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Published July 20, 2023 | Version v40
Dataset Open

Reliance on Science

  • 1. Cornell University
  • 2. Boston University


This dataset contains patent-to-paper citations through 2022 as well as patent-paper pairs (through 2021).  If you use the data, please cite these two articles:

1. M. Marx & A. Fuegi, "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations."  forthcoming in Journal of Economics and Management Strategy. (

2. M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), Strategic Management Journal 41(9):1572-1594. (

The datafile containing the citations is _pcs_oa.csv.  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, _pcs_countsbypatent.csv and _pcs_countsbypaper.csv, which collapse these to the paper and patent level by citation type.

The datafile containing the patent-paper pairs (PPPs) is _patent_paper_pairs.tsv. 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  (This release replaces files from the Microsoft Academic Graph, which was retired on 12/20/2021.) 

The above is documented in greater detail in __reliance_on_science.pdf.

These data are provided under a Creative Commons Attribution Non-Commercial license. Please contact us regarding commercial use.  Questions & feedback to

This work is sponsored by the Alfred P. Sloan Foundation grant #G-2021-16822.



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Additional details


  • Marx, Matt and Aaron Fuegi, "Reliance on Science in Patenting: USPTO Front-Page Citations to Scientific Articles" (
  • Sinha, Arnab, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An 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