cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4062694
title: Reliance on Science in Patenting

description: This dataset contains both front-page and in-text citations from patents to scientific articles. <em>If you use the data, please cite</em><strong> M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), <em>Strategic Management Journal 41(9):1572-1594</em>. (</strong>https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145<strong>). </strong>In-text matches are described in _<strong>fulltext_patent_to_paper_citations.pdf</strong>. The datafile containing the linkages is <strong>_pcs_mag_doi_pmid.tsv. </strong>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. <strong>_data_description.pdf</strong> has full details. <strong>bodytextknowngood.tsv</strong> 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: https://github.com/mattmarx/reliance_on_science. Questions & feedback to support@relianceonscience.org<em>.</em>
description: {"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"]}
DOI: 10.5281/zenodo.5080077
title: Reliance on Science in Patenting

description: This dataset contains both front-page and in-text citations from patents to scientific articles through 2020. <em>If you use the data, please cite </em>these two articles: <strong>1. M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), <em>Strategic Management Journal 41(9):1572-1594</em>. (</strong>https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145<strong>) </strong> <strong>2. M. Marx & A. Fuegi, "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations." NBER Working Paper 27987</strong>.(https://www.nber.org/papers/w27987) The datafile containing the citations is <strong>_pcs_mag_doi_pmid.tsv. </strong>DOIs and PMIDs provided where available. Each citation 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. <strong>_data_description.pdf</strong> has full details. <strong>bodytextknowngood.tsv</strong> contains the known-good references for calculating recall. <strong>bodytextpatrefstopatents.tsv</strong> contains references to patents in the body text of patents. The remaining files redistribute the Microsoft Academic Graph. Please also 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 is at https://github.com/mattmarx/reliance_on_science and for in-text is at https://github.com/mattmarx/intextcitations. Questions & feedback to support@relianceonscience.org<em>.</em>
description: {"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"]}
DOI: 10.5281/zenodo.6629738
title: Reliance on Science in Patenting

description: This dataset contains both front-page and in-text citations from patents to scientific articles through 2020. <em>If you use the data, please cite </em>these two articles: <strong>1. M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), <em>Strategic Management Journal 41(9):1572-1594</em>. (</strong>https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145<strong>) </strong> <strong>2. M. Marx & A. Fuegi, "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations." </strong> <em>forthcoming in Journal of Economics and Management Strategy. </em>(http://doi.org/10.1111/jems.12455) The datafile containing the citations is <strong>_pcs_mag_doi_pmid.tsv. </strong>DOIs and PMIDs provided where available. Each citation 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. <strong>_data_description.pdf</strong> has full details. <strong>bodytextknowngood.tsv</strong> contains the known-good references for calculating recall. The remaining files redistribute much of the *final* edition of the Microsoft Academic Graph (12/20/2021). Please also 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. 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. 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 is at https://github.com/mattmarx/reliance_on_science and for in-text is at https://github.com/mattmarx/intextcitations. Questions & feedback to support@relianceonscience.org<em>.</em> <strong><em>This work is sponsored by the Alfred P. Sloan Foundation grant #G-2021-16822.</em></strong>
description: {"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"]}
DOI: 10.5281/zenodo.3238722
title: Patent Citations to Science

description: This dataset contains citations from USPTO patents granted 1947-2018 to articles captured by the Microsoft Academic Graph (ID) from 1800-2018.  

The main file, <strong>pcs.tsv</strong>, contains the resolved citations. Fields are tab-separated. Each match has the patent number, MAG ID, the original citation from the patent, an indicator for whether the citation was supplied by the applicant, examiner, or unknown, and a confidence score (1-10) indicating how likely this match is correct. Note that this distribution does not contain matches with confidence 2 or 1.

The remaining files are a redistribution of the 1 January 2019 release of the Microsoft Academic Graph. All of these files are compressed using ZIP compression under CentOS5. Original files, documented at https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema, can be downloaded from https://aka.ms/msracad; this redistribution carves up the original files into smaller, variable-specific files that can be loaded individually (see <strong>reliance_on_science.pdf</strong> for full details, the latest version of which is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686).

