Reliance on Science in Patenting

This dataset contains both front-page and in-text citations from patents to scientific articles through 2020.  If you use the data, please cite these two articles:

1. M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), Strategic Management Journal 41(9):1572-1594. (https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145) 

2. M. Marx & A. Fuegi, "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations." NBER Working Paper 27987.(https://www.nber.org/papers/w27987)

The datafile containing the citations is _pcs_mag_doi_pmid.tsv. 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. _data_description.pdf has full details. bodytextknowngood.tsv contains the known-good references for calculating recall. bodytextpatrefstopatents.tsv contains references to patents in the body text of patents.

The remaining files redistribute the Microsoft Academic Graph. 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.

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 & feedback to support@relianceonscience.org.