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: Matysiak, A., Hardy, W. and van der Velde Lucas (2024). Structural Labour Market Change and Gender Inequality in Earnings. Work, Employment and Society, vol. (), pp. - (to be filled in upon publication). 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). 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.

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

Replication package for "Business disruptions from social distancing"

Please cite as

Koren, Miklós and Rita Pető. 2020. "Replication package for «Business disruptions from social distancing»" [dataset] Zenodo. http://doi.org/10.5281/zenodo.4012191

License and copyright

All text (*.md, *.txt, *.tex, *.pdf) are CC-BY-4.0. All code (*.do, Makefile) are subject to the 3-clause BSD license. All derived data (data/derived/*) are subject to Open Database License. Please respect to copyright and license terms of original data vendors (data/raw/*).

Data Availability Statements

The mobility data used in this paper (SafeGraph 2020) is proprietary, but may be obtained free of charge for COVID-19-related research from the COVID-19 Consortium. The authors are not affiliated with this consortium. Researchers interested in access to the data can apply at https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com). After signing a Data Agreement, access is granted within a few days. The Consortium does not require coauthorship and does not review or approve research results before publication. Datafiles used: /monthly-patterns/patterns_backfill/2020/05/07/12/2020/02/patterns-part[1-4].csv.gz (Monthly Places Patterns for February 2020, released May 7, 2020), /monthly-patterns/patterns/2020/06/05/06/patterns-part[1-4].csv.gz (Monthly Places Patterns for February 2020, released June 5, 2020) and /core/2020/06/Core-USA-June2020-Release-CORE_POI-2020_05-2020-06-06.zip (Core Places for June 2020, released June 6, 2020). The COVID-19 Consortium will keep these datafiles accessible for researchers. The authors will assist with any reasonable replication attempts for two years following publication.

All other data used in the analysis, including raw data, are available for reuse with permissive licenses. Raw data are saved in the folder data/raw/. The Makefile in each folder shows the URLs used to download the data.

SafeGraph

Citation

SafeGraph. "Patterns [dataset]"; 2020. Downloaded 2020-06-20.

License

Proprietary, see https://shop.safegraph.com/ or https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com)

O*NET

Citation

U.S. Department of Labor/Employment and Training Administration, 2020. "O*NET Online." Downloaded 2020-03-12.

License

CC-BY-4.0 https://www.onetonline.org/help/license

Current Employment Statistics

Citation

U.S. Bureau of Labor Statistics. 2020. "Current Employment Statistics." https://www.bls.gov/ces/ Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

National Employment Matrix

Citation

U.S. Bureau of Labor Statistics. 2018. "National Employment Matrix." https://www.bls.gov/emp/data/occupational-data.htm Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

Crosswalk

Citation

U.S. Bureau of Labor Statistics. 2019. "O* NET-SOC to Occupational Outlook Handbook Crosswalk." https://www.bls.gov/emp/classifications-crosswalks/nem-onet-to-soc-crosswalk.xlsx Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

American Time Use Survey

Citation

U.S. Bureau of Labor Statistics. 2018. “American Time Use Survey.” https://www.bls.gov/tus/.

We are using the following files:

• Respondent File
• Activity File
• Who File
• Replicate Weights
• Leave Module 2017-18

License

Data is in public domain.

County Business Patterns

Citation

U.S. Bureau of the Census. 2017. "County Business Patterns." Available at https://www.census.gov/programs-surveys/cbp.html

License

https://www.census.gov/data/developers/about/terms-of-service.html

Dataset list

Raw data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/raw/bls/industry-employment/ces.txt | BLS Current Employment Statistics | Public domain | Yes |

| data/raw/bls/atus/*.dat | BLS Time Use Survey | Public domain | Yes |

| data/raw/bls/employment-matrix/matrix.xlsx | BLS National Employment Matrix | Public domain | Yes |

| data/raw/bls/crosswalk/matrix.xlsx | ONET-SOC to Occupational Outlook Handbook Crosswalk | Public domain | Yes |

| data/raw/onet/*.csv | ONET Online | Creative Commons 4.0 | Yes |

| data/raw/census/cbp/*.txt | County Business Patterns | Public domain | Yes |

| not-included/safegraph/02/*.csv| SafeGraph | Available with Data Agreement with SafeGraph | No |

| not-included/safegraph/05/*.csv| SafeGraph | Available with Data Agreement with SafeGraph | No |

Clean data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/clean/industry-employment/industry-employment.dta | BLS Current Employment Statistics | Public domain | Yes |

| data/clean/time-use/atus.dta | BLS Time Use Survey | Public domain | Yes |

| data/clean/employment-matrix/matrix.dta | BLS National Employment Matrix | Public domain | Yes |

| data/clean/onet/risks.csv | ONET Online | Creative Commons 4.0 | Yes |

| data/clean/cbp/zip_code_business_patterns.dta | County Business Patterns | Public domain | Yes |

Derived data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/derived/occupation/* | Various sources | Public domain | Yes |

| data/derived/time-use/atus_working_at_home_occupationlevel.dta | BLS Time Use Survey | Public domain | Yes |

| data/derived/crosswalk/* | Various sources | Public domain | Yes |

| not-included/safegraph/naics-zip-??.csv| SafeGraph | Available with Data Agreement with SafeGraph | Yes, with permission of SafeGraph |

| data/derived/visit/visit-change.dta| SafeGraph | Aggregated to 3-digit NAICS industries | Yes, with permission of SafeGraph |

Computational requirements

Software Requirements

• Stata (code was last run with version 16.1)
  • estout (from http://www.stata-journal.com/software/sj14-2/)
  • running make install from the root of the folder will install estout locally, and should be run once.

