Construction Worker

## Overview

Construction Worker is a dataset for object detection tasks - it contains Worker annotations for 1,782 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A collection of performance indicators and regional benchmarks for consistently comparing neighborhoods (census block groups) across the US in regards to their accessibility to jobs or workers via public transit service. Accessibility was modeled by calculating total travel time between block group centroids inclusive of walking to/from transit stops, wait times, and transfers. Block groups that can be accessed in 45 minutes or less from the origin block group are considered accessible. Indicators reflect public transit service in December 2012 and employment/worker counts in 2010. Coverage is limited to census block groups within metropolitan regions served by transit agencies who share their service data in a standardized format called GTFS.

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)

This data set contains DOT employee workers compensation claim data for current and past DOT employees. Types of data include claim data consisting of PII data (SSN, Name, Date of Birth, Home Address, Financial Institution, medical, etc.) and claim data from the Department of Labor

Employee Surveillance Client Data

## Overview

Employee Surveillance Client Data is a dataset for object detection tasks - it contains Employee annotations for 959 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Digital adherence technologies (DATs) have emerged as an alternative to directly observed therapy (DOT) for supervisions of tuberculosis (TB) treatment. We conducted a meta-analysis of implementation feedback obtained from people with TB and health care workers (HCWs) involved in TB REACH Wave 6-funded DAT evaluation projects. Projects administered standardized post-implementation surveys based on the Capability, Opportunity, Motivation, Behavior (COM-B) model to people with TB and their health care workers. The surveys included questions on demographics and technology use, Likert scale questions to assess capability, opportunity, and motivation to use DAT and open-ended feedback. We summarized demographic and technology use data descriptively, generated pooled estimates of responses to Likert scale questions within each COM-B category for people with TB and health care workers using random effects models, and performed qualitative analysis of open-ended feedback using a modified framework analysis approach. The analysis included surveys administered to 1290 people with TB and 90 HCWs across 6 TB REACH-funded projects. People with TB and HCWs had an overall positive impression of DATs with pooled estimates between 4·0 to 4·8 out of 5 across COM-B categories. However, 44% of people with TB reported taking TB medications without reporting dosing via DATs and 23% reported missing a dose of medication. Common reasons included problems with electricity, network coverage, and technical issues with the DAT platform. DATs were overall perceived to reduce visits to clinics, decrease cost, increase social support, and decrease workload of HCWs. DATs were acceptable in a wide variety of settings. However, there were challenges related to the feasibility of using current DAT platforms. Implementation efforts should concentrate on ensuring access, anticipating, and addressing technical challenges, and minimizing additional cost to people with TB.

Oregon workers' compensation data about insurers and self-insured employers. The data is presented in the Department of Consumer and Business Services report at https://www.oregon.gov/dcbs/reports/compensation/Pages/index.aspx. The attached pdf provides definitions of the data.

Comprehensive dataset containing 64,793 verified Social worker businesses in United States with complete contact information, ratings, reviews, and location data.

Readme

1. Data include raw data

2. Analysis using bootstrapping method to determine whether sleep mediate shift work and QOL

3. Baron and Kenny method to determine lifestyle factor mediate shift work and QOL

Three tables display data including violations, cases with violations, and back wages involving H-2B non-imigrant visas. The tables are organized by Act and by industry

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.

Using a longitudinal matched employer-employee dataset for Portugal over the 1986-2007 period, this study analyzes the wage responses to aggregate labor market conditions for newly hired workers and existing workers within the same firm. Accounting for worker, firm, and job title heterogeneity, the data support the hypothesis that entry wages are more procyclical than wages of stayers. A one point increase in the unemployment rate decreases wages of newly hired workers within a given firm-job title by around 2.7 percent and by 2.2 percent for stayers within the same firm-job title. Finally, the results reveal a one-for-one wage response to changes in labor productivity. (JEL: E24, E32, J64)

This open-access dataset, provides a detailed time-motion study of construction work, specifically focusing on MEP (Mechanical, Electrical, and Plumbing) activities. The dataset is intended to facilitate research and analysis to improve operational efficiency and safety within the construction industry. It includes anonymized and pseudonymized data, ensuring privacy while still offering valuable insights into worker activities.

Contents: (1)Time-motion study dataset: Captures categorized work activities by MEP workers at a second-to-second level. (2) Description of work activities: Provides detailed classifications of the tasks performed, allowing for in-depth analysis.

