reliance_on_science — Patent-to-paper citations (Reliance on Science, 2024-2026)

Description

Citations from patents to scientific articles, from the Reliance on Science dataset of M. Marx and A. Fuegi. The references to the scientific literature in patents ("non-patent literature") were matched to the scholarly works of OpenAlex, both on the front page of the patents (the references listed by the applicant or the examiner) and in their full text (the references in the body of the patent, extracted with a hybrid of machine learning and rules). Each match has a confidence score from 1 to 10 (10 being the most certain); the authors recommend a score of at least 3 or 4 for most uses, and all the matches are included here.

uspto_2025: the citations of the patents granted by the U.S. Patent and Trademark Office (USPTO), up to 2025, from the release of September 2026 (which, unlike the earlier ones, covers only the USPTO).
worldwide_2024: the citations of the patents of all the patent offices worldwide, from the release of June 2024 (the last one that covers them), with flags for USPTO patents and for self-citations.

In both networks, a directed edge goes from a patent to a work it cites; the networks are bipartite, with patents and works as the two kinds of nodes.

The dataset also contains patent-paper pairs: pairs of a patent and an article that disclose the same invention (e.g. a discovery published in a journal and patented by the same people), found from the overlap of the authors and inventors and the similarity of their titles and abstracts, and the dates. These are the undirected bipartite networks

patent_paper_pairs: the pairs of the release of September 2026 (USPTO patents up to 2021, with the works identified by Microsoft Academic Graph identifiers and a confidence level),
patent_paper_pairs_2024: the pairs of the release of June 2024 (with OpenAlex work identifiers and a score).

The other files of the earlier releases (e.g. the metadata of the works from the Microsoft Academic Graph, of the releases up to 2023) are not included; the counts of citations per patent and per work, which can be computed from the networks, also not.

The networks have the following properties:

(Node) is_patent: whether the node is a patent (otherwise it is a scientific work).
(Node) name: the identifier of the patent (e.g. us-10494607-b2: office, number and kind code; in the patent-paper pairs, US-10000036 or just the number of the USPTO patent) or of the work (its OpenAlex identifier, e.g. W3066, whose page is https://openalex.org/W3066, or, in patent_paper_pairs, its Microsoft Academic Graph identifier).
(Edge) examiner (citation networks): whether the reference was given by the examiner (otherwise by the applicant).
(Edge) confscore (citation networks): the confidence score of the match, from 1 to 10.
(Edge) wherefound (citation networks): where the reference was found in the patent: frontonly (only on the front page), bodyonly (only in the full text) or both.
(Edge) uspto (worldwide_2024): whether the patent is a USPTO patent.
(Edge) self (worldwide_2024): whether the citation is a self-citation (an inventor of the patent is an author of the work): self, notself, or unkself (unknown).
(Edge) confidence_level (patent_paper_pairs): the confidence level of the pair, high or mid.
(Edge) ppp_score (patent_paper_pairs_2024): the score of the pair given by the authors, from 1 to 4.
(Edge) days_diff (patent_paper_pairs_2024): the difference between the date of the patent application and the publication date of the work, in days.
(Edge) same (patent_paper_pairs_2024): the evidence for the pair given by the authors, e.g. same parent, same abstract (empty if none is given).

Tags

Informational Citation Unweighted Metadata

Added

2026-10-28

Citation

M. Marx, A. Fuegi, "Reliance on science: Worldwide front-page patent citations to scientific articles." Strategic Management Journal 41(9), 1572-1594 (2020) 10.1002/smj.3145 Sci-Hub
M. Marx, A. Fuegi, "Reliance on science by inventors: Hybrid extraction of in-text patent-to-article citations." Journal of Economics & Management Strategy 31(2), 369-392 (2022) 10.1111/jems.12455 Sci-Hub

Upstream URL OK

https://relianceonscience.org/
https://doi.org/10.5281/zenodo.3382981

Upstream license

CC BY-NC 4.0, https://creativecommons.org/licenses/by-nc/4.0/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
uspto_2025	7,854,697	34,825,379	4.43	58.50	690.09	3621.45	-0.02	43	0.89	Directed	Bipartite	is_patent name	examiner confscore wherefound	86.2 MiB	227.8 MiB	213.1 MiB	236.7 MiB
worldwide_2024	14,544,053	47,844,586	3.29	53.49	686.46	2073.73	-0.01	45	0.80	Directed	Bipartite	is_patent name	examiner confscore wherefound uspto self	142.6 MiB	355.4 MiB	345.5 MiB	368.8 MiB
patent_paper_pairs	104,827	59,202	1.13	0.41	3.61	0.01	-0.06	2	0.00	Undirected	Bipartite	is_patent name	confidence_level	1.9 MiB	2.8 MiB	2.7 MiB	2.4 MiB
patent_paper_pairs_2024	645,646	548,315	1.70	1.88	29.27	1255.70	0.01	36	0.00	Undirected	Bipartite	is_patent name	ppp_score days_diff same	13.0 MiB	19.4 MiB	18.9 MiB	17.7 MiB