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)

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for information systems database systems in the U.S.

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

The Jobs of the World Database (JWD) is a publicly available database constructed by harmonizing the available micro-datasets which contain information on jobs and labor market activities in low- and middle-income countries to create a multi-country macro-dataset. The current version of the data harmonizes census data (IPUMS) and Demographic and Health Surveys (DHS). This provides coverage of countries representing about 81 percent of the world’s population, and more than 90 percent of the population in low- and middle-income countries. The database focuses on a wide range of labor market characteristics including, but not limited to: labor force participation, type of employment (e.g., waged or self-employment), sector of employment, skill level, etc. The data also contains information about internal and external migration patterns. All these aspects can be shown as aggregate at the level of country year, but also split along different characteristics including gender, education level, age groups, urban vs rural regions, etc. A major advantage of the database is the use of detailed data about household assets and dwelling characteristics to estimate a wealth density at the household level. This density was used to create wealth quintiles which can be used to investigate the labor market characteristic for different socio-economic groups (very poor, poor, average, rich, and very rich).

Apprenticeship data for Office of Apprenticeship states and SAA states. All states are available on the Data and Statistics page.

Job Data Scraper Project

This project is designed to scrape job listing data from a public website, allowing you to extract and organize important job-related information. It can be used to build a database of job listings or to gain insights into the job market.

Extracted Features

The following features are extracted from each job listing:

• Identifiers:

  • _version_: The version identifier of the job data.
  • guid: A unique identifier for the job listing.
  • reqid: The job requisition ID.
  • buid: A unique business ID.
  • id: A general identifier for the job.

• Geographical Information:

  • GeoLocation: Geographic coordinates of the job location.
  • country_exact: The exact country where the job is located.
  • city_exact: The exact city of the job location.
  • state_exact: The state or region of the job location.
  • postal_code: The postal code of the job location.

• Chronological Information:

  • date_added: The date when the job was added to the listing.
  • date_new: The date when the job became new.
  • date_updated: The last date when the job was updated.
  • salted_date: A derived date attribute.

• Company Information:

  • company_exact: The exact name of the company offering the job.
  • company_member: A specific identifier related to the company's status.

• Additional Information:

  • federal_contractor: Indicates whether the company is a federal contractor.
  • is_posted: Shows whether the job is currently posted.
  • network: Information about the company's network.
  • on_sites: Information about the onsite locations for the job.

• Job Details:

  • title_exact: The exact title of the job.
  • score: A score associated with the job listing.
  • description: The full job description.

Libraries and Skills Used

The project uses the following libraries and skills to achieve its purpose:

• Libraries:

  • requests: To send HTTP requests and fetch job data.
  • csv: To create and write data into a CSV file.
  • os: To check for the existence of files and other operating system operations.
  • random.choice: For rotating user agents to avoid detection when sending HTTP requests.

• Skills:

  • Data scraping to collect information from a public website.
  • Data extraction to pull specific features from JSON responses.
  • Data organization to structure and save data into a CSV file.
  • Error handling to manage failed HTTP requests and retry logic.
  • Pagination handling to iterate through job listings.

Applications

This project can be used to create a database of job listings, track job trends, or analyze the job market. The extracted data provides a comprehensive view of job details, company information, and location-based features. With these capabilities, this project can serve as a valuable tool for job seekers, recruiters, and business analysts.

The Database of Labor Markets and Social Security Information System (SIMS) is the most important source of information about jobs and pensions in Latin America and the Caribbean. It encompasses harmonized statistics of 25 countries in the region, assuring the comparability of the indicators among them and also over time. The dataset includes data since 1990 and it presents 72 main indicators, which can be broken down by age group, gender, zone, level of education and other. The SIMS contains information in 6 broad categories: population, employment, unemployment, income, social security and poverty. This database seeks to contribute to public policies design based on evidence to strengthen the development of the region.

This dataset provides a detailed SQL-based employee database, which is ideal for practicing SQL queries and performing database-related operations. The dataset is structured to simulate a real-world organizational database, featuring various tables related to employee information, job roles, departments, and more.