This dataset has been made publicly available under the CC-BY-SA license, encouraging reuse and redistribution with proper attribution and share-alike terms. By downloading the dataset, users acknowledge and agree to comply with the terms outlined above.

Funding and Support: This work has been supported by the “Hukka LVI- ja sähkötöissä” (Waste in Plumbing and Electrical Work) project, funded by STUL (Electrical Contractor Association), LVI-TU (HVAC Contractor Association), and STTA (Electrical Employers Union) from Finland.

This comprehensive dataset offers valuable resources for research and analysis purposes. For further information or collaboration inquiries, feel free to reach out to discuss data collection methods and potential research partnerships: olli.seppanen@aalto.fi & christopher.gorsch@vtt.fi.

Through collecting 16 relatively small-scale motion datasets and conducting a series of in-lab expreiment, we established a 3D skeleton dataset for recognizing construction worker actions. All skeleton data were processed in four major steps, including uniform data extraction, skeleton structure alignment, resampling, and coordination transformation. Then all the aligned skeleton data will be manually annotated into four activity categories and assigned with labels. Experiment version: It contains over 61,275 samples (10 million frames) from 73 classes performed by about 300 different subjects.The dataset includes four fundamental categories of activities, including Production Activities(12), Unsafe Activities(38), Awkward Activities(10), and Common Activities(13).
However, We have carefully reviewed the licenses of all the current datasets. We found more than half of the datasets did not specify their licenses and usage policy. Therefore, in this version, we only shared the tagged and processed dataset that clearly allows redistribution and modification. For the rest of the datasets, we highlighted their URL and doi (all of them are publicly accessible and free for use). Instead of providing the processed data, we public the full preprocess codes on GitHub, which could be used to retag and process (such as converting to predefined .bvh files). All readers and users could process the source dataset by themselves. Public version： Construction Motion Data Library(CML) contains 6131 samples(ALL_DATA); among them, and 4333 samples are highly related to construction activities ( Construction_Related_Data). GitHub: https://github.com/YUANYUAN2222/Integrated-public-3D-skeleton-form-CML-library.

Hybrid models of working are on the rise in the United States according to survey data covering worker habits between 2019 and 2024. In the second quarter of 2024, ** percent of U.S. workers reported working in a hybrid manner. The emergence of the COVID-19 pandemic saw a record number of people working remotely to help curb the spread of the virus. Since then, many workers have found a new shape to their home and working lives, finding that a hybrid model of working is more flexible than always being required to work on-site.

This dataset contains information about employees from different fake companies. It is not real data, and it has been created to resemble real data. The dataset includes various details about the employees, which is useful for research and analysis. It provides a lifelike representation of employee data without compromising privacy or using real personal information.

Comprehensive dataset containing 1,999 verified Social worker businesses in Ohio, United States with complete contact information, ratings, reviews, and location data.

A rising share of employees now regularly engage in working from home (WFH), but there are concerns this can lead to “shirking from home.” We report the results of a WFH experiment at Ctrip, a 16,000-employee, NASDAQ-listed Chinese travel agency. Call center employees who volunteered to WFH were randomly assigned either to work from home or in the office for nine months. Home working led to a 13% performance increase, of which 9% was from working more minutes per shift (fewer breaks and sick days) and 4% from more calls per minute (attributed to a quieter and more convenient working environment). Home workers also reported improved work satisfaction, and their attrition rate halved, but their promotion rate conditional on performance fell. Due to the success of the experiment, Ctrip rolled out the option to WFH to the whole firm and allowed the experimental employees to reselect between the home and office. Interestingly, over half of them switched, which led to the gains from WFH almost doubling to 22%. This highlights the benefits of learning and selection effects when adopting modern management practices like WFH. JEL Codes: D24, L23, L84, M11, M54, O31.

As of March 2022, the U.S. labor force participation rate remained one percentage point below its pre-pandemic level. After accounting for the effects of slower population growth and the aging of the population in the past two years, I estimate that around 2 million workers are missing from the labor force. Individuals age 65 and older, whose participation rates remain persistently below pre-pandemic levels, constitute most of the missing labor force.

Number of employees by class of worker and gender. Data are presented for 12 months earlier, previous month and current month, as well as year-over-year and month-to-month level change and percentage change. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.