There is a newer version of this record available.

Dataset Open Access

Reliance on Science in Patenting

Marx, Matt; Aaron Fuegi

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Marx, Matt</dc:creator>
  <dc:creator>Aaron Fuegi</dc:creator>
  <dc:description>This dataset contains both front-page and in-text citations from patents to scientific articles.  If you use the data, please cite M. Marx, &amp; A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), Strategic Management Journal 41(9):1572-1594. (  In-text matches are described in _fulltext_patent_to_paper_citations.pdf. 

The datafile containing the linkages is _pcs_mag_doi_pmid.tsv. DOIs and PMIDs provided where available. Each linkage has the applicant/examiner flag, confidence score (1-10), and whether the reference was a) only on the front page, b) only in the body text, or c) in both. _data_description.pdf has full details. bodytextknowngood.tsv contains the known-good references for calculating recall.

The remaining files redistribute the Microsoft Academic Graph. Please cite 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 ’15 Companion). ACM, New York, NY, USA, 243-246.

These data are under an Open Data Commons Attribution license (ODC-By); use them for anything as long as you cite us! Source code for front-page matches: Questions &amp; feedback to 

  <dc:subject>innovation, patenting, science, citation</dc:subject>
  <dc:title>Reliance on Science in Patenting</dc:title>
All versions This version
Views 24,707328
Downloads 39,124519
Data volume 115.3 TB1.3 TB
Unique views 19,948313
Unique downloads 14,206174


Cite as