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 data set gathered and maintained by NREL that tracks over 300 vehicles during the course of a 4-year period and how they behave in a workplace charging capacity. The data is further enriched by examining the effect of free charging versus paid charging. There is also a distinction in data marked by the onset of Covid-19. Vehicles are owned and operated by employees and range from smaller pack PHEV to larger pack BEVs.

Longitudinal Employer-Household Dynamics (LEHD): Workplace Area Characteristics (WAC)

2015-2019 Workplace Area Characteristics (WAC) for Virginia. LODES7 is based on 2010 Census Blocks.

LEHD makes available several data products that may be used to research and characterize workforce dynamics for specific groups. Learn more about this data at https://lehd.ces.census.gov/

Processing steps: Files downloaded from https://lehd.ces.census.gov/data/lodes/LODES7/va/wac/ and merged into a single file for all job types, and all segments of the workforce by year. See technical document for more details on original file structure https://lehd.ces.census.gov/data/lodes/LODES7/LODESTechDoc7.5.pdf.

In terms of cyber security measures in French companies, the source asked which proactive or reckless actions French employees executed in the workplace, regarding the protection of company and personal data in 2019. It appeared that around ** and ** percent of the respondents had never opened and email without making sure who sent it to them and had never sent a professional document via sharing platforms that were not specific to their company, indicating a proactive approach against malware. Moreover, around ** percent of the sample regularly configured their privacy on social media, on their personal mobile phones as well as on their personal PCs.

Release 2 of the restricted-access data for the Work, Family & Health Study, featuring the CAPI survey data on individual employees, collected at the Leef (long-term care) industry workplaces.

The dataset contains workplace lead measurement results collected during health hazards evaluation surveys from 1991 to 2015 for over 1,900 area lead exposure assessment. The data about exposure are estimates of lead concentration in air and on working area surfaces and are accompanied by description of location, industry, working area, the activity that generates exposure, as well as other variables.

The report contains thirteen (13) performance metrics for City's workforce development programs. Each metric can be breakdown by three demographic types (gender, race/ethnicity, and age group) and the program target population (e.g., youth and young adults, NYCHA communities) as well.

This report is a key output of an integrated data system that collects, integrates, and generates disaggregated data by Mayor's Office for Economic Opportunity (NYC Opportunity). Currently, the report is generated by the integrated database incorporating data from 18 workforce development programs managed by 5 City agencies.

There has been no single "workforce development system" in the City of New York. Instead, many discrete public agencies directly manage or fund local partners to deliver a range of different services, sometimes tailored to specific populations. As a result, program data have historically been fragmented as well, making it challenging to develop insights based on a comprehensive picture. To overcome it, NYC Opportunity collects data from 5 City agencies and builds the integrated database, and it begins to build a complete picture of how participants move through the system onto a career pathway.

Each row represents a count of unique individuals for a specific performance metric, program target population, a specific demographic group, and a specific period. For example, if the Metric Value is 2000 with Clients Served (Metric Name), NYCHA Communities (Program Target Population), Asian (Subgroup), and 2019 (Period), you can say that "In 2019, 2,000 Asian individuals participated programs targeting NYCHA communities.

Please refer to the Workforce Data Portal for further data guidance (https://workforcedata.nyc.gov/en/data-guidance), and interactive visualizations for this report (https://workforcedata.nyc.gov/en/common-metrics).

Numbers of jobs in an area by gender, and whether an employee or self-employed. This data shows the number of jobs, not the number of people. People with more than one job are counted more than once. Figures may not add up due to rounding. Figures are rounded to the nearest thousand. Percentages are rounded to one decimal place and were calculated on unrounded figures. Employees - An employee is anyone aged 16 years or over that an organisation directly pays from its payroll(s), in return for carrying out a full-time or part-time job or being on a training scheme. It excludes voluntary workers, self-employed and working owners who are not paid via PAYE People who are self-employed in a second job are included in the self-employed totals. Self-employment data is taken from the Annual Population Survey, and is by place of work. The micro data is available from the UK Data Archive. The male/female split for employee jobs was taken from the Annual Survey of Hours and Earnings data from NOMISweb.co.uk. These proportions were then applied to the BRES employee totals since the ASHE is a not a reliable source for total employee jobs. See more on the BRES website. Modelled estimates and projections of jobs are available in the GLA Employment Projections. These are considered to be the most accurate jobs estimates at borough level.

