The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Comprehensive profile of occupational descriptors and characteristics for 923 O*NET-SOC occupations. Includes, knowledge, skills, abilities, tasks, work activities and additional attributes. Available as downloadable files, and web services/APIs. See: www.onetcenter.org

Historical versions of the Occupational Information Network (ONET) database starting with the prototype ONET 98 db, and from ONET 3.0 (8/2000) through ONET 26.3 (May 2022). Downloadable files from www.ONETCenter.org

When using this resource please cite the article for which it was developed (an accepted version is uploaded in this repository):

Matysiak, A., Hardy, W. and van der Velde Lucas (2024). Structural Labour Market Change and Gender Inequality in Earnings. Work, Employment and Society, DOI: 10.1177/09500170241258953.

The dataset contributes a categorisation of tasks conducted across occupations, with a distinction between social tasks directed "inward" (e.g. towards members of own organisation, co-workers, employees, etc.) and those directed "outward" (e.g. towards students, clients, patients, etc.). This provides more depth to the discussion on technology, labour market changes and gender differences in how these trends are experienced. The dataset builds on the ESCO database v1.0.8 found here.

The following task categories are available at occupation levels:

• Social
  • Social Inward
  • Social Outward
• Analytical*
• Routine**
• Manual

* Additionally, a distinction between technical and creative/artistic tasks is provided although it is not used in Matysiak et al. (2024).

** In the initial files, some task items are categorised as Routine, while some are categorised as Non-Routine. In the subsequent steps for occupation-level information, the Routine task score consists of a difference between the Routine score and the Non-Routine score (see the paper for more information).

The repository contains four data files at different stages of task development. For the codes, please see the accompanying GitHub repository. The ESCO database covers, i.a., skills/competences and attitudes, to which we jointly refer as task items (as is standard in the literature using other databases such as ONET). For detailed methodology and interpretation see Matysiak et al. (2024).

1) esco_tasks.csv - encompasses all ESCO occupations and all task items with tags on task categorisation into broader categories. It also includes the split between the "essential" and "optional" task items and the variant "management-focused" and "care-focused" measures of social tasks as used in the robustness checks in the Matysiak et al. (2024) paper.

2) esco_onet_tasks.csv - additionally includes pre-prepped task items from the ONET database, traditionally used to describe the task content of occupations. These data can be used to validate the ESCO measures.

3) esco_onet_matysiaketal2024.csv - contains a subset of the variables from esco_onet_tasks.csv used for the Matysiak et al. (2024) paper.

4) tasks_isco08_2018_stdlfs.csv - contains the final task measures after the standardisation and derivation procedures described in Matysiak et al. (2024).

5a) Matysiak et al 2024 - Structural Labour Market Change and Gender Inequality in Earnings.pdf - the Accepted Manuscript version of the Matysiak et al. (2024) paper.

5b) Appendix to Matysiak et al 2024 - Structural Labour Market Change and Gender Inequality in Earnings.pdf - the appendix with additional tables and figures for the paper.

For all details on the procedures, applied crosswalks, methods, etc. please refer to the GitHub repository and the Matysiak et al. (2024) paper.

The State of California defines the requirements for various positions through Classifications. Examples of Classifications are Office Technician, Staff Services Analyst, Information Technology Specialist I and about 3,000 others. The Federal Government classifies various occupations using ONET groupings. The data set contained here shows how the State of California maps its Classes to the ONET codes. The purpose of this mapping is to standardize reporting when needing to compare State positions to non-State positions.

Online advertised jobs data categorized by FIPS area, city, state, employer, ONET occupation code/title, NAIC industry code/title, education level, experience level, and wages. Job description and additional information data from the job orders that has been stripped of HTML is included.

1B+ job posts 42k+ job sources 5M+ unique employers 4.8M new jobs per month 18 years of data

At iQuery, we provide unparalleled insight into the labor market through our proprietary aggregated jobs data. By combining historical data with real-time job postings, our platform captures employment trends as they unfold—offering powerful predictive capabilities for workforce analysis, planning, and development.

Our specialized team of developers processes and refines 7 to 10 million job listings daily, collected from a wide array of online sources including public and private job boards, government portals, healthcare systems, and various other employment websites. We ensure data freshness by validating and de-duplicating postings and conducting daily checks to confirm their active status—making our datasets among the most accurate and current in the industry.

