Published June 3, 2024 | Version v64
Dataset Open

Reliance on Science

Creators

  • 1. Cornell University

Description

This dataset contains patent-to-paper citations through 2023 as well as patent-paper pairs.

 If you use the citations 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. (http://doi.org/10.1111/jems.12455)

2. 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

 If you use the patent-paper-pairs data, please cite this article:

1. M. Marx & E. Scharfmann, "Does Patenting Promote the Progress of Science? Evidence from Patent-Paper Pairs."  

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.

The datafile containing the patent-paper pairs (PPPs) is _patent_paper_pairs.csv. These are USPTO only, through 2022. 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 above is documented in greater detail in __relianceonscience2024.pdf.

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

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

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__relianceonscience2024.pdf

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Additional details

Dates

Available
2024-06-03

References

  • Marx, Matt and Aaron Fuegi, "Reliance on Science in Patenting: USPTO Front-Page Citations to Scientific Articles" (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686)
  • 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