Reliance on Science in Patenting

This dataset contains both front-page and in-text citations from patents to scientific articles through 2020. <em>If you use the data, please cite </em>these two articles: <strong>1. 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> <strong>2. M. Marx &amp; A. Fuegi, "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations." </strong> <em>forthcoming in Journal of Economics and Management Strategy. </em>(http://doi.org/10.1111/jems.12455) The datafile containing the citations is <strong>_pcs_mag_doi_pmid.tsv. </strong>DOIs and PMIDs provided where available. Each citation 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 much of the *final* edition of the Microsoft Academic Graph (12/20/2021). Please also 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. Note that jif.zip, jcif.zip, and the OECD/wos-category crosswalks are derivatives of MAG and may not be updated through the end of 2021. 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 is at https://github.com/mattmarx/reliance_on_science and for in-text is at https://github.com/mattmarx/intextcitations. Questions &amp; feedback to [e-mail]<em>.</em> <strong><em>This work is sponsored by the Alfred P. Sloan Foundation grant #G-2021-16822.</em></strong>本数据集收录了截至2020年的专利对科学论文的首页引用与正文引用。若使用本数据集，请引用以下两篇文献：1. M. Marx与A. Fuegi，"对科学的依赖：全球范围内专利首页对科学论文的引用"（2020），刊载于《战略管理杂志》（Strategic Management Journal）41卷第9期，页码1572-1594。（链接：https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145）2. M. Marx与A. Fuegi，"发明者对科学的依赖：专利与论文正文引用的混合提取方法"，即将刊载于《经济学与管理战略杂志》（Journal of Economics and Management Strategy）。（链接：http://doi.org/10.1111/jems.12455）包含引用信息的数据文件为`_pcs_mag_doi_pmid.tsv`，若可获取则会提供文献对象标识符（DOI, Digital Object Identifier）与PubMed标识符（PMID, PubMed Identifier）。每条引用均包含申请人/审查员标记、置信度评分（1-10分），以及该引用的位置：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`包含完整的详细说明。`bodytextknowngood.tsv`包含用于计算召回率的已知正确引用集。其余文件大多基于微软学术图谱（Microsoft Academic Graph, MAG）2021年12月20日的最终版本进行二次分发。此外，请同时引用Sinha等人2015年的文献："微软学术服务（MAS）概述及其应用"，收录于第24届国际万维网大会（WWW ’15 Companion）会议论文集，美国计算机协会（ACM），纽约，2015年，页码243-246。请注意：jif.zip、jcif.zip以及OECD/wos分类交叉对照表均为微软学术图谱的衍生数据，可能未更新至2021年底。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，您可自由使用本数据，仅需遵守引用要求即可。首页匹配的源代码可在https://github.com/mattmarx/reliance_on_science 获取，正文匹配的源代码可在https://github.com/mattmarx/intextcitations 获取。如有疑问或反馈，请发送邮件至[e-mail]。本研究由阿尔弗雷德·P·斯隆基金会资助（项目编号G-2021-16822）。