The Research and Patent Clusters Dataset (“Clusters Dataset”) contains detailed information on CSET’s research clusters and patent clusters. Our research clusters are groupings of global scientific publications from Merged Academic Corpus organized into approximately 91,000 clusters based on their citations and title and abstract text similarity. Our patent clusters are groupings of global patent documents organized into approximately 116,000 clusters based on their citations, title and abstract text similarity, and Cooperative Patent Classification (CPC) text similarity. The dataset includes publications and patents in each cluster along with cluster metadata such as titles and summaries, common subjects, top producing entities.
The dataset is available on Zenodo.
Our research and patent clusters are displayed in our Map of Science and forthcoming Map of Patents.
This dataset is subject to ETO's general terms of use. If you use it, please cite us.
No.
Please cite the "Emerging Technology Observatory Research and Patent Cluster dataset," including the link.
If you use Map of Science to access cluster data, you should cite the Map of Science instead.
The Clusters Dataset contains detailed information on CSET’s research clusters and patent clusters.
For the research clusters, each cluster has the following metadata fields. Note that this dataset does not include all the cluster metadata displayed on the Map of Science cluster detail pages.
cluster_id: a unique cluster identifiercluster_title: LLM-generated cluster titlecluster_summary: LLM-generated cluster summaryn_articles: number of articles in the clustern_recent_articles: number of articles in the cluster published in the last 5 yearsdomains: top, high level domains of the clusterfields: more granular fields in the clusterkeywords: extracted phrases based on the titles and abstracts of articles in the clustertop_papers: top papers in the cluster, including titles, DOI, OpenAlex ID, year of publication, number of within-cluster citations, a core measure for that paper in that cluster, a citation ranking, a ranking for core articles, a ranking for review articles, and flags for whether or not the article is a top cited, core article, or review article,author_info: most prolific authors in that clusterauthor_orgs: top author affiliations within the clusterauthor_country_info: country information from author organizationssource_info: top sources for articles in that clusterFor the patent clusters, each cluster has the following metadata fields.
cluster_id: a unique cluster identifiercluster_title: LLM-generated cluster titlecluster_summary: LLM-generated cluster summaryn_patent_families: number of patent families in that clustermain_category: main category of patents in the clustertop_categories: top categories of patents in the clusterkeywords: extracted keywords from the patent titles and abstractstop_patents: top patent families within the cluster, including family ID, a core statistic, number of within-cluster citations, a core ranking, a citation ranking, and the patent family titletop_inventors: inventors with the most patent families in a clustertop_assignees: assignee (owners) with the most patent families in a clusterOur research clusters are generated from the Merged Academic Corpus, which includes over 280 million scientific publications from The Lens, OpenAlex, Semantic Scholar, and ArXiv. Our patent clusters are generated from patent documents from The Lens and PATSTAT.
A research cluster is a group of publications that are highly interconnected through direct citation and similarity in the text of their titles and abstracts. A patent cluster is a group of patent documents that are highly interconnected through direct citation, similarity in the text of their titles and abstracts, and similarity in their Cooperative Patent Classification.
We generate research clusters by first generating a large network connecting all scientific publications in our Merged Academic Corpus using the similarity between articles determined by the similarity in their embeddings using a pre-trained, multilingual SentenceTransformer model. We combine this with a network based on between-article citations, to generate a hybrid network that includes both the relevant citation information and text similarities between articles.
We generate patent clusters in the same way, but with patent families as the basic unit. Because the same invention is often filed in multiple countries and at multiple points in time, we group patents into families using PATSTAT identifiers and treat each family as a single document (excluding design and plant patents, as well as families with no title or abstract text in any language). The patent network also combines three signals rather than two: family-to-family citations, similarity of title and abstract embeddings, and similarity of embeddings of the text descriptions of each family's CPC and IPC classification codes.
We then organize documents into clusters using the Leiden algorithm. Essentially, this algorithm generates many random clusterings then finds the one whose clusters have the highest modularity.
However, because the clustering algorithm involves a degree of randomness, relying on the algorithm alone to generate the clusters can produce unstable results - that is, different runs of the algorithm will often assign the same document to different clusters. For research clusters, we address this by screening out documents that are less likely to convey meaningful information, which tend to "throw off" the clustering algorithm. Specifically, we screen out articles unless:
We also disregard "unstable" article links when clustering articles - that is, network connections that cause the clustering structure to vary significantly over different runs of the clustering algorithm.
For patent clusters, we do not screen out individual documents; instead, we address instability through the network connections alone, running the clustering algorithm five times and keeping only the stable links - those that place the same two families in the same cluster in at least two of the five runs.
After screening documents and connections, we run the Leiden algorithm again using only the stable documents and network links that remain. After that, we delete clusters with fewer than 50 documents, and assign each of the documents in those clusters to the remaining cluster that is most strongly connected to that document. (Documents published between clustering updates are assigned to clusters using a similar method.)
Finally, each document that was screened out is added to the cluster to which it has the strongest connection. For patent clusters, this reattachment proceeds in successive waves, so that families whose only connections are to other reattached families can still be assigned.
This process of data screening, algorithmic sorting, and selective reassignment produces clusters that are (1) highly modular; (2) large enough to be analytically meaningful - generally in the hundreds of documents; and (3) stable across different runs.
For each cluster, we include metadata to describe the clusters and their constituent documents.
Clustering updates recalculate the clustering structure of the entire dataset "from scratch" using the most recent data and the method described above. The last clustering update was in December 2025.
Existing cluster metadata updates run roughly monthly.
Virtually all CSET data team members have contributed to the Clusters Dataset in some form, whether by providing feedback, reviewing code, or helping generate ideas. In alphabetical order, some particular contributions follow:
Dewey Murdick and SciTech Strategies helped develop the clustering concept and methodology. Katherine Quinn implemented structural changes to clustering concept and methodology to include citation and text similarity. Zach Arnold wrote the original documentation, and this updated version was written by Katherine Quinn, Adrian Thinnyun, and Catherine Aiken.
Emerging technology topic classifications are based on work supported in part by the Alfred P. Sloan Foundation under Grant No. G-2023-22358. Cluster titles and summaries are based on work supported by Google.org.
| 10/13/22 | Initial release (ETO/CSET internal) |
| 11/22/23 | 2023 update following changes to MAC (ETO/CSET internal) |
| 12/15/25 | Changes to Clustering Method to include both citation and text similarity |
| 4/27/26 | Cluster titles and text summaries added |
| 7/16/26 | New Cluster Dataset published |