Intelligent Innovation Dataset on Scientific Research Outcomes and Patents

Xinran Wu, Hui Zou, Yidan Xing, Jingjing Qu, Qiongxiu Li, Renxia Xue, Xiaoming Fu
{"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}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
关于科研成果和专利的智能创新数据集
研究人员、政府机构、企业和实验室等各利益相关方都需要可靠的科研成果和专利数据来支持他们的工作。这些数据对于推进科学研究、开展商业评估和政策分析至关重要。因此,许多用户转而使用可公开访问的数据进行研究。然而,这些开放数据的发布可能存在不同数据源之间缺乏关联或时间覆盖范围有限的问题。在这种情况下,我们提出了一个新的智能创新数据集(IDS数据集),它由六个相互关联的数据集组成,时间跨度近120年,涵盖论文信息、论文引用关系、专利详情、专利法律状态、资助信息和资助关系。IIDS 数据集广泛的上下文和时间覆盖将为研究人员提供全面的数据支持,使他们能够深入开展科学研究并进行全面的数据分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Publishing Instincts: An Exploration-Exploitation Framework for Studying Academic Publishing Behavior and "Home Venues" Research Citations Building Trust in Wikipedia Evaluating the Linguistic Coverage of OpenAlex: An Assessment of Metadata Accuracy and Completeness Towards understanding evolution of science through language model series Ensuring Adherence to Standards in Experiment-Related Metadata Entered Via Spreadsheets
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1