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Data

CSET’s unique data-driven approach is enabled by our data team. The team includes data scientists, data research analysts, software engineers, survey and translation specialists, and more. We maintain CSET’s vast data holdings, which include nearly 60 analysis-ready datasets, offering unprecedented coverage of the emerging technology ecosystem. The team develops and deploys the latest methods in data science and machine learning to clean, link, classify, and otherwise enhance data for analytic use, as well as support the curation and annotation of original datasets - from surveys to scraped online information. Resulting research and tools are presented in CSET Data Briefs and Data Snapshots, public repositories, as well as academic conferences and publications.



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You can also check out our work on CSET’s Emerging Technology Observatory. ETO provides free, high-quality data resources leveraging CSET’s data and analytic capabilities to transform data into actionable insights. Currently, ETO hosts 10 public tools and 8 open datasets maintained by CSET’s data team. Subscribe to receive the ETO analysis and updates.

Recent Publications

Data Snapshot

The NIH’s Impact on Research and Innovation

Katherine Quinn, Steph Batalis, and Rebecca Gelles
| August 7, 2025

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This three-part series introduces CSET’s patent clusters, which connect related patents through citations and text similarity.

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Reports

Opportunities in Open Science, Metascience, and Artificial Intelligence

Catherine Aiken, Greg Tananbaum, James Dunham, Ronnie Kinoshita, and Erin McKiernan
| June 2025

This new report summarizes a March 2025 workshop hosted by CSET and ORCA, with support from NSF. The workshop brought together more than 30 experts to discuss advancing open science and metascience, and brainstorm how artificial intelligence can be a tool in those efforts. Informed by workshop panels and discussions,...

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Data Brief

Identifying Emerging Technologies in Research

Catherine Aiken, James Dunham, Jennifer Melot, and Zachary Arnold
| December 2024

This paper presents two new methods for identifying research relevant to emerging technology. The authors developed and deployed technology topic classification and targeted research field scoring over a corpus of scientific literature to identify research relevant to cybersecurity, LLM development, and chips fabrication and design—expanding CSET’s existing set of topic...

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Recent Blog Articles

CSET is partnering with top universities to expand AGORA, its living collection of 1,000+ AI-related laws, regulations, and standards.

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Blog

The Executive Order on Removing Barriers To American Leadership In Artificial Intelligence

Ronnie Kinoshita and Mia Hoffmann
| November 5, 2025

On July 31, 2025, the Trump administration released “Winning the Race: America’s AI Action Plan.” CSET has broken down the Action Plan, focusing on specific government deliverables. Our Provision and Timeline tracker breaks down which agencies are responsible for implementing recommendations and the types of actions...

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In the second installation of our blog series analyzing 147 AI-related laws enacted by Congress between January 2020 and March 2025 from AGORA, we explore the governance strategies, risk-related concepts, and harms addressed in the legislation. In the first blog, we showed that the majority of these AI-related legislative documents were drawn...

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Our People

Catherine Aiken

Director of Data Science and Research

Adrian Thinnyun

Data Research Analyst

Ben Murphy

Translation Manager

Brian Love

Senior Software Engineer

Daniel Chou

Data Scientist

Jacob Feldgoise

Senior Data Research Analyst

Katherine Quinn

Senior Data Scientist

Max Hatfield

Data Research Analyst

Rebecca Gelles

ML Engineer

Ronnie Kinoshita

Deputy Director of Data Science & Research

Shruti Agarwal

Software Engineer

Sonali Subbu Rathinam

Data Research Analyst

Related News

CSET’s Catherine Aiken shared her expert insight in an article published by Nature. The article explores an open-access dataset called Cosmos 1.0, published in Scientific Data, which uses a Wikipedia-based AI model to identify the "Momentum 100," a data-driven list of rapidly emerging technologies such as reinforcement learning, blockchain, and 3D printing.
CSET’s Ronnie Kinoshita shared her expert insight in an article published by NPR. The article explores the rapid expansion of AI data centers across the United States and the growing political backlash as communities push back against their environmental and economic impacts.
In The News

Mapping the AI Governance Landscape

October 15, 2025
🔔 The number of AI-related governance documents is rapidly proliferating, but what risks, mitigations, and other concepts do these documents actually cover? MIT AI Risk Initiative researchers Simon Mylius, Peter Slattery, Yan Zhu, Alexander Saeri, Jess Graham, Michael Noetel, and Neil Thompson teamed up with CSET’s Mina Narayanan and Adrian Thinnyun to pilot an approach to map over 950 AI governance documents to several extensible taxonomies. These taxonomies cover AI risks and actors, industry sectors targeted, and other AI-related concepts, complementing AGORA’s thematic taxonomy of risk factors, harms, governance strategies, incentives for compliance, and application areas.
As technology competition intensifies between the United States and China, governments and policy researchers are looking for metrics to assess each country’s relative strengths and weaknesses. One measure of technology innovation increasingly used by the policy community is research output. Drawing on CSET’s experiences over the last four years, this post shares our best practices for using research output to study national technological competition and inform public policy.
CSET has received a lot of questions about LLMs and their implications. But questions and discussions tend to miss some basics about LLMs and how they work. In this blog post, we ask CSET’s NLP Engineer, James Dunham, to help us explain LLMs in plain English.
Making sense of the often overwhelming world of emerging tech with data-driven tools and resources.