I³ Innovation Dataset Catalog


1	Title	URL	I3 member author?	Description	Terms of use	Timeframe	Documentation	Performance/error metrics	Citation	Open-source code	Versioning	API or Bulk downloads	Keywords associated with this dataset	Datasets and publications using this dataset

2	PatCit	https://doi.org/10.5281/zenodo.3710993	Yes	In-text and front page citations to non-patent literature + in-text patent citations, extracted and parsed using NLP techniques. Open source project	CC-BY 4.0 International	1836-2018	https://cverluise.github.io/PatCit/	yes	Cyril Verluise, Gabriele Cristelli, Kyle Higham, Lucas Violon, & Gaétan de Rassenfosse. (2020). PatCit: A Comprehensive Dataset of Patent Citations (Version 0.3.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4391095	https://github.com/cverluise/PatCit	Yes	Bulk	Citations, In-text, Front page, Patent, Science, Database, Wikipedia, Standard	https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3754772
3	Chilean IP and firm data	https://eml.berkeley.edu//~bhhall/Chile_ipdata.html	Yes	These data are a public release from a joint WIPO-INAPI project.	?	1995-2005	https://eml.berkeley.edu//~bhhall/Chile_ipdata/chile_inno_ip.txt		Abud, M.J., Fink, C., Hall, B. and Helmers, C., 2013. The use of intellectual property in Chile (Vol. 11). WIPO.			Bulk
4	Chinese Patent Data Project	https://sites.google.com/site/sipopdb/home/sipo---asie		In this project, patents from China's State Intellectual Property Office (SIPO) are matched to various types of companies. Matching SIPO patents to firms in the Annual Survey of Industrial Enterprises (ASIE) of China's National Bureau of Statistics.								Bulk
5	Reliance on Science in Patenting	https://zenodo.org/record/3575146#.XfQZMWRKiUk	Yes	This contains citations from the front pages of worldwide patents to articles in the Microsoft Academic Graph (MAG) from 1800-2018.	Open Data Commons Attribution License v1.0	1834-2019	https://zenodo.org/record/4235193#.X6Fgb5CSm38	Yes	Marx, Matt and Aaron Fuegi, "Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles"	https://github.com/mattmarx/reliance_on_science	Yes	Bulk	error margins
6	Japanese Patent Office	http://www.iip.or.jp/e/index.html		Patent database of the IIP	Only for use by academic research institutions and other institutions for academic research purposes, cannot be used for commercial purposes.	1964-9/2019			State that you used: III Patent DB			Bulk
7	MIT Scholarly Works 1950-2018	https://lens-public.s3-us-west-2.amazonaws.com/sloan/scholarly/201932/mit_scholarly.zip	Yes	Scholarly works produced by MIT 1950-2018
8	MIT Scholarly Works Cited by Patents	https://lens-public.s3-us-west-2.amazonaws.com/sloan/scholarly/201932/mit_scholarly_cited_by_patents.zip	Yes	MIT Scholarly Works Cited by Patents 1950-2018
9	Patents Citing MIT Publications	https://www.lens.org/lens/search/patent/list?collectionId=22790&p=0&n=10	Yes	This collection encompasses patents that cite the scholarly works of Massachusetts Institute of Technology.
10	Disambiguation and Co-authorship Networks of the U.S. Patent Inventor Database	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5F1RRI		Name disambiguation of US inventors, 1975-2010	CC0 - "Public Domain Dedication"				Ronald Lai; Alexander D'Amour; Amy Yu; Ye Sun; Lee Fleming, 2011, "Disambiguation and Co-authorship Networks of the U.S. Patent Inventor Database (1975 - 2010)", https://doi.org/10.7910/DVN/5F1RRI, Harvard Dataverse, V5, UNF:5:RqsI3LsQEYLHkkg5jG/jRg== [fileUNF]	https://github.com/funginstitute/downloads			coauthor network
11	The careers and co-authorship networks of U.S. patent-holders, since 1975	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YJUNUN		The identification enables construction of social networks based on patent co-authorship. We will eventually provide descriptive statistics of individual and collaborative variables and illustrated examples of networks for an individual, an organization, a technology, and a region. The data and code will be publically available for community use and improvement and will enable updating as frequently as new patents are issued.	CC0 - "Public Domain Dedication"				Ronald Lai; Alexander D'Amour; Lee Fleming, 2010, "The careers and co-authorship networks of U.S. patent-holders, since 1975", https://doi.org/10.7910/DVN/YJUNUN, Harvard Dataverse, V3, UNF:5:daJuoNgCZlcYY8RqU+/j2Q== [fileUNF]				coauthor network
12	Penn World Tables	https://doi.org/10.15141/S50T0R		PWT version 9.1 is a database with information on relative levels of income, output, input and productivity, covering 182 countries between 1950 and 2017.	CC 4.0	1950-2017			Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt
13	Worldwide Count of Priority Patents	http://www.gder.info/download_wwc_excel.html		The goal of the project was to produce a dataset of priority patent applications filed across the globe, allocated by inventor and applicant location.					De Rassenfosse, G., Dernis, H., Guellec, D., Picci, L., & van Pottelsberghe de la Potterie, B. (2013). The worldwide count of priority patents: A new indicator of inventive activity. Research Policy, 42(3), 720–737. doi:10.1016/j.respol.2012.11.002	http://www.gder.info/download_wwc_mysql.html
14	Geocoding of worldwide patent data	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OTTBDX			CC0 - "Public Domain Dedication"				Seliger, Florian; Kozak, Jan; de Rassenfosse, Gaétan, 2019, "Geocoding of worldwide patent data", https://doi.org/10.7910/DVN/OTTBDX, Harvard Dataverse, V5	https://github.com/seligerf/Imputation-of-missing-location-information-for-worldwide-patent-data			geography
15	On the price elasticity of demand for patents	http://www.gder.info/download_OBES_data.html		Fees since 1980 at the European (EPO), the US and the Japanese patent offices.					Rassenfosse, G. de, & Potterie, B. van P. de la.				patent demand
16	Patents arising from U.S. government funding	https://zenodo.org/record/3369582		Dataset of patents arising from government funding since the year 2000.	CC-BY 4.0 International	2000-2019			de Rassenfosse Gaétan, & Emilio Raiteri. (2019). 3PFL: Database of Patents and Publications with a Public-Funding Linkage (Version 1.2) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3369582
17	PATSTAT	https://www.epo.org/searching-for-patents/business/patstat.html#tab3		PATSTAT contains bibliographical and legal event patent data from leading industrialised and developing countries. This is extracted from the EPO’s databases and is either provided as bulk data or can be consulted online.	Requires a subscription to access				PATSTAT	patstat cookbook' by Gaétan de Rassenfosse https://onlinelibrary.wiley.com/doi/full/10.1111/1467-8462.12073
18	Lens.org	https://lens.org/	Yes	Lens serves nearly all of the patent documents in the world as open, annotatable digital public goods that are integrated with scholarly and technical literature along with regulatory and business data. The Lens will allow document collections, aggregations, and analyses to be shared, annotated, and embedded to forge open mapping of the world of knowledge-directed innovation.	Cambia grants you a non-exclusive, non-transferable, revocable, limited license to access and personally use the features of the Service. The conditions by which The Lens data may be used are intended to resonate with the principles of Creative Commons Attribution licenses with a public benefit element.				Please use the expression 'Enabled by The Lens' or 'Data Sourced from The Lens' and the Lens.org URL.
19	Microsoft Academic Graph	https://academic.microsoft.com/home		The Microsoft Academic Graph is a heterogeneous graph containing scientific publication records, citation relationships between those publications, as well as authors, institutions, journals, conferences, and fields of study.	ODC-BY				Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839 K. Wang et al., “A Review of Microsoft Academic Services for Science of Science Studies”, Frontiers in Big Data, 2019, doi: 10.3389/fdata.2019.00045				Microsoft Academic
20	Crios‐Patstat Database	http://download.unibocconi.it/ICRIOS2018/icrios201806.rar		Disambiguated inventor's and applicant's names for EPO records.	EPO License				Coffano, M., & Tarasconi, G. (2014). CRIOS - Patstat Database: Sources, Contents and Access Rules. SSRN Electronic Journal. doi:10.2139/ssrn.2404344
21	NBER US Patent Citation Datafile	https://sites.google.com/site/patentdataproject/Home/downloads		The main dataset extends from Jan 1, 1963, through december 30, 1999, and includes all the utility patents granted during that period. The citations file includes all citations made by patents granted in 1975-1999.	The main dataset extends from Jan 1, 1963, through december 30, 1999, and includes all the utility patents granted during that period. The citations file includes all citations made by patents granted in 1975-1999.	1963-1999			Bronwyn H. Hall, Jim Bessen, Grid Thoma
22	USPTO PatentsView	https://www.patentsview.org/download/		PatentsView includes US patent data including raw data and disambugations of inventors and assignees, also inventor gender.	Creative Commons Attribution 4.0 International License.	1963-1999	Provided at link	N/A	Attribution should be given to PatentsView for use, distribution, or derivative works.	https://github.com/CSSIP-AIR/PatentsView-Code-Snippets/
