Reliance on Science in Patenting

This dataset contains citations from worldwide patents to scientific articles.  If you use the data, please cite this paper: Marx, Matt and Aaron Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" Forthcoming in Strategic Management Journal. (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686). 

We link to the Microsoft Academic Graph (_pcs_mag_bodytextbeta.tsv), and PubMed (_pcs_pubmed_bodytextbeta.tsv). Each linkage has the patent #, MAG ID or PMID, 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. The file _data_description.pdf has full details.

The remaining files redistribute the Microsoft Academic Graph, carved up into smaller files for convenience. 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.

• PubMed linkages are publicly available. MAG linkages are under an Open Data Commons Attribution license (ODC-By), so you can use them for anything as long as you cite us.
• Questions & feedback to support@relianceonscience.org. 
• Source code is available at https://github.com/mattmarx/reliance_on_science.

All computation was performed on the Boston University Shared Computing Cluster.