This site is a work in progress and has not been widely shared. Content may contain errors. Feedback is welcome.
This site is undergoing review. Some annotations were human-generated, some AI-generated — all are being verified.
Back to datasets

O*NET

Occupational Information Network

OtherPublicBoth
Visit Dataset
Specific Type
Skills, activities or tasks data
Dataset Type
Cross-sectional
Institution
Department of Labor (ETA)
Institution Type
Federal government
Level of Focus
Occupation
Most Granular Level
6-digit O*NET-SOC occupation level
Perspective
Both
Time Coverage
2001-present
Frequency
Annual database updates
Sample Size
~225,000 job incumbents; ~10,000 experts since 2001
Geographic Detail
National
Occupational Classification
8-digit O*NET-SOC
Industrial Classification
Not found
Other Classification
Task-level detailed work activities (DWAs)
Key Variables
Knowledge; skills; abilities; tasks; work activities; work context; education requirements; work values
AI/Tech Tracking
Not found
Access Details
Publicly available downloads and web services
Notes
Occupational characteristics database; widely used in AI exposure and task-based automation studies

Key Papers

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

Eloundou, Manning, Mishkin, Rock (2023)

The Impact of Artificial Intelligence on the Labor Market

Webb (2019)

What Can Machines Learn, and What Does It Mean for Occupations and the Economy?

Brynjolfsson, Mitchell, Rock (2018)

Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses

Felten, Raj, Seamans (2021)

Artificial Intelligence and Jobs: Evidence from Online Vacancies

Acemoglu, Autor, Hazell, Restrepo (2020)

Skill Requirements across Firms and Labor Markets

Deming, Kahn (2017)

Skills, Tasks and Technologies: Implications for Employment and Earnings

Acemoglu, Autor (2011)

The Future of Employment: How Susceptible are Jobs to Computerisation?

Frey, Osborne (2016)

Task-Based Automation: Implications for Wages and Inequality

Hubmer (2023)

Tasks, Automation, and the Rise in US Wage Inequality

Acemoglu, Restrepo (2021)

Applying AI to Rebuild Middle Class Jobs

Autor (2024)

New Frontiers: The Origins and Content of New Work, 1940-2018

Autor, Chin, Salomons, Seegmiller (2022)

Gen-AI: Artificial Intelligence and the Future of Work

Cazzaniga, Jaumotte, Li, Melina, Panton, Pizzinelli, Rockall, Tavares (2024)

Generative AI and Jobs: A Global Analysis of Potential Effects on Job Quantity and Quality

Gmyrek, Berg, Bescond (2023)

Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Resume and Job Posting Data

Lichtinger, Maasoum (2025)

How People Use ChatGPT

Chatterji, Cunningham, Deming, Hitzig, Ong, Shan, Wadman (2025)

The Unequal Adoption of ChatGPT Exacerbates Existing Inequalities Among Workers

Humlum, Vestergaard (2024)

Tasks At Work: Comparative Advantage, Technology and Labor Demand

Acemoglu, Kong, Restrepo (2024)

Expertise

Autor, Thompson (2025)

Who Is Using AI to Code? Global Diffusion and Impact of Generative AI

Daniotti, Wachs, Feng, Neffke (2026)

How Are Patented AI, Software and Robot Technologies Related to Wage Changes in the United States?

Fossen, Samaan, Sorgner (2022)

Mapping the Future of Occupations: Transformative and Destructive Effects of New Digital Technologies on Jobs

Fossen, Sorgner (2019)

Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data

Kogan, Papanikolaou, Schmidt, Seegmiller (2023)

'Generate' the Future of Work through AI: Empirical Evidence from Online Labor Markets

Liu, Xu, Li, Tan (2023)

Labour Market Exposure to AI: Cross-Country Differences and Distributional Implications

Pizzinelli (2023)

The Potential Impact of AI Innovations on U.S. Occupations

Septiandri, Constantinides, Quercia (2024)

AI-Exposed Jobs Deteriorated Before ChatGPT

Frank, Sabet, Simon, Bana, Yu (2026)

GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

Patwardhan, Dias, Proehl, Kim, Wang, Watkins, Posada Fishman, Aljubeh, Thacker, Fauconnet, Kim, Chao, Miserendino, Chabot, Li, Sharman, Barr, Glaese, Tworek (2025)

The AI Productivity Index (APEX)

Vidgen, Thrush, Hale, Madnani, Awal, Majumder, Luger, Baines, Klyman, Saifullah, Kirk (2025)

Anthropic Economic Index Report: Economic Primitives

Appel, Massenkoff, McCrory, McCain, Heller, Neylon, Tamkin (2026)

How Adaptable Are American Workers to AI-Induced Job Displacement?

Manning, Aguirre (2026)

AI and Jobs: A Review of Theory, Estimates, and Evidence

del Rio-Chanona, Ernst, Merola, Samaan, Teutloff (2025)

Eloundou et al. (2023); Webb (2019); Brynjolfsson et al. (2018); Felten et al. (2021); Acemoglu et al. (2020); Deming & Kahn (2017); Acemoglu & Autor (2011); Frey & Osborne (2016); Hubmer (2023); Acemoglu & Restrepo (2021); Autor (2024); Autor et al. (2022); Cazzaniga et al. (2024); Gmyrek et al. (2023); Lichtinger & Maasoum (2025); Chatterji et al. (2025); Humlum & Vestergaard (2024); Acemoglu et al. (2024); Autor & Thompson (2025); Daniotti et al. (2026); Fossen et al. (2022); Fossen & Sorgner (2019); Kogan et al. (2023); Liu et al. (2023); Pizzinelli (2023); Septiandri et al. (2024); Frank et al. (2026); Patwardhan et al. (2025); Vidgen et al. (2025); Appel et al. (2026); Manning & Aguirre (2026); del Rio-Chanona et al. (2025)