The dataset is sourced from the GitHub repository https://github.com/cmoeser5/Employee-Database-SQL. It is intended for educational purposes, particularly for learning and practicing SQL.

Tables Included - employees: Contains records of employees with fields such as employee ID, name, job title, and department. - departments: Lists departments within the organization with fields including department ID and department name. - jobs: Includes details about job roles with fields such as job ID, job title, and job description. - salaries: Provides salary information for employees, including employee ID, salary amount, and salary date. - titles: Contains historical job title data for employees, including employee ID, job title, and title date.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4MM+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

The purpose of Water/Wastewater Information System and Records Database is to track the progress of water supply and wastewater construction projects, including those being financed through the State Revolving Fund (SRF). The Department of Natural Resources’ Water Quality Bureau has the responsibility of reviewing projects, permitting water supply and wastewater construction, and facilitating SRF approvals. From water main installation to major drinking water treatment plant upgrades, from sewer extensions to new wastewater facilities, DNR staff work with applicants around the state to ensure that important public health and environmental protection goals are met. The public search function allows access to information about projects, including project managers, permits, approvals, and overall status.

Activity data for small molecules are invaluable in chemoinformatics. Various bioactivity databases exist containing detailed information of target proteins and quantitative binding data for small molecules extracted from journals and patents. In the current work, we have merged several public and commercial bioactivity databases into one bioactivity metabase. The molecular presentation, target information, and activity data of the vendor databases were standardized. The main motivation of the work was to create a single relational database which allows fast and simple data retrieval by in-house scientists. Second, we wanted to know the amount of overlap between databases by commercial and public vendors to see whether the former contain data complementing the latter. Third, we quantified the degree of inconsistency between data sources by comparing data points derived from the same scientific article cited by more than one vendor. We found that each data source contains unique data which is due to different scientific articles cited by the vendors. When comparing data derived from the same article we found that inconsistencies between the vendors are common. In conclusion, using databases of different vendors is still useful since the data overlap is not complete. It should be noted that this can be partially explained by the inconsistencies and errors in the source data.

Job Dataset

This dataset provides a comprehensive collection of synthetic job postings to facilitate research and analysis in the field of job market trends, natural language processing (NLP), and machine learning. Created for educational and research purposes, this dataset offers a diverse set of job listings across various industries and job types.

Descriptions for each of the columns in the dataset:

1. Job Id: A unique identifier for each job posting.
2. Experience: The required or preferred years of experience for the job.
3. Qualifications: The educational qualifications needed for the job.
4. Salary Range: The range of salaries or compensation offered for the position.
5. Location: The city or area where the job is located.
6. Country: The country where the job is located.
7. Latitude: The latitude coordinate of the job location.
8. Longitude: The longitude coordinate of the job location.
9. Work Type: The type of employment (e.g., full-time, part-time, contract).
10. Company Size: The approximate size or scale of the hiring company.
11. Job Posting Date: The date when the job posting was made public.
12. Preference: Special preferences or requirements for applicants (e.g., Only Male or Only Female, or Both)
13. Contact Person: The name of the contact person or recruiter for the job.
14. Contact: Contact information for job inquiries.
15. Job Title: The job title or position being advertised.
16. Role: The role or category of the job (e.g., software developer, marketing manager).
17. Job Portal: The platform or website where the job was posted.
18. Job Description: A detailed description of the job responsibilities and requirements.
19. Benefits: Information about benefits offered with the job (e.g., health insurance, retirement plans).
20. Skills: The skills or qualifications required for the job.
21. Responsibilities: Specific responsibilities and duties associated with the job.
22. Company Name: The name of the hiring company.
23. Company Profile: A brief overview of the company's background and mission.

Potential Use Cases:

• Building predictive models to forecast job market trends.
• Enhancing job recommendation systems for job seekers.
• Developing NLP models for resume parsing and job matching.
• Analyzing regional job market disparities and opportunities.
• Exploring salary prediction models for various job roles.