23	Microsoft Academic Knowledge Graph	http://ma-graph.org/		A large RDF data set with over eight billion triples with information about scientific publications and related entities, such as authors, institutions, journals, and fields of study. The data set is based on the Microsoft Academic Graph and licensed under the Open Data Attributions license. Furthermore, we provide entity embeddings for all 210M represented scientific papers.	Open Data Commons Attribution License (ODC-By) v1.0				@inproceedings{DBLP:conf/semweb/Farber19, author = {Michael F{\"{a}}rber}, title = "{The Microsoft Academic Knowledge Graph: {A} Linked Data Source with 8 Billion Triples of Scholarly Data}", booktitle = "{Proceedings of the 18th International Semantic Web Conference}", series = "{ISWC'19}", location = "{Auckland, New Zealand}", pages = {113--129}, year = {2019}, url = {https://doi.org/10.1007/978-3-030-30796-7\_8}, doi = {10.1007/978-3-030-30796-7\_8} }	https://github.com/michaelfaerber/makg-linking			Microsoft Academic
24	IPRoduct		Yes	The IPRoduct project seeks to link innovative goods to the patents upon which they are based. By directly linking products to patents, this project tracks innovation to the point where it meets consumers, the true commercial end point of investments in Science & Technology. The output of the project is a database of linked product-patent pairs that is made publicly available.									Products
25	Replication Data for: Government-funded research increasingly fuels innovation	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DKESRC		This includes patent level metadata, 1926-1975 (OCRed from USPTO Image PDF files), 1976-2017 (parsed from USPTO HTML files), patent meta data, CPC, geography, agencies, entity size of the patent owner etc, government support categories at patent level and finally, aggregate yearly statistics. (2019-06-02)	CC0 - "Public Domain Dedication"	1926-1975 and 1975-2017			Lee Fleming; Hillary Green; Guan-Cheng Li; Matt Marx; Dennis Yao, 2019, "Replication Data for: Government-funded research increasingly fuels innovation", https://doi.org/10.7910/DVN/DKESRC, Harvard Dataverse, V4, UNF:6:kMIqsh3DCvKiKYgMT6/H8A== [fileUNF]	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DKESRC
26	Google Patents Public Datasets	https://console.cloud.google.com/marketplace/details/google_patents_public_datasets/google-patents-public-data		Worldwide (100+ countries) bibliographic and USPTO full-text, available via BigQuery. Provided by IFI CLAIMS Patent Services, a worldwide bibliographic and US full-text dataset of patent publications. Updated quarterly.	CC BY 4.0, requires subscription to query API	1834-present (quarterly)	https://cloud.google.com/blog/topics/public-datasets/google-patents-public-datasets-connecting-public-paid-and-private-patent-data	N/A	“Google Patents Public Data” by IFI CLAIMS Patent Services and Google, used under CC BY 4.0	patent analysis sample code: https://github.com/google/patents-public-data, source code not accessible	Yes, quarterly	API, Bulk export	Google Patents
27	Google Patents number linking API	https://patents.google.com/api/match		Turn an unformatted application or publication number into the DOCDB format publication number		1834-present (~weekly)						API	Google Patents
28	Semantic Scholar Open Research Corpus	http://s2-public-api-prod.us-west-2.elasticbeanstalk.com/corpus/		Semantic Scholar's records for research papers published in all fields provided as an easy-to-use JSON archive.	ODC-BY				Waleed Ammar et al. 2018. Construction of the Literature Graph in Semantic Scholar. NAACL https://www.semanticscholar.org/paper/09e3cf5704bcb16e6657f6ceed70e93373a54618				Citation affect
29	UVA Darden Global Corporate Patent Dataset (disambiguated assignees)	https://patents.darden.virginia.edu/				1980-2017	https://patents.darden.virginia.edu/documents/DataConstructionDetails_v01.pdf
30	DISCERN patent/compustat crosswalk	https://zenodo.org/record/3709084#.XzbbVn-SlGp				1976-2015	Provided at link
31	Patent Citation Similarity	https://storage.googleapis.com/jmk_public/Kuhn-Younge-Marco_Patent_Citation_Similarity_2017-10-23.csv		from Jeff Kuhn		1976-2017	Paper: https://ssrn.com/abstract=2714954
32	Patent Scope and Examiner Toughness	https://storage.googleapis.com/jmk_public/Kuhn-Thompson_Patent_Scope_2017-10-23.csv		from Jeff Kuhn		Need to check paper https://ssrn.com/abstract=2977273	Not unless it’s in the paper						Examiners
