Reliance on Science in Patenting

This dataset contains both front-page and in-text citations from patents to scientific articles. <em>If you use the data, please cite</em><strong> M. Marx, &amp; A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), <em>Strategic Management Journal 41(9):1572-1594</em>. (</strong>https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145<strong>). </strong>In-text matches are described in _<strong>fulltext_patent_to_paper_citations.pdf</strong>. The datafile containing the linkages is <strong>_pcs_mag_doi_pmid.tsv. </strong>DOIs and PMIDs provided where available. Each linkage has the applicant/examiner flag, confidence score (1-10), and whether the reference was a) only on the front page, b) only in the body text, or c) in both. <strong>_data_description.pdf</strong> has full details. <strong>bodytextknowngood.tsv</strong> contains the known-good references for calculating recall. The remaining files redistribute the Microsoft Academic Graph. Please cite Sinha, A, et al. 2015. Overview of Microsoft Academic Service (MAS) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW ’15 Companion). ACM, New York, NY, USA, 243-246. These data are under an Open Data Commons Attribution license (ODC-By); use them for anything as long as you cite us! Source code for front-page matches: https://github.com/mattmarx/reliance_on_science. Questions &amp; feedback to [e-mail]<em>.</em>本数据集涵盖专利指向学术论文的首页引用与正文引用。**若使用该数据集，请引用**M. Marx与A. Fuegi于2020年发表在《战略管理期刊（Strategic Management Journal）》第41卷第9期第1572-1594页的论文《Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles》（链接：https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145）。 正文引用的匹配详情见文档_fulltext_patent_to_paper_citations.pdf。存储关联关系的数据文件为_pcs_mag_doi_pmid.tsv，该文件会尽可能提供每条关联对应的数字对象标识符（DOI, Digital Object Identifier）与PubMed文献标识码（PMID, PubMed ID）。每条关联数据均包含申请人/审查员标记、置信度评分（取值范围1-10），以及引用类型：a) 仅出现在专利首页；b) 仅出现在专利正文；c) 同时出现在首页与正文。 _data_description.pdf包含该数据集的完整细节说明。_bodytextknowngood.tsv存储了用于计算召回率的标准参考引用集。其余文件均基于微软学术图谱（Microsoft Academic Graph, MAG）二次分发，因此需同时引用以下文献：A. Sinha等（2015）发表的《微软学术服务及其应用（Overview of Microsoft Academic Service (MAS) and Applications）》，该论文收录于第24届国际万维网大会（WWW ’15 Companion）论文集，由美国计算机协会（ACM）出版，美国纽约，2015年，第243-246页。 本数据集采用开放数据共同体署名许可协议（Open Data Commons Attribution License, ODC-By）进行授权，您可自由使用该数据集，只需按照要求引用即可。首页匹配的源代码可通过以下链接获取：https://github.com/mattmarx/reliance_on_science。如有疑问或反馈，请发送邮件至[e-mail]。