Our dedicated Data Services Team enhances the dataset by assigning standardized taxonomy codes for occupation (ONET), employer industry (NAICS), location (FIPS), education level, and experience requirements. We also offer crosswalk capabilities between Classification of Instructional Programs (CIP) codes and federal taxonomies, enabling education providers to align curricula with real-time and projected workforce needs. Our proprietary taxonomy extends to skills, tools, and certifications, further enriching each job posting for granular labor market analysis.

With a comprehensive blend of real-time and historical data, our platform supports economic development by tracking workforce dynamics across occupations, industries, and skill sets. Our in-house economist and analysts generate reliable labor market forecasts—including unemployment trends—often outperforming traditional forecasting models.

Our data empowers a wide range of stakeholders:

Governments can assess local labor supply to attract employers and inform policy. Researchers can uncover trends and model future workforce shifts. Employers can locate optimal labor pools for hiring needs. Colleges and universities can tailor programs and credentials to match employer demand in specific regions. iQuery delivers a data-rich foundation for workforce planning, policy development, and educational alignment—driving smarter decisions in a dynamic labor market.

Deduplicated aggregated job data by area and occupation with salary data. Data is available for USA and territories by FIPS area codes, ONET occupation code, NAIC industry code, education group, and experience group, with USD salary information by month, quarter, and year time frames. Data includes aggregations for both active jobs in given time frame and those first posted in given time frame.

1B+ job posts 42k+ job sources 5M+ unique employers 4.8M new jobs per month 18 years of data

At iQuery, we provide unparalleled insight into the labor market through our proprietary aggregated jobs data. By combining historical data with real-time job postings, our platform captures employment trends as they unfold—offering powerful predictive capabilities for workforce analysis, planning, and development.

Our specialized team of developers processes and refines 7 to 10 million job listings daily, collected from a wide array of online sources including public and private job boards, government portals, healthcare systems, and various other employment websites. We ensure data freshness by validating and de-duplicating postings and conducting daily checks to confirm their active status—making our datasets among the most accurate and current in the industry.

Our dedicated Data Services Team enhances the dataset by assigning standardized taxonomy codes for occupation (ONET), employer industry (NAICS), location (FIPS), education level, and experience requirements. We also offer crosswalk capabilities between Classification of Instructional Programs (CIP) codes and federal taxonomies, enabling education providers to align curricula with real-time and projected workforce needs. Our proprietary taxonomy extends to skills, tools, and certifications, further enriching each job posting for granular labor market analysis.

With a comprehensive blend of real-time and historical data, our platform supports economic development by tracking workforce dynamics across occupations, industries, and skill sets. Our in-house economist and analysts generate reliable labor market forecasts—including unemployment trends—often outperforming traditional forecasting models.

Our data empowers a wide range of stakeholders:

Governments can assess local labor supply to attract employers and inform policy. Researchers can uncover trends and model future workforce shifts. Employers can locate optimal labor pools for hiring needs. Colleges and universities can tailor programs and credentials to match employer demand in specific regions. iQuery delivers a data-rich foundation for workforce planning, policy development, and educational alignment—driving smarter decisions in a dynamic labor market.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅214M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

The eICU Collaborative Research Database is a multi-center database comprising deidentified health data associated with over 200,000 admissions to ICUs across the United States between 2014-2015. The database includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, and treatment information. Data is collected through the Philips eICU program, a critical care telehealth program that delivers information to caregivers at the bedside.

Onet Global Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce.While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity.Here, we fill this gap by presenting Course-Skill Atlas -- a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education's role in preparing students for the labor market.Overall, Course-Skill Atlas can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands, especially in the face of new technologies.

Väestönlaskennan tiedot Onet-le-Château

Number of private schools since the beginning of 2005/2006 CSV FORMAT

Crimes, délits et actes de délinquance par année à Onet-le-Château, par type de crimes et de délits, ration crimes et délit pour 1000 habitants.

Résultats élections législatives depuis 2002 FORMAT CSV

Dati del censimento Onet-le-Château

Údaje o sčítaní Onet-le-Château

Spis danych Onet-le-Château

Брой на държавните и частните училища от началото на 2005—2006 г. Xlsx формат

Broj privatnih škola od početka 2005./2006. CSV FORMAT