as researchers, government agencies, businesses,\\nand laboratories require reliable scientific research outcomes and patent data\\nto support their work. These data are crucial for advancing scientific\\nresearch, conducting business evaluations, and policy analysis. However,\\ncollecting such data is often a time-consuming and laborious task.\\nConsequently, many users turn to using openly accessible data for their\\nresearch. However, these open data releases may suffer from lack of\\nrelationship between different data sources or limited temporal coverage. In\\nthis context, we present a new Intelligent Innovation Dataset (IIDS dataset),\\nwhich comprises six inter-related datasets spanning nearly 120 years,\\nencompassing paper information, paper citation relationships, patent details,\\npatent legal statuses, funding information and funding relationship. The\\nextensive contextual and extensive temporal coverage of the IIDS dataset will\\nprovide researchers with comprehensive data support, enabling them to delve\\ninto in-depth scientific research and conduct thorough data analysis.\",\"PeriodicalId\":501285,\"journal\":{\"name\":\"arXiv - CS - Digital Libraries\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Digital Libraries\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.06936\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06936","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligent Innovation Dataset on Scientific Research Outcomes and Patents
Various stakeholders, such as researchers, government agencies, businesses,
and laboratories require reliable scientific research outcomes and patent data
to support their work. These data are crucial for advancing scientific
research, conducting business evaluations, and policy analysis. However,
collecting such data is often a time-consuming and laborious task.
Consequently, many users turn to using openly accessible data for their
research. However, these open data releases may suffer from lack of
relationship between different data sources or limited temporal coverage. In
this context, we present a new Intelligent Innovation Dataset (IIDS dataset),
which comprises six inter-related datasets spanning nearly 120 years,
encompassing paper information, paper citation relationships, patent details,
patent legal statuses, funding information and funding relationship. The
extensive contextual and extensive temporal coverage of the IIDS dataset will
provide researchers with comprehensive data support, enabling them to delve
into in-depth scientific research and conduct thorough data analysis.