Dataset Open Access

Reliance on Science

Marx, Matt; Aaron Fuegi

DataCite XML Export

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<resource xmlns:xsi="" xmlns="" xsi:schemaLocation="">
  <identifier identifierType="DOI">10.5281/zenodo.7497435</identifier>
      <creatorName>Marx, Matt</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-6173-4142</nameIdentifier>
      <affiliation>Cornell University</affiliation>
      <creatorName>Aaron Fuegi</creatorName>
      <affiliation>Boston University</affiliation>
    <title>Reliance on Science</title>
    <subject>innovation, patenting, science, citation</subject>
    <date dateType="Issued">2022-07-19</date>
  <resourceType resourceTypeGeneral="Dataset"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3236339</relatedIdentifier>
    <rights rightsURI="">Open Data Commons Attribution License v1.0</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;This dataset contains both front-page and in-text citations from patents to scientific articles through 2020. &amp;nbsp;&lt;em&gt;If you use the data, please cite &lt;/em&gt;these two articles:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;1. M. Marx &amp;amp; A. Fuegi, &amp;quot;Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations.&amp;quot; &lt;/strong&gt;&amp;nbsp;&lt;em&gt;forthcoming in Journal of Economics and Management Strategy.&amp;nbsp;&lt;/em&gt;(&lt;a href=""&gt;;/a&gt;)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;2. M. Marx, &amp;amp; A.&amp;nbsp;Fuegi, &amp;quot;Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles&amp;quot; (2020),&amp;nbsp;&lt;em&gt;Strategic Management Journal 41(9):1572-1594&lt;/em&gt;. (&lt;/strong&gt;&lt;a href=""&gt;;/a&gt;&lt;strong&gt;)&amp;nbsp;&lt;/strong&gt;&lt;/p&gt;


&lt;p&gt;The datafile containing the citations is &lt;strong&gt;_pcs_mag_doi_pmid.tsv.&amp;nbsp;&lt;/strong&gt;DOIs and PMIDs provided where available. Each citation has the&amp;nbsp;applicant/examiner flag, confidence score&amp;nbsp;(1-10), and&amp;nbsp;whether the reference was a) only on the front page, b) only in the body text, or c) in both. Each paper-patent citation also includes a preview release (think: alpha, not beta) of the temporal gap (in months) and three related measures of self-citation (i.e., was one or more of the inventors on the citing patent also an author on the cited paper).&amp;nbsp;&lt;strong&gt;_data_description.pdf&lt;/strong&gt;&amp;nbsp;has full details.&amp;nbsp;&lt;strong&gt;bodytextknowngood.tsv&lt;/strong&gt;&amp;nbsp;contains the known-good references for calculating recall.&lt;/p&gt;

&lt;p&gt;The remaining files redistribute much of the *final* edition of the&amp;nbsp;&lt;a href=""&gt;Microsoft Academic Graph&lt;/a&gt;&amp;nbsp;(12/20/2021). Please also cite&amp;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 &amp;rsquo;15 Companion). ACM, New York, NY, USA, 243-246. Note that,, and the OECD/wos-category crosswalks are derivatives of MAG and may not be updated through the end of 2021.&lt;/p&gt;

&lt;p&gt;These data are under an&amp;nbsp;Open Data Commons Attribution license (ODC-By);&amp;nbsp;use them for anything&amp;nbsp;as long as you cite us! Source code for front-page matches is at&amp;nbsp;;nbsp;and for in-text is at Questions &amp;amp; feedback to &lt;a href=""&gt;;/a&gt;&lt;em&gt;.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;This work is sponsored by the Alfred P. Sloan Foundation grant #G-2021-16822.&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;</description>
    <description descriptionType="Other">{"references": ["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"]}</description>
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