Source code is available at https://github.com/mattmarx/reliance_on_science. Be sure to see the replication disclaimers.
DOI: 10.5281/zenodo.4028934
title: Reliance on Science in Patenting

description: This dataset contains both front-page and in-text citations from patents to scientific articles. <em>If you use the data, please cite</em><strong> M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), <em>Strategic Management Journal 41(9):1572-1594</em>. (</strong>https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145<strong>). </strong>In-text matches are described in _<strong>fulltext_patent_to_paper_citations.pdf</strong>. The datafile containing the linkages is <strong>_pcs_mag_doi_pmid.tsv. </strong>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. <strong>_data_description.pdf</strong> has full details. <strong>bodytextknowngood.tsv</strong> 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: https://github.com/mattmarx/reliance_on_science. Questions & feedback to support@relianceonscience.org<em>.</em>
description: {"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"]}
DOI: 10.5281/zenodo.4233119
title: Reliance on Science in Patenting

description: This dataset contains both front-page and in-text citations from patents to scientific articles. <em>If you use the data, please cite </em>these two articles: <strong>1. M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), <em>Strategic Management Journal 41(9):1572-1594</em>. (</strong>https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145<strong>) </strong> <strong>2. M. Marx & A. Fuegi, "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations." NBER Working Paper 27987</strong>.(https://www.nber.org/papers/w27987) The datafile containing the citations is <strong>_pcs_mag_doi_pmid.tsv. </strong>DOIs and PMIDs provided where available. Each citation 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. <strong>_data_description.pdf</strong> has full details. <strong>bodytextknowngood.tsv</strong> contains the known-good references for calculating recall. <strong>bodytextpatrefstopatents.tsv</strong> contains references to patents in the body text of patents. The remaining files redistribute the Microsoft Academic Graph. Please also 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: https://github.com/mattmarx/reliance_on_science. Questions & feedback to support@relianceonscience.org<em>.</em>
description: {"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"]}
DOI: 10.5281/zenodo.3680340
title: Reliance on Science in Patenting

description: This dataset contains citations from worldwide patents to scientific articles. <em>If you use the data, please cite this paper</em><strong>: Marx, Matt and Aaron Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" Forthcoming in <em>Strategic Management Journal</em>. (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686). </strong> We link to the Microsoft Academic Graph (<strong>_pcs_mag_bodytextbeta.tsv</strong>), and PubMed (<strong>_pcs_pubmed_bodytextbeta.tsv</strong>). 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 <strong>_data_description.pdf</strong> 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<em>.</em> Source code is available at https://github.com/mattmarx/reliance_on_science. <em>All computation was performed on the Boston University Shared Computing Cluster.</em>
description: {"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"]}
DOI: 10.5281/zenodo.4023202
title: Reliance on Science in Patenting

description: This dataset contains both front-page and in-text citations from patents to scientific articles. <em>If you use the data, please cite</em><strong> M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), <em>Strategic Management Journal 41(9):1572-1594</em>. (</strong>https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145<strong>). </strong>In-text matches are described in _<strong>fulltext_patent_to_paper_citations.pdf</strong>. The datafile containing the linkages is <strong>_pcs_mag_doi_pmid.tsv. </strong>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. <strong>_data_description.pdf</strong> has full details. <strong>bodytextknowngood.tsv</strong> 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: https://github.com/mattmarx/reliance_on_science. Questions & feedback to support@relianceonscience.org<em>.</em>
description: {"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"]}
DOI: 10.5281/zenodo.4843762
title: Reliance on Science in Patenting

