Patent Citations to Science

This dataset contains citations from USPTO patents granted 1947-2018 to articles captured by the Microsoft Academic Graph (ID) from 1800-2018. The main file, <strong>pcs.tsv</strong>, contains the resolved citations. Fields are tab-separated. Each match has the patent number, MAG ID, the original citation from the patent, an indicator for whether the citation was supplied by the applicant, examiner, or unknown, and a confidence score (1-10) indicating how likely this match is correct. Note that this distribution does not contain matches with confidence 2 or 1. There is also a PubMed-specific match in <strong>pcs-pubmed.tsv</strong>. The remaining files are a redistribution of the 1 January 2019 release of the Microsoft Academic Graph. All of these files are compressed using ZIP compression under CentOS5. Original files, documented at https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema, can be downloaded from https://aka.ms/msracad; this redistribution carves up the original files into smaller, variable-specific files that can be loaded individually (see <strong>_README.pdf</strong> for full details.. Source code for generating the patent citations to science in pcs.tsv is available at https://github.com/mattmarx/reliance_on_science. Source code for generating jif.zip and jcif.zip (Journal Impact Factor and Journal Commercial Impact Factor) is at https://github.com/mattmarx/jcif. Although MAG contains authors and affiliations for each paper, it does not contain the location for affiliations. We have created a dataset of locations for affiliations appearing at least 100x using Bing Maps and Google Maps; however, it is unclear to us whether the API licensing terms allow us to repost their data. In any case, you can download our source code for doing so here: https://github.com/ksjiaxian/api-requester-locations. MAG extracts field keywords for each paper (paperfieldid.zip and fieldidname.zip) --more than 200,000 fields in all! When looking to study industries or technical areas you might find this a bit overwhelming. We mapped the MAG subjects to six OECD fields and 39 subfields, defined here: http://www.oecd.org/science/inno/38235147.pdf. Clarivate provides a crosswalk between the OECD classifications and Web of Science fields, so we include WoS fields as well. This file is <strong>magfield_oecd_wos_crosswalk.zip</strong>.

本数据集收录了1947-2018年美国专利商标局（United States Patent and Trademark Office, USPTO）授权专利的参考文献，这些参考文献对应1800-2018年间被微软学术图谱（Microsoft Academic Graph, MAG）收录的文献。主文件为pcs.tsv，包含已解析的参考文献匹配记录，字段以制表符分隔。每条匹配记录包含专利号、MAG ID、该专利中的原始参考文献条目、用于标识该参考文献由申请人、审查员提供或来源不明的指示器字段，以及表示该匹配正确可能性的置信度评分（1-10分）。请注意，本数据集不包含置信度为1或2的匹配记录。此外还有针对PubMed的专属匹配文件pcs-pubmed.tsv。其余文件均为2019年1月1日发布版微软学术图谱的再分发版本。所有文件均采用ZIP压缩格式，适配CentOS5操作系统。
原始文件的文档说明位于https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema，可从https://aka.ms/msracad下载；本次再分发将原始文件拆分为多个按变量分类的小型独立加载文件，完整细节请参阅_README.pdf。
用于生成pcs.tsv中专利与科技文献参考文献对应关系的源代码，可从https://github.com/mattmarx/reliance_on_science获取。用于生成jif.zip与jcif.zip（即期刊影响因子与期刊商业影响因子）的源代码，托管于https://github.com/mattmarx/jcif。
尽管微软学术图谱收录了每篇文献的作者与所属机构信息，但未提供所属机构的地理位置数据。我们基于必应地图（Bing Maps）与谷歌地图（Google Maps），构建了出现频次≥100次的所属机构地理位置数据集，但目前尚不明确相关API许可条款是否允许我们重新发布该数据集。无论如何，你可从https://github.com/ksjiaxian/api-requester-locations下载我们用于生成该数据集的源代码。
微软学术图谱会为每篇文献提取领域关键词（对应文件为paperfieldid.zip与fieldidname.zip），总计领域数量超过20万个！若你希望开展产业或技术领域研究，可能会觉得该数据量过于庞杂。我们将MAG的主题分类映射至经合组织（Organisation for Economic Co-operation and Development, OECD）划分的6大领域与39个子领域，具体定义参见：http://www.oecd.org/science/inno/38235147.pdf。科睿唯安（Clarivate）提供了OECD分类与Web of Science领域的交叉对照表，因此我们也一并收录了Web of Science领域相关信息，对应文件为magfield_oecd_wos_crosswalk.zip。