Reliance on Science in Patenting
Description
This dataset contains citations from worldwide patents to scientific articles. If you use the data, please cite this paper: Marx, Matt and Aaron Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" Forthcoming in Strategic Management Journal. (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686).
We link to the Microsoft Academic Graph (_pcs_mag_bodytextbeta.tsv), and PubMed (_pcs_pubmed_bodytextbeta.tsv). Each linkage has the patent #, MAG ID or PMID, 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. The file _data_description.pdf has full details.
The remaining files redistribute the Microsoft Academic Graph, carved up into smaller files for convenience. If you use them, please cite the following article: 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.
- PubMed linkages are publicly available. MAG linkages are under an Open Data Commons Attribution license (ODC-By), so you can use them for anything as long as you cite us.
- Questions & feedback to support@relianceonscience.org.
- Source code is available at https://github.com/mattmarx/reliance_on_science.
All computation was performed on the Boston University Shared Computing Cluster.
Files
__datadescription.pdf
Files
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Additional details
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