Dataset Open Access
Reliance on Science in Patenting
Marx, Matt;
Aaron Fuegi
DCAT Export
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<dct:title>Reliance on Science in Patenting</dct:title>
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<dct:description><p>This dataset contains both front-page and in-text citations from patents to scientific articles through 2020. &nbsp;<em>If you use the data, please cite </em>these two articles:</p> <p><strong>1. M. Marx, &amp; A.&nbsp;Fuegi, &quot;Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles&quot; (2020),&nbsp;<em>Strategic Management Journal 41(9):1572-1594</em>. (</strong><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145">https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145</a><strong>)&nbsp;</strong></p> <p><strong>2. M. Marx &amp; A. Fuegi, &quot;Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations.&quot; </strong>&nbsp;<em>forthcoming in Journal of Economics and Management Strategy.&nbsp;</em>(<a href="http://doi.org/10.1111/jems.12455">http://doi.org/10.1111/jems.12455</a>)</p> <p>The datafile containing the citations is <strong>_pcs_mag_doi_pmid.tsv.&nbsp;</strong>DOIs and PMIDs provided where available. Each citation has the&nbsp;applicant/examiner flag, confidence score&nbsp;(1-10), and&nbsp;whether the reference was a) only on the front page, b) only in the body text, or c) in both.&nbsp;<strong>_data_description.pdf</strong>&nbsp;has full details.&nbsp;<strong>bodytextknowngood.tsv</strong>&nbsp;contains the known-good references for calculating recall.</p> <p>The remaining files redistribute much of the *final* edition of the&nbsp;<a href="http://aka.ms/msracad">Microsoft Academic Graph</a>&nbsp;(12/20/2021). Please also cite&nbsp;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 &rsquo;15 Companion). ACM, New York, NY, USA, 243-246. Note that jif.zip, jcif.zip, and the OECD/wos-category crosswalks are derivatives of MAG and may not be updated through the end of 2021.</p> <p>These data are under an&nbsp;Open Data Commons Attribution license (ODC-By);&nbsp;use them for anything&nbsp;as long as you cite us! Source code for front-page matches is at&nbsp;https://github.com/mattmarx/reliance_on_science&nbsp;and for in-text is at https://github.com/mattmarx/intextcitations. Questions &amp; feedback to <a href="mailto:support@relianceonscience.org">support@relianceonscience.org</a><em>.</em></p> <p><strong><em>This work is sponsored by the Alfred P. Sloan Foundation grant #G-2021-16822.</em></strong></p></dct:description>
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52,501
62,812
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 52,501 | 2,332 |
| Downloads | 62,812 | 1,436 |
| Data volume | 154.6 TB | 2.4 TB |
| Unique views | 42,919 | 2,074 |
| Unique downloads | 25,504 | 955 |
Indexed in
- Publication date:
- April 18, 2022
- DOI:
-
- Keyword(s):
- innovation, patenting, science, citation
- License (for files):
- Open Data Commons Attribution License v1.0