Acknowledgements:

We would like to express our gratitude to the Python Faker library for its invaluable contribution to the dataset generation process. Additionally, we appreciate the guidance provided by ChatGPT in fine-tuning the dataset, ensuring its quality, and adhering to ethical standards.

Note:

Please note that the examples provided are fictional and for illustrative purposes. You can tailor the descriptions and examples to match the specifics of your dataset. It is not suitable for real-world applications and should only be used within the scope of research and experimentation. You can also reach me via email at: rrana157@gmail.com

The Veterans Health Administration (VHA) Leadership and Workforce Development System (VHALWD) has 36 separate databases that contain information on people, positions, and organizations, work groups, workforce, workforce and leadership classes, workforce development programs and participation, personal development plans, supervisory levels, mentor and coach attributes, High Performance Development Model (HPDM) core competency, intern data, Equal Employment Opportunity (EEO) reporting, succession planning, workforce planning, senior executive information, applicant tracking and recruitment, Executive Career Field (ECF) position and performance information, and education funding and programs. The VHA Executive Management Program consists of the functions that fall under the purview of the VHA Executive Resources Board (ERB) and the VHA Performance Review Board (PRB). Their functions include executive development, recruitment and placement, organizational analysis, succession planning, workforce planning, EEO and Alternative Dispute Resolution (ADR) assessment, workload tracking and reporting of human capital and HR, and individual and organizational performance assessment and recognition. The method used to collect this information is a proprietary system using relational database technology. Information from these databases are joined and expanded to inform programs and processes. This combination of information is used in the administration of talent management, VHA human capital objectives, and in the support of the ERB and PRB functions.

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.

United States BED: sa: Job Losses: CO: Information data was reported at 28.000 Unit th in Dec 2017. This records an increase from the previous number of 24.000 Unit th for Sep 2017. United States BED: sa: Job Losses: CO: Information data is updated quarterly, averaging 31.000 Unit th from Sep 1992 (Median) to Dec 2017, with 102 observations. The data reached an all-time high of 95.000 Unit th in Jun 1998 and a record low of 18.000 Unit th in Mar 2012. United States BED: sa: Job Losses: CO: Information data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G043: Business Employment Dynamics.

A Comprehensive List of Open Data Portals from Around the World

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This document can cover either or both of the database and its contents (the data). Because databases can have a wide variety of content – not just factual data – rightsholders should use the Open Data Commons – Public Domain Dedication & Licence for an entire database and its contents only if everything can be placed under the terms of this document. Because even factual data can sometimes have intellectual property rights, rightsholders should use this licence to cover b...

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. LinkHub is a software system using Semantic Web RDF that manages the graph of identifier relationships and allows exploration with a variety of interfaces. It leverages Semantic Web standards-based integrated data to provide novel information retrieval to identifier-related documents through relational graph queries, simplifies and manages connections to major hubs such as UniProt, and provides useful interactive and query interfaces for exploring the integrated data. For efficiency, it is also provided with relational-database access and translation between the relational and RDF versions. LinkHub is practically useful in creating small, local hubs on common topics and then connecting these to major portals in a federated architecture; LinkHub was used to establish such a relationship between UniProt and the North East Structural Genomics Consortium. LinkHub also facilitates queries and access to information and documents related to identifiers spread across multiple databases, acting as connecting glue between different identifier spaces. LinkHub is available at hub.gersteinlab.org and hub.nesg.org with supplement, database models and full-source code. Sponsors: Funding for this work comes from NIH/NIGMS grant P50 GM62413-01, NIH grant K25 HG02378, and NSF grant DBI-0135442.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given and data on retracted papers (based on Retraction Watch database) as well as citations to/from retracted papers have been added. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2024 and single recent year data pertain to citations received during calendar year 2024. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (7) is based on the August 1, 2025 snapshot from Scopus, updated to end of citation year 2024. This work uses Scopus data. Calculations were performed using all Scopus author profiles as of August 1, 2025. If an author is not on the list, it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work. PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases. The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, see attached file on FREQUENTLY ASKED QUESTIONS. Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a