Reliance on Science

This dataset contains <strong>patent-to-paper citations</strong> through 2022 as well as <strong>patent-paper</strong> <strong>pairs </strong>(through 2021). <em>If you use the data, please cite </em>these two articles: <strong>1. 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) <strong>2. 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> The datafile containing the citations is <strong>_pcs_oa.csv. </strong> Each citation has the applicant/examiner flag, confidence score (1-10), whether the reference was a) only on the front page, b) only in the body text, or c) in both, and an indicator for a self-citation (i.e., one of the authors is an inventor on the patent). There are two "shorthand" files, <strong>_pcs_countsbypatent.csv</strong> and <strong>_pcs_countsbypaper.csv</strong>, which collapse these to the paper and patent level by citation type. The datafile containing the patent-paper pairs (PPPs) is <strong>_patent_paper_pairs.tsv</strong>. These are USPTO only, through 2021. Each PPP has a confidence score and the count of days between the publication of the paper and the filing of the patent. (If the patent is a continuation of another patent, the filing date of the original patent is used.) Also, when a paper is paired with multiple patents, an indicator variable reports whether those patents are continuations or otherwise identical. The remaining files redistribute some of the end-2022 edition of OpenAlex. To retrieve additional OpenAlex files or fields, please visit openalex.com. (This release replaces files from the Microsoft Academic Graph, which was retired on 12/20/2021.) The above is documented in greater detail in <strong>__reliance_on_science.pdf.</strong> These data are provided under a Creative Commons Attribution Non-Commercial license. Please contact us regarding commercial use. 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>本数据集包含截至2022年的**专利-论文引用（patent-to-paper citations）**，以及截至2021年的**专利-论文配对（patent-paper pairs）**。若使用本数据集，请引用以下两篇文献： 1. M. Marx与A. Fuegi，《发明者对科学的依赖：文本内专利到文章引用的混合提取方法》，即将发表于*Journal of Economics and Management Strategy*（http://doi.org/10.1111/jems.12455）。 2. M. Marx与A. Fuegi，《科学依赖：科学文章的全球首页专利引用》（2020），载于*Strategic Management Journal* 41(9):1572-1594（https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145）。 包含引用数据的文件为`_pcs_oa.csv`。每条引用数据包含申请人/审查员标记、置信度评分（1-10分）、参考文献的位置类型：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，以及自引用标识（即作者之一为该专利的发明人）。另有两份“简写”聚合文件：`_pcs_countsbypatent.csv`与`_pcs_countsbypaper.csv`，二者可按引用类型将数据分别聚合至专利层面与论文层面。 包含专利-论文配对（patent-paper pairs，以下简称PPPs）数据的文件为`_patent_paper_pairs.tsv`，该数据集仅包含美国专利商标局（United States Patent and Trademark Office, USPTO）的数据，截至2021年。每条专利-论文配对数据包含置信度评分，以及论文发表与专利申请之间的天数差；若该专利为另一专利的延续专利，则使用原专利的申请日期。此外，当一篇论文对应多项专利时，将通过指示变量说明这些专利是否为延续专利或完全相同的专利。 剩余文件为2022年末版OpenAlex的部分重分发数据。若需获取更多OpenAlex文件或字段，请访问openalex.com。（本版本替代了2021年12月20日停用的微软学术图谱（Microsoft Academic Graph）相关文件。）上述内容的详细说明可参见`__reliance_on_science.pdf`。 本数据集采用知识共享署名-非商业性使用许可协议进行发布。商业使用事宜请联系我们。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（Alfred P. Sloan Foundation）资助，资助编号为G-2021-16822。