Reliance on Science

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."  Journal of Economics and Management Strategy 31(2);369-392 (2020). (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) 

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 openalex.com.  (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. 

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