Note, 7/10/2025: Please see this article for information on a change to the EMPLOYEE DATASET ID column and this article for information on a data correction. Welcome to the official source for Employee Payroll Costing data for the City of Chicago. This dataset offers a clean, comprehensive view of the City's payroll information by employee. About the Dataset: This has been extracted from the City of Chicago's Financial Management and Purchasing System (FMPS). FMPS is the system used to process all financial transactions made by the City of Chicago, ensuring accuracy and transparency in fiscal operations. This dataset includes useful details like employee name, pay element, pay period, fund, appropriation, department, and job title. Data Disclaimer: The following data disclaimer governs your use of the dataset extracted from the Payroll Costing module of the City of Chicago's Financial Management and Purchasing System (FMPS) or (FMPS Payroll Costing). Point-in-Time Extract: The dataset provided herein, represents a point-in-time extract from the FMPS Payroll Costing module and may not reflect real-time or up-to-date data. Financial Statement Disclaimer – Timeframe and Limitations: This dataset is provided without audit. It is essential to note that this dataset is not a component of the City's Annual Comprehensive Financial Report (ACFR). As such, it remains preliminary and is subject to the end-of-year reconciliation process inherent to the City's annual financial procedures outlined in the ACFR. Note on Pay Elements: All pay elements available in the FMPS Payroll Costing module have been included in this dataset. Previously published datasets, such as "Employee Overtime and Supplemental Earnings," contained only a subset of these pay elements. Payroll Period: The dataset's timeframe is organized into 24 payroll periods. It is important to understand that these periods may or may not directly correspond to specific earnings periods. Aggregating Data: The CIty of Chicago often has employees with the same name (including middle initials). It is vital to use the unique employee identifier code (EMPLOYEE DATASET ID) when aggregating at the employee level to avoid duplication. Data Subject to Change: This dataset is subject to updates and modifications due to the course of business, including activities such as canceling, adjusting, and reissuing checks. Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

Attrition of nurses in the US Healthcare system is at an all-time high. It is a major area of focus, especially for hospitals.

This dataset contains employee and company data useful for supervised ML, unsupervised ML, and analytics. Attrition - whether an employee left or not - is included and can be used as the target variable.

The data is synthetic and based on the IBM Watson dataset for attrition. Employee roles and departments were changed to reflect the healthcare domain. Also, known outcomes for some employees were changed to help increase the performance of ML models.

Here's an app I use as a demo based on this dataset and an ML classification model.

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The Office of Personnel Management requires government agencies, at a minimum, to query employees on job satisfaction, organizational assessment and organizational culture. VHA maintains response data for all census surveys such as the Voice of VA as well as the VA Entrance and Exit surveys.

1) Data Introduction • 'Employee dataset: Employee data' aggregates comprehensive information encompassing various aspects of employee data such as training, surveys, performance, recruitment, and attendance. This rich dataset is designed to support in-depth analysis and research in human resources.

2) Data Utilization (1) Employee dataset: Employee data has characteristics that: • The dataset includes extensive details on employee demographics, job roles, performance ratings, and other HR-related metrics. • It is an invaluable resource for modeling and predicting employee behavior and outcomes based on historical data. (2) Employee dataset: Employee data can be used to: • Human Resources Management: Assists HR professionals in making informed decisions regarding recruitment, training, and employee retention strategies. • Predictive Analysis: Enables companies to forecast trends in employee turnover and performance, aiding in proactive management and planning.

This transformed view of Employee Demographics - Public dataset counts the number of and percentage of city employees by race as self-reported by employee based on EEOC classification. This information is used by "City Employee vs. Community Demographics dataset" at https://citydata.mesaaz.gov/Economic-Development/Chart-Data-for-City-Employee-vs-Community-Demograp/bt2n-zimw

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

Description: This dataset contains detailed leave records of employees across various departments in an organization for the year 2024. It provides insights into leave patterns, types of leaves taken, and remaining leave balances. The dataset can be used for HR analytics, leave management system optimization, and employee behavior analysis.

Features: Employee: The name of the employee. Department: The department to which the employee belongs (e.g., IT, HR, Finance). Position: The job title of the employee (e.g., Software Engineer, HR Manager). Leave Type: The type of leave taken (e.g., Sick Leave, Maternity Leave, Casual Leave). Start Date: The starting date of the leave. End Date: The ending date of the leave. Days Taken: The number of days the leave lasted. Total Leave: The total leave entitlement of the employee for the year. Leave Taken: The cumulative number of leave days taken by the employee so far. Remaining Leave: The remaining leave balance for the employee. Month: The month in which the leave occurred. Potential Use Cases: HR Analytics: Analyzing leave trends across departments and positions. Leave Policy Review: Assessing if leave entitlements align with employee needs. Employee Behavior Insights: Identifying patterns in leave usage. Predictive Modeling: Forecasting leave trends for better workforce planning. This dataset is well-suited for data visualization, statistical analysis, and machine learning projects related to human resources.

This dataset has been published by the Human Resources Department of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

Distributed by

data.virginiabeach.gov

2405 Courthouse Dr.

Virginia Beach, VA 23456

Entity

Employee Salaries

Point of Contact

Human Resources

Attributes

Column: Department

Description: 3-letter department code

Column: Department Division

Description: This is the City Division that the position is assigned to.

Column: PCN

Description: Tracking number used to reference each unique position within the City.

Column: Position Title

Description: This is the title of the position (per the City’s pay plan).

