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)

Note: Sample data provided. ・ 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.https://data.nrel.gov/submissions/182

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 45 and 46 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 40 percent of the sample regularly configured their privacy on social media, on their personal mobile phones as well as on their personal PCs.

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 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.

This dataset contains the up-to-date metadata on Work Zone feeds that meet the Work Zone Data Exchange (WZDx) specifications and is registered with USDOT ITS DataHub. The current work zone data from each feed can be accessed through their respective API links. Some links provide direct access, while others require a user to create their own API access key first. Please see the attached API Key Instructions document to learn how to sign up for API keys for the requisite feeds. The ITS Work Zone Sandbox, contains an archive of work zone data collected from each feed at a rate of at least every 15 minutes. This is not intended as a replacement for the work zone feeds and in many cases does not update as frequently as the feed does.

This data asset was created in response to House Report 117-401, which stated, "The Committee directs the USAID Administrator, in consultation with the Director of the Office of Personnel Management and the Director of the Office of Management and Budget, to submit a report to the appropriate congressional committees, not later than 180 days after enactment of this Act, on USAID's workforce data that includes disaggregated demographic data and other information regarding the diversity of the workforce of USAID. Such report shall include the following data to the maximum extent practicable and permissible by law: 1) demographic data of USAID workforce disaggregated by grade or grade-equivalent; 2) assessment of agency compliance with the Equal Employment Opportunity Commission Management Directive 715; and 3) data on the overall number of individuals who are part of the workforce, including all U.S. Direct Hires, personnel under personal services contracts, and Locally Employed staff at USAID. The report shall also be published on a publicly available website of USAID in a searchable database format." This data asset fulfills the final part of this requirement, to publish the data in a searchable database format. The data are compiled from USAID's 2021 MD-715 report, available at https://www.usaid.gov/reports/md-715. The original data source is the system National Finance Center Insight owned by the Treasury Department.

Workforce Information Cubes for NASA, sourced from NASA's personnel/payroll system, gives data about who is working where and on what. Includes records for every civil service employee in NASA, snapshots of workforce composition as of certain dates, and data on personnel transactions, such as hires, losses and promotions. Updates occur every 2 weeks.

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The Washington State Department of Health presents this information as a service to the public. This includes information on the work status, practice characteristics, education, and demographics of healthcare providers, provided in response to the Washington Health Workforce Survey. This is a complete set of data across all of the responding professions. The data dictionary identifies questions that are specific to an individual profession and aren't common to all surveys. The dataset is provided without identifying information for the responding providers. More information on the Washington Health Workforce Survey can be found at www.doh.wa.gov/workforcesurvey This dataset has been federated from https://data.wa.gov/Health/Washington-Health-Workforce-Survey-Data/cvrw-ujje.

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.

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.

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.

Abstract copyright UK Data Service and data collection copyright owner.

The Workplace Employment Relations Survey (WERS) Time-Series Dataset 1980-2011 is derived from the data collected in the interviews with the main management respondent in each of the cross-section surveys in the Workplace Employment Relations Survey series (1980, 1984, 1990, 1998, 2004 and 2011) and the 1991 Employers' Manpower Skills Practices Survey (a follow-up to the 1990 WIRS). The dataset contains consistently defined variables (where possible) for data items that are present in the 1998, 2004 or 2011 surveys and at least one other previous cross-section survey in the series.

Taking a topic such as the presence of a multi-issue joint consultative committee (Questions: DJOINT and DISSUES), similar questions were first identified in each of the 1980, 1984 and 1990 surveys. The 1991 EMSPS also served as a source of data in instances where an issue was not covered in WIRS90 but was covered in EMSPS. If the questions that were found across the series were considered sufficiently similar to permit the compilation of a consistent measure, a data item would be derived from each of the relevant survey data files in turn (see syntax files Get80ts.sps, Get84ts.sps, etc.). Having derived these consistent data items, workplace records from each of the surveys were then added together (see MergeTS.sps) to form a single time-series dataset that spanned the whole of the series. An additional variable named YEAR was also added to the dataset to identify the survey from which each individual observation originates. Further details may be found in the documentation and on the gov.uk WERS 2011 webpage.

The time-series dataset also contains a link variable (TEUKLEMS) that enables observations to be linked to the EU KLEMS industry-level database, which provides measures of economic growth, productivity, employment creation, capital formation and technological change at the industry level for the UK from 1970 onwards. Further information can be found at the EU KLEMS Project web page.

For the third edition (September 2014), data for 2011 have been added to the time series file and the documentation updated accordingly. The SPSS syntax used for the addition of the 2011 data has also been deposited.


Main Topics:

The WIRS/WERS series provides statistically reliable, nationally representative data on the state of workplace relations and employment practices in Britain. In each of the five cross-section surveys in the series, the main management respondent (the most senior manager at the workplace with responsibility for employment relations) has been asked mainly factual questions covering a wide range of issues that deal with the employment relationship. Some examples include consultation and communication, pay determination arrangements, and work organization.

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">

Organizational climate is a key determinant of diverse aspects of success in work settings, including in academia. Power dynamics in higher education can result in inequitable experiences of workplace climate, potentially harming the well-being and productivity of employees. Quantifying experiences of climate across employment categories can help identify changes necessary to create a more equitable workplace for all. We developed and administered a climate survey within our academic workplace—the Department of Zoology and Physiology at the University of Wyoming—to evaluate experiences of climate across three employment categories: faculty, graduate students, and staff. Our survey included a combination of closed-response (e.g., Likert-scale) and open-ended questions. Most department members (82%) completed the survey, which was administered in fall 2021. Faculty generally reported more positive experiences than staff. Graduate students often fell between these two groups, though in some survey sections (e.g., mental health and well-being) students reported the most negative experiences of departmental climate. Three common themes emerged from the analysis of open-ended responses: equity, community, and accountability. We discuss how these themes correspond to concrete action items for improving our departmental climate, some of which have been implemented already, while others constitute future initiatives and/or require a collective push towards systemic change in academia. Finally, service work of this type often falls outside of job descriptions, requiring individuals to either work more or trade-off productivity in other areas that are formally evaluated. With the goal of minimizing this burden for others, we detail our process and provide the materials and framework necessary to streamline this process for other departments aiming to evaluate workplace climate as a key first step in building a positive work environment for all employees. Methods All methodological information can be found in the attached metadata file entitled "README_PONE-D-23-00732_ClimateSurveyResponses".

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.

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).

The respondents in the data felt that the pre-covid workplace provided them with benefits. In the hybrid model, work efficiency is improved by the presence of autonomy and flexibility. Happiness at work and productivity both increase when employees are given more responsibility.

China Women, Business and the Law Index: Workplace Score data was reported at 100.000 Score in 2023. This stayed constant from the previous number of 100.000 Score for 2022. China Women, Business and the Law Index: Workplace Score data is updated yearly, averaging 50.000 Score from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 100.000 Score in 2023 and a record low of 25.000 Score in 1994. China Women, Business and the Law Index: Workplace Score data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WBL: Governance: Women, Business and the Law Index: Annual.