The db_emp file contains 50 employee records with details such as employee ID, name, email, phone number, hire date, job ID, salary, commission percentage, manager ID, and department ID. It provides key insights into employee roles, salaries, and reporting structures.

This Workplace Ethnography project generated content-coded data from the full population of book-length English language organizational ethnographies. Drawn from Australia, Canada, China, Colombia, France, Great Britain, Hungary, India, Israel, Japan, Norway, the Philippines, Scotland, South Africa, Sweden, Taiwan, United States, and Zambia, these ethnographies provided deep descriptions on a wide range of topics, such as worker behavior, management behavior, coworker relations, labor process, conflict and resistance, citizenship behavior, emotional labor, and sexual harassment. Coding of these characteristics yielded variables based on descriptions of worklife in specific organizational settings. The study data was collected in mainly two periods: the early 1990s and the early 2000s. The study generated 204 ethnographic cases. These cases were derived from 156 separate books since the observations reported in some books allowed the coding of multiple cases. The general scope of questions included organizational factors such as occupation, workplace organization, pay scheme, employment size, the situation of the company, the nature of company ownership, staff turnover, layoff frequency, how well the organization operated in terms of communications, recruitment and retention of personnel, and maintenance of equipment, as well as substantive facts concerning labor market opportunity, and labor force composition. On the topic of management, questions addressed leadership, organization of production, sexual harassment, and control strategies. Community factors were assessed through questions regarding unemployment and if the area was rural or urban. A series of questions addressed job satisfaction, pay, benefit package, job security, effort bargain, conflict with management/supervisors, training, worker strategies, conditions of consent/compliance, and nature of consent/compliance. The nature of work was queried through questions regarding autonomy, creativity, meaningful work, freedom of movement, comfort of work, injuries, employment status, and frequency of conflict with customers. Additional questions included size and nature of the focal group, group dynamics, conflict between the focal group and management, basis of alternative social groups at work, and if work friendships carried over to the outside. Questions about methodology covered ethnographer's theoretical orientation, focus of ethnography, ethnographer's gender, data collection method, supplemental data used, main type of supplemental data used, and position of key informant. Additional items gathered basic information about book title, author's last name, modal occupation, industry, country/region, and observer's role.

The dataset provides the list of inquiries received by DCWP’s Office of Labor Policy & Standards (OLPS). OLPS is NYC’s central resource for workers. OLPS is charged to protect and promote labor standards and policies that create fair workplaces to ensure all workers can realize their rights, regardless of immigration status. OLPS takes complaints about workplace laws and investigates claims under those laws, such as the Paid Safe and Sick Leave Law. For other issues, OLPS connects workers to relevant government agencies, legal service providers, and resources to help them access and protect their rights and get critical services.

Release 3 of the restricted-access data for the Work, Family & Health Study, featuring DBS and Actigraphy data for individual employees, collected at the Tomo (IT) industry workplaces.

Release 3 of the restricted-access data for the Work, Family & Health Study, featuring DBS and Actigraphy data for individual employees and managers, collected at the Leef (long-term care) industry workplaces.

This data set provides data regarding workplace safety at the City of Tacoma including number of injuries and cost of claims incurred.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Captures information on disability beneficiaries that are participating in one of the "the return to work initiative"; to help become self sufficient, and to limit reliance on disability benefits.