Column: FLSA Status

Description: Represents the position’s status with regards to the Fair Labor Standards Act (FLSA)

“Exempt” - These positions do not qualify for overtime compensation – Generally, a position is classified as FLSA exempt if all three of the following criteria are met: 1) Paid at least $47,476 per year ($913 per week); 2) Paid on a salary basis - generally, salary basis is defined as having a guaranteed minimum amount of pay for any work week in which the employee performs any work; 3) Perform exempt job duties - Job duties are split between three classifications: executive, professional, and administrative. All three have specific job functions which, if present in the employee’s regular work, would exempt the individual from FLSA. Employees may also be exempt from overtime compensation if they are a “highly compensated employee” as defined by the FLSA or the position meets the criteria for other enumerated exemptions in the FLSA.

“Non-exempt” – These positions are eligible for overtime compensation - positions classified as FLSA non-exempt if they fail to meet any of exempt categories specified in the FLSA.

Column: Initial Hire Date

Description: This is the date that the full-time employee first began employment with the City.

Column: Date in Title

Description: This is the date that the full-time employee first began employment in their current position.

Column: Salary

Description: This is the annual salary of the full-time employee or the hourly rate of the part-time employee.

Frequency of dataset update

Monthly

This dataset comes from the Biennial City of Tempe Employee Survey questions related to employee engagement. Survey respondents are asked to rate their level of agreement on a scale of 5 to 1, where 5 means "Strongly Agree" and 1 means "Strongly Disagree".This dataset includes responses to the following statements:I have received fair consideration for advancement & promotion, when available, within City of TempeI have been mentored at workThe City's programs related to professional development & career mobility, such as educational partnerships, Tempe Professional Development Network, etc., are useful to meThe following adequately support my work-related needs: City Manager's OfficeThe following adequately support my work-related needs: Strategic Management & Diversity OfficeI believe my opinions seem to countConflict in my work area is resolved effectivelyI believe exceptional job performance is recognized appropriately by managers/supervisors in my work unitThe amount that I pay for health care benefits is reasonableI think the amount I am paid is adequate for the work I doCommunication between my work unit/pision & work units/pisions OUTSIDE my department is goodEmployees in my department take personal accountability for their actions and work performance (starting in 2018 survey)Participation in the survey is voluntary and confidential.This page provides data for the Employee Engagement performance measure. The performance measure dashboard is available at 2.13 Employee Engagement.Additional InformationSource: paper and digital survey submissionsContact: Aaron PetersonContact E-Mail: Aaron_Peterson@tempe.govData Source Type: ExcelPreparation Method: NAPublish Frequency: biennialPublish Method: ManualData Dictionary

Employee performance records are ratings of record, the performance plans on which ratings are based, supporting documentation for those ratings, and any other performance-related material required by an agency’s performance appraisal system. Acceptable performance appraisals of non-senior executive service employees. Performance records for employees as defined in 5 U.S.C. 4301(2)).

The average spend per employee is forecast to experience significant growth in all segments in 2029. As part of the positive trend, the indicator reaches the maximum value for all three different segments at the end of the comparison period. Particularly noteworthy is the segment servers, which has the highest value of ****** U.S. dollars. Find further statistics on other topics such as a comparison of the revenue in the world and a comparison of the revenue in the United States.The Statista Market Insights cover a broad range of additional markets.

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Overall and voluntary turnover data for State of Oklahoma classified employees beginning in fiscal year 2007.

Employee payroll data for all Cook County employees excluding Forest Preserves, indicating amount of base salary paid to an employee during the County fiscal quarter. Salaries are paid to employees on a bi-weekly basis. Any pay period that extended between quarters will be reported to the quarter of the Pay Period End Date. (e.g. If a Pay Period runs 02/21-03/05, that pay period would be reported in the Q2 period, as the end of the pay period falls in March - Q2) The county fiscal quarters are: Q1: December - February Q2: March - May Q3: June - August Q4: September - November The Employee Unique Identifier field is a unique number assigned to each employee for the purpose of this data set, that is not their internal employee ID number, and allows an employee to be identified in the data set over time, in case of a name change or other change. This number will be consistent within the data set, but we reserve the right to regenerate this number over time across the data set. ISSUE RESOLVED: As of 4/19/2018 there was an issue regarding employee FY2016 and FY2017 payroll in which records were duplicated in the quarterly aggregation, resulting in inflated base pay amounts. Please disregard any data extracted from this dataset prior to the correction date and use this version moving forward. KNOWN ISSUE: Several records are missing Bureau and Office information. We are working on correcting this and will update the dataset when this issue has been resolved. For data prior to Fiscal Year 2016, see datasets at https://datacatalog.cookcountyil.gov/browse?tags=payroll

Unlock valuable salary insights with our comprehensive Salary Dataset, designed for businesses, recruiters, and job seekers to analyze compensation trends, workforce planning, and market competitiveness.

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According to 35 percent of Chief Information Security Officers (CISO) from worldwide organizations, an employee or a so-called compromised insider that might inadvertently expose their credentials, giving cybercriminals access to sensitive data, was the most common cause of a data breach. A further 33 percent thought a malicious insider, who would intentionally steal the information would most likely cause a data breach in their organization in the next 12 months.

United States Private Employee: PHB: Full-Time Workers data was reported at 90.000 % in 2017. This stayed constant from the previous number of 90.000 % for 2016. United States Private Employee: PHB: Full-Time Workers data is updated yearly, averaging 90.000 % from Mar 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 91.000 % in 2003 and a record low of 87.000 % in 2000. United States Private Employee: PHB: Full-Time Workers 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.G076: Employee Benefits Survey: Private Industry.