description: This dataset contains both front-page and in-text citations from patents to scientific articles. <em>If you use the data, please cite </em>these two articles: <strong>1. M. Marx, & A. Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles" (2020), <em>Strategic Management Journal 41(9):1572-1594</em>. (</strong>https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3145<strong>) </strong> <strong>2. M. Marx & A. Fuegi, "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations." NBER Working Paper 27987</strong>.(https://www.nber.org/papers/w27987) The datafile containing the citations is <strong>_pcs_mag_doi_pmid.tsv. </strong>DOIs and PMIDs provided where available. Each citation 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. <strong>_data_description.pdf</strong> has full details. <strong>bodytextknowngood.tsv</strong> contains the known-good references for calculating recall. <strong>bodytextpatrefstopatents.tsv</strong> contains references to patents in the body text of patents. The remaining files redistribute the Microsoft Academic Graph. Please also 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: https://github.com/mattmarx/reliance_on_science. Questions & feedback to support@relianceonscience.org<em>.</em> (note ,this is an interim release and does not include the MAG files; those will be posted shortly)
description: {"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"]}
DOI: 10.5281/zenodo.11461587
title: Reliance on Science

description: This dataset contains patent-to-paper citations through 2023 as well as patent-paper pairs.

 If you use the citations 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."  forthcoming in Journal of Economics and Management Strategy. (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) 

 If you use the patent-paper-pairs data, please cite this article:

1. M. Marx & E. Scharfmann, "Does Patenting Promote the Progress of Science? Evidence from Patent-Paper Pairs."  

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.

The datafile containing the patent-paper pairs (PPPs) is _patent_paper_pairs.csv. These are USPTO only, through 2022. 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 above is documented in greater detail in __relianceonscience2024.pdf.

These data are provided under a Creative Commons Attribution Non-Commercial license. Please contact us regarding commercial use.  Questions & feedback to support@relianceonscience.org.

This work is sponsored by the Alfred P. Sloan Foundation grant #G-2021-16822.
DOI: 10.5281/zenodo.3236340
title: Patent Citations to Science

description: This dataset contains citations from USPTO patents granted 1947-2018 to articles captured by the Microsoft Academic Graph (ID) from 1800-2018. All files in this distribution are compressed using ZIP compression, except the <strong>reliance_on_science.pdf</strong> documentation which is provided uncompressed. 

The main file, <strong>pcs.zip</strong>, contains the resolved citations. Each has the patent number, MAG ID, the original citation from the patent, an indicator for whether the citation was supplied by the applicant, examiner, or unknown, and a confidence score (1-10) indicating how likely this match is correct. Note that this distribution does not contain matches with confidence 2 or 1.

The remaining files are a redistribution of the 1 January 2019 release of the Microsoft Academic Graph. Original files can be downloaded from https://aka.ms/msracad; this redistribution carves up the original files into smaller, variable-specific files that can be loaded individually (see <strong>reliance_on_science.pdf</strong> for full details, the latest version of which is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686).
DOI: 10.5281/zenodo.3338601
title: Patent Citations to Science

description: This dataset contains citations from USPTO patents granted 1947-2018 to articles captured by the Microsoft Academic Graph (ID) from 1800-2018. The main file, <strong>pcs.tsv</strong>, contains the resolved citations. Fields are tab-separated. Each match has the patent number, MAG ID, the original citation from the patent, an indicator for whether the citation was supplied by the applicant, examiner, or unknown, and a confidence score (1-10) indicating how likely this match is correct. Note that this distribution does not contain matches with confidence 2 or 1. There is also a PubMed-specific match in <strong>pcs-pubmed.tsv</strong>. The remaining files are a redistribution of the 1 January 2019 release of the Microsoft Academic Graph. All of these files are compressed using ZIP compression under CentOS5. Original files, documented at https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema, can be downloaded from https://aka.ms/msracad; this redistribution carves up the original files into smaller, variable-specific files that can be loaded individually (see _<strong>reliance_on_science.pdf</strong> for full details, the latest version of which is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686). Source code for generating the patent citations to science in pcs.tsv is available at https://github.com/mattmarx/reliance_on_science. Source code for generating jif.zip and jcif.zip (Journal Impact Factor and Journal Commercial Impact Factor) is at https://github.com/mattmarx/jcif.