The research questionnaire was designed by adaptation measures from previous researchs for Vietnamese context. We conducted the data collection by using Google docs. We upload soft electronic copies of survey questionnaire online. The questionnaires were sent to about 1902 email addresses, which were collected from student alumni of 5 universities in Hanoi –the capital of Vietnam. We received 510 responses (response rate of 26.8%). After screening the questionnaires, bias answers were eliminated. The final sample size consists of 502 responses.

This dataset was created to explore the diverse factors impacting employee performance and satisfaction in a typical organization. It spans a variety of fields from personal demographics to performance metrics and job details, offering a comprehensive view into the dynamics of the workplace.

The inspiration behind the creation of this dataset is to provide an accessible resource for those interested in the field of HR analytics. It can be used to derive insights into employee performance, satisfaction, and overall engagement at work. This dataset is particularly useful for tasks such as predicting employee turnover, analyzing employee performance, and understanding the factors that influence job satisfaction.

History of Work

Here you find the History of Work resources as Linked Open Data. It enables you to look ups for HISCO and HISCAM scores for an incredible amount of occupational titles in numerous languages.

Data can be queried (obtained) via the SPARQL endpoint or via the example queries. If the Linked Open Data format is new to you, you might enjoy these data stories on History of Work as Linked Open Data and this user question on Is there a list of female occupations?.

NEW version - CHANGE notes

This version is dated Apr 2025 and is not backwards compatible with the previous version (Feb 2021). The major changes are: - incredible simplification of graph representation (from 81 to 12); - use of sdo (https://schema.org/) rather than schema (http://schema.org); - replacement of prov:wasDerivedFrom with sdo:isPartOf to link occupational titles to originating datasets; - etl files (used for conversion to Linked Data) now publicly available via https://github.com/rlzijdeman/rdf-hisco; - update of issues with language tags; - specfication of language tags for english (eg. @en-gb, instead of @en); - new preferred API: https://api.druid.datalegend.net/datasets/HistoryOfWork/historyOfWork-all-latest/sparql (old API will be deprecated at some point: https://api.druid.datalegend.net/datasets/HistoryOfWork/historyOfWork-all-latest/services/historyOfWork-all-latest/sparql ) .

There are bound to be some issues. Please leave report them here.

Figure 1. Part of model illustrating the basic relation between occupations, schema.org and HISCO. https://druid.datalegend.net/HistoryOfWork/historyOfWork-all-latest/assets/601beed0f7d371035bca5521" alt="hisco-basic">

Figure 2. Part of model illustrating the relation between occupation, provenance and HISCO auxiliary variables. https://druid.datalegend.net/HistoryOfWork/historyOfWork-all-latest/assets/601beed0f7d371035bca551e" alt="hisco-aux">

BackgroundA growing body of research has confirmed that workplace bullying is a source of distress and poor mental health. Here we summarize the cross-sectional and longitudinal literature on these associations.MethodsSystematic review and meta-analyses on the relation between workplace bullying and mental health.ResultsThe cross-sectional data (65 effect sizes, N = 115.783) showed positive associations between workplace bullying and symptoms of depression (r = .28, 95% CI = .23–.34), anxiety (r = .34, 95% CI = .29–.40) and stress-related psychological complaints (r = .37, 95% CI = .30–.44). Pooling the literature that investigated longitudinal relationships (26 effect sizes, N = 54.450) showed that workplace bullying was related to mental health complaints over time (r = 0.21, 95% CI = 0.13–0.21). Interestingly, baseline mental health problems were associated with subsequent exposure to workplace bullying (r = 0.18, 95% CI = 0.10–0.27; 11 effect sizes, N = 27.028).LimitationsAll data were self-reported, raising the possibility of reporting- and response set bias.ConclusionsWorkplace bullying is consistently, and in a bi-directional manner, associated with reduced mental health. This may call for intervention strategies against bullying at work.

The dataset contains workplace noise measurement results collected during health hazards evaluation surveys from 1996 to 2013 for over 580 area noise level assessments. The collected data about exposure are based on OSHA and NIOSH assessment criteria and are accompanied by description of location, industry, working area, the activity that generates exposure, as well as other variables.