Context

Similar to others who have created HR data sets, we felt that the lack of data out there for HR was limiting. It is very hard for someone to test new systems or learn People Analytics in the HR space. The only dataset most HR practitioners have is their real employee data and there are a lot of reasons why you would not want to use that when experimenting. We hope that by providing this dataset with an evergrowing variation of data points, others can learn and grow their HR data analytics and systems knowledge.

Some example test cases where someone might use this dataset:

HR Technology Testing and Mock-Ups Engagement survey tools HCM tools BI Tools Learning To Code For People Analytics Python/R/SQL HR Tech and People Analytics Educational Courses/Tools

Content

The core data CompanyData.txt has the basic demographic data about a worker. We treat this as the core data that you can join future data sets to.

Please read the Readme.md for additional information about this along with the Changelog for additional updates as they are made.

Acknowledgements

Initial names, addresses, and ages were generated using FakenameGenerator.com. All additional details including Job, compensation, and additional data sets were created by the Koluit team using random generation in Excel.

Inspiration

Our hope is this data is used in the HR or Research space to experiment and learn using HR data. Some examples that we hope this data will be used are listed above.

Contact Us

Have any suggestions for additions to the data? See any issues with our data? Want to use it for your project? Please reach out to us! https://koluit.com/ ryan@koluit.com

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.

Information supporting the management, payment and benefits of FSA personnel.

Project: Human Recourses Analysis - Human_Resources.csv

Description:

The dataset, named "Human_Resources.csv", is a comprehensive collection of employee records from a fictional company. Each row represents an individual employee, and the columns represent various features associated with that employee.

The dataset is rich, highlighting features like 'Age', 'MonthlyIncome', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department', 'EducationField', 'JobSatisfaction', and many more. The main focus is the 'Attrition' variable, which indicates whether an employee left the company or not.

Employee data were sourced from various departments, encompassing a diverse array of job roles and levels. Each employee's record provides an in-depth look into their background, job specifics, and satisfaction levels.

The dataset further includes specific indicators and parameters that were considered during employee performance assessments, offering a granular look into the complexities of each employee's experience.

For privacy reasons, certain personal details and specific identifiers have been anonymized or fictionalized. Instead of names or direct identifiers, each entry is associated with a unique 'EmployeeNumber', ensuring data privacy while retaining data integrity.

The employee records were subjected to rigorous examination, encompassing both manual assessments and automated checks. The end result of this examination, specifically whether an employee left the company or not, is clearly indicated for each record.

This dataset titled Human Resources.csv contains anonymized employee data collected for internal HR analysis and research purposes. It includes fields such as employee ID, department, gender, age, job role, and employment status. The data can be used for workforce trend analysis, HR benchmarking, diversity studies, and training models in human resource analytics.The file is provided in CSV format (3.05 MB) and adheres to general data privacy standards, with no personally identifiable information (PII).Last updated: April 11, 2025. Uploaded by Anurag Pardiash.

Human Resources datasets related to individual staff eg learning and development.

This database contains only a very small subset of the Human Resources Operational Data Store data. It supports the SSA Employee and Office Data Retrieval (SEODR) Core Services function. This service is used by management personnel to obtain job-related information about an SSA employee.

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.

Updated 30 January 2023

Version 14 of Dataset

License Update:

There has been some confusion around licensing for this data set. Dr. Carla Patalano and Dr. Rich Huebner are the original authors of this dataset.

We provide a license to anyone who wishes to use this dataset for learning or teaching. For the purposes of sharing, please follow this license:

CC-BY-NC-ND This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Codebook

https://rpubs.com/rhuebner/hrd_cb_v14

PLEASE NOTE -- I recently updated the codebook - please use the above link. A few minor discrepancies were identified between the codebook and the dataset. Please feel free to contact me through LinkedIn (www.linkedin.com/in/RichHuebner) to report discrepancies and make requests.

Context

HR data can be hard to come by, and HR professionals generally lag behind with respect to analytics and data visualization competency. Thus, Dr. Carla Patalano and I set out to create our own HR-related dataset, which is used in one of our graduate MSHRM courses called HR Metrics and Analytics, at New England College of Business. We created this data set ourselves. We use the data set to teach HR students how to use and analyze the data in Tableau Desktop - a data visualization tool that's easy to learn.

This version provides a variety of features that are useful for both data visualization AND creating machine learning / predictive analytics models. We are working on expanding the data set even further by generating even more records and a few additional features. We will be keeping this as one file/one data set for now. There is a possibility of creating a second file perhaps down the road where you can join the files together to practice SQL/joins, etc.