Portions of the code use bash scripting (make, wget, head, tail), which may require Linux or Mac OS X.

The entry point for analysis is analysis/Makefile, which can be run by GNU Make on any Unix-like system by

cd analysis
make

The dependence of outputs on code and input data is captured in the respective Makefiles.

We have used Mac OS X, but all the code should run on Linux and Windows platforms, too.

Hardware

The analysis takes a few minutes on a standard laptop.

Description of programs

1. Raw data are in data/raw/. This data is saved as it has been received from the data publisher, downloaded by the respective Makefiles. Each folder has a README.md with data citation and license terms.
2. Clean data are in data/clean/. Each folder has a Makefile that specifies the steps of data cleaning.
3. Analysis data are in data/derived/. Each folder has a Makefile that

Analysis of ‘Données recensement Onet-le-Château ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5aec4e7688ee381170143b23 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Données recensement Onet-le-Château

--- Original source retains full ownership of the source dataset ---

SummaryThe Placements dataset contains information on job placements attained through OhioMeansJobs|Cleveland-Cuyahoga County programs (July 2022 - March 2025). Includes basic job seeker information along with job placement information categorized to highlight local industry partnerships and initiatives. Data comes from ARIES, Ohio's system for workforce programs, but goes through an extensive manual cleaning and categorization. Update FrequencyQuarterlyRelated Data ItemsWorkforce Program DashboardWorkforce Program Enrollments DatasetContactsGreater Cleveland Works (formerly Cleveland-Cuyahoga County Workforce Development Board) oversees the public workforce system – helping employers find and develop the skilled workers they need and helping jobseekers find good-paying jobs. The Board currently serves over 10,000 jobseekers a year – helping the region prosper.1910 Carnegie Avenue, Cleveland, OH 44115 216-777-8200greaterclevelandworks.orgDashboard/Data-specific questions: email bryan.metlesitz@jfs.ohio.gov Data GlossaryField | Definition Customer_ID | A unique identification number for workforce data systemsCustomer_Age | The age of a customer determined by the Date of Birth entered into ARIESCustomer_Gender | The gender of a customerCustomer_Race | The race of a customerCustomer_Ethnicity | The ethnicity of a customerEmployer | The company hiring a CCWDB customerEmployer_City | The City in which the Employer is hiring a CCWDB customerEmployer_ZIP | The Zip Code in which the Employer is hiring a CCWDB customerJob_Title | The job title associated with a CCWDB customer job placement. CCWDB_Sector | A categorization of the job placement as it relates to CCWDB industry partnerships (Healthcare, Manufacturing, Information Technology)CCWDB_Job_Family | A categorization of the job placement as it related to the ONET Job Family, with minor adjustments to emphasize CCWDB industry partnerships (Built Environment, Healthcare)Program_Year | The Program Year associated with the Employment Start DateThe CCWDB Program Year runs from July-JunePY_Quarter | The Program Quarter associated with the Employment Start Date (Q1 = July - September, Q2 = October - December, Q3 = January - March, Q4 = April - June)Employment_Start_Date |Date customer begins employmentWage | The compensation associated with a new job placement. ($/hour) Enrollment_Program | Most recent workforce program a customer was enrolled before finding employmentbarriers_Low_Income | An individual or member of a family who receives now or in the last 6 months, income-based public assistance; in a family whose income is not higher than the poverty line or 705 of the lower living standard income level; is homeless; eligible for free or reduced price lunch; foster child for whom government payments are made or is an individual with a disability. barriers_Foster_Care_Status | An individual with a temporary living situation for kids whose parents cannot take care of them and whose need for care has come to the attention of child welfare agency staff. barriers_Homeless | Individual lacks a fixed, regular, and adequate nighttime residencebarriers_Veteran_Flag | Individual is a veteranbarriers_Customer_Disability_Status |  An individual without the ability to work at a substantial gainful activity due to a physical or mental impairmentbarriers_Youth_Offender | A youth involved with the justice systembarriers_Adult_Offender | An Adult involved with the justice systembarriers_TANF_Recipient | An individual who receives income and/or benefits from the federal Temporary Assistance to Needy Families program barriers_SSI_Recipient | An individual who receives Supplemental Security Income from the federal Social Security Administrationbarriers_SNAP_Recipient | An individual who receives help to buy food through the Supplemental Nutrition Assistance Programbarriers_Other_Public_Assistance_Recipient | An individual who receives some form of means-tested assistanceindex | Unique identification number for the CCWDB Open Data Placement datasetCity | The City in which the customer residesPostal | The Zip Code in which the customer residesWard | The City of Cleveland Ward in which the customer resides

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 public schools since the beginning of 2005/2006 CSV FORMAT

Financial portal Money.pl was the fastest-loading website in Poland in December 2018, with a ***-second last visual change (the amount of time taken for all elements to load on the main page). Information websites Onet.pl and Wp.pl followed, with a one-second last visual change each. Google was ninth, with *** seconds, and film database Filmweb.pl was last on the ranking, with *** seconds

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.

Kommunalwahlen seit 2001 FORMAT CSV

Данни от преброяването Onet-le-Château

Number of public and private schools since the beginning of 2005/2006 XLSX format

Presidential election results since 2002 CSV FORMAT

Dati del censimento Onet-le-Château

Résultats élections législatives depuis 2002 FORMAT CSV