33	Patent Citation Timing and Source	https://storage.googleapis.com/jmk_public/Kuhn-Younge-Marco_Patent_Citation_Source_and_Timing_2017-09-25.csv		from Jeff Kuhn		2001-2004	Not unless it’s in the paper here https://ssrn.com/abstract=2714954
34	Patent Families Dataset	https://storage.googleapis.com/jmk_public/Younge-Kuhn_Patent_Families_2017-09-25.csv		from Jeff Kuhn		2005-2014	Not unless it’s in the paper (https://ssrn.com/abstract=2709238)
35	Geography of patents	https://www.nature.com/articles/sdata201674				1836-1975	https://www.nature.com/articles/sdata201674#MOESM51		Petralia, S., Balland, PA. & Rigby, D. Unveiling the geography of historical patents in the United States from 1836 to 1975. Sci Data 3, 160074 (2016). https://doi.org/10.1038/sdata.2016.74
36	Inventor disambiguation	https://dataverse.harvard.edu/dataverse/patent				1975-2010			Ronald Lai; Alexander D'Amour; Amy Yu; Ye Sun; Lee Fleming, 2011, "Disambiguation and Co-authorship Networks of the U.S. Patent Inventor Database (1975 - 2010)", https://doi.org/10.7910/DVN/5F1RRI, Harvard Dataverse, V5, UNF:5:RqsI3LsQEYLHkkg5jG/jRg== [fileUNF]				Disambiguation
37	Patent-to-article intext citations for 244 journals	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZEZWBX				197?-2015?			Bryan, Kevin, 2019, "In-Text Patent Citation Database Bryan/Ozcan/Sampat Beta version .9", https://doi.org/10.7910/DVN/ZEZWBX, Harvard Dataverse, V2, UNF:6:+28YcwvDoaxFl/9hPXQaSA== [fileUNF]
38	Patent value	https://iu.box.com/patents		Updated Mar 19, 2014 by Noah Stoffman		1926-2010
39	Government-funded US patents	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DKESRC		This includes patent level metadata, 1926-1975 (OCRed from USPTO Image PDF files), 1976-2017 (parsed from USPTO HTML files), patent meta data, CPC, geography, agencies, entity size of the patent owner etc, government support categories at patent level and finally, aggregate yearly statistics. (2019-06-02)	CC0 - "Public Domain Dedication"	1926-2017	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DKESRC	Yes	Lee Fleming; Hillary Green; Guan-Cheng Li; Matt Marx; Dennis Yao, 2019, "Replication Data for: Government-funded research increasingly fuels innovation", https://doi.org/10.7910/DVN/DKESRC	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DKESRC	N	Bulk
40	Geocoding worldwide patent data	https://doi.org/10.7910/DVN/OTTBDX				30 years			Seliger, Florian; Kozak, Jan; de Rassenfosse, Gaétan, 2019, "Geocoding of worldwide patent data", https://doi.org/10.7910/DVN/OTTBDX, Harvard Dataverse, V5				Geography
41	PatentCity	https://mailchi.mp/e0495246a573/patentcity	Yes	Data coming soon; accessible via Google BigQuery						https://github.com/Antoberge/patent_city		coming soon
42	Patent text: code, data, and new measures	https://zenodo.org/record/3515985		Different open access data files related to the text of USPTO patent documents, including 1) for each US patent a list of processed, cleaned and stemmed keywords, 2) for each patent a list of the 1,000 most similar patents (based on cosine similarity) from the entire population of US patents, 3) for each US patent the average cosine similarity with all prior patents from the previous 5 years, and the average cosine similarity with all later patents in the following 5 years, 4) each new keyword (unigram), bigram (sequence of two adjacent keywords), trigram, and pairwise keyword combination introduced for the first time in history by a US patent, the number of the patent introducing it for the first time, and the total number of patents from the entire population using these new keywords, bigrams, trigrams, and new keyword combinations.	Open Data Commons Attribution License v1.0	1969-2018	https://zenodo.org/record/3515985	Yes	Arts S, Hou J, Gomez JC. (2020). Natural language processing to identify the creation and impact of new technologies in patent text: code, data, and new measures. Forthcoming Research Policy. (https://doi.org/10.1016/j.respol.2020.104144)	https://github.com/sam-arts/respol_patents_code	Yes	Bulk	patent measures, text, natural language processing, novelty, impact, USPTO, technological progress	Arts S, Hou J, Gomez JC. (2020). Natural language processing to identify the creation and impact of new technologies in patent text: code, data, and new measures. Forthcoming Research Policy. (https://doi.org/10.1016/j.respol.2020.104144)
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98	Title	URL	I3 member author?	Description	Terms of use	Timeframe	Documentation	Performance/error metrics	Citation	Open-source code	Versioning	API or Bulk downloads	Keywords associated with this dataset	Datasets and publications using this dataset