Note that this dataset isn't perfect. By design, there are some issues that are present. It is primarily designed as a teaching data set - to teach human resources professionals how to work with data and analytics.

Content

We have reduced the complexity of the dataset down to a single data file (v14). The CSV revolves around a fictitious company and the core data set contains names, DOBs, age, gender, marital status, date of hire, reasons for termination, department, whether they are active or terminated, position title, pay rate, manager name, and performance score.

Recent additions to the data include: - Absences - Most Recent Performance Review Date - Employee Engagement Score

Acknowledgements

Dr. Carla Patalano provided the baseline idea for creating this synthetic data set, which has been used now by over 200 Human Resource Management students at the college. Students in the course learn data visualization techniques with Tableau Desktop and use this data set to complete a series of assignments.

Inspiration

We've included some open-ended questions that you can explore and try to address through creating Tableau visualizations, or R or Python analyses. Good luck and enjoy the learning!

• Is there any relationship between who a person works for and their performance score?
• What is the overall diversity profile of the organization?
• What are our best recruiting sources if we want to ensure a diverse organization?
• Can we predict who is going to terminate and who isn't? What level of accuracy can we achieve on this?
• Are there areas of the company where pay is not equitable?

There are so many other interesting questions that could be addressed through this interesting data set. Dr. Patalano and I look forward to seeing what we can come up with.

If you have any questions or comments about the dataset, please do not hesitate to reach out to me on LinkedIn: http://www.linkedin.com/in/RichHuebner

You can also reach me via email at: Richard.Huebner@go.cambridgecollege.edu

The Green Human Resource Management research & publication dataset, which was indexed by Scopus from 2008 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.

Contains SSA Employee Information.

Human resources data

Personnel data held within the HR database containing personal data relating to Met Office staff

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 dataset is designed for use in Human Resources (HR) Analytics, specifically focusing on collaboration discovery in virtual teams. It serves to help understand and analyse interactions and contributions within remote or distributed team environments, supporting the application of Artificial Intelligence (AI) in HR [1, 2]. It is part of a larger collection of datasets intended to supplement learning how AI can be applied to various HR fields [1].

Columns

The dataset sample includes identifiers for team members such as "Jeff", "Stacy", "Lisa", "Rob", and "Emma", alongside numerical values that appear to represent collaboration metrics or contribution percentages. For instance, values like 50%, 29%, 21%, 18%, 64%, 57%, 11%, 32%, 68%, 7%, and 25% are present [2]. The source indicates that a more detailed column description can be found in an accompanying notebook [2].

Distribution

The dataset is typically provided in a CSV file format [3]. Specific numbers for rows or records are not available in the current sources, but sample files are usually updated separately to the platform [3, 4]. It is structured for analysis of virtual team collaboration dynamics.

Usage

This dataset is ideal for analysing and predicting collaboration patterns within virtual teams [1, 2]. It can be applied to use cases such as: * Understanding individual and group contributions in remote work settings [2]. * Identifying key collaborators or bottlenecks in virtual team projects. * Developing AI models to optimise virtual team performance and improve communication [1]. * Researching the effectiveness of different virtual collaboration strategies.

Coverage

The dataset has a global region coverage [5]. Specific time ranges or demographic scopes for the data points are not detailed within the provided materials, but it is suitable for general application in virtual team analysis across various contexts.

License

CCO

Who Can Use It

This dataset is intended for: * HR professionals seeking to leverage AI for talent management and team optimisation [1]. * Data scientists and analysts working on HR analytics, machine learning, and natural language processing (NLP) projects [1]. * Researchers and students studying virtual team dynamics, organisational behaviour, and the application of AI in human resources [1]. * Organisations aiming to improve virtual team effectiveness and collaboration.

Dataset Name Suggestions

• Virtual Team Collaboration Insights Dataset
• HR Virtual Collaboration Analytics
• Remote Team Dynamics Data
• Team Collaboration Metrics Dataset
• AI for Virtual Teams Dataset

Attributes

Original Data Source: Original Data Source:

This is a fictional data set

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

Dataset

This dataset was created by Davide Polizzi

Released under CC BY-SA 4.0

Contents

Success.ai’s B2B Contact Data for Human Resources Professionals Worldwide empowers businesses to connect with HR leaders across the globe. With access to over 170 million verified professional profiles, this dataset includes critical contact information for key HR decision-makers in various industries. Whether you’re targeting HR