Patent Citations to Science

This dataset contains citations from worldwide patents to scientific articles. <b>If you use the data, please cite this paper</b><b>: Marx, Matt and Aaron Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (</b><b>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686</b><b>). </b><br><br>There are two "flavors" of matches: linking to the Microsoft Academic Graph (_pcs_mag.tsv), and to PubMed (_pcs_pubmed.tsv). Each citation to science has the patent number, paper ID for MAG or PubMed, applicant/examiner indicator, and a confidence score (1-10); _data_description.pdf has full details.We also have a beta release of matches from the body text of USPTO patents since patent #1 in 1836. The files _pcs_mag_bodytextbeta.tsv and _pcs_pubmed_bodytextbeta.tsv add a field indicating whether the citation appeared on the front page, in the body text, or in both.<br><br>The remaining files redistribute the Microsoft Academic Graph, carving up the original files into smaller, variable-specific files. There are also extensions including journal impact factor and high-level technical classifications. If you use them, please cite the following article: 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.The PubMed linkages are publicly available without any licensing restrictions. The MAG linkages are subject to the Open Data Commons Attribution license (ODC-By), so you can use them for anything as long as you cite us.Questions &amp; feedback to [e-mail]. Join our listserv by sending a plain text email to [e-mail] with "subscribe relianceonscience-l" in the body. Source code is available at https://github.com/mattmarx/reliance_on_science.This computational work was performed on the Boston University Shared Computing Cluster.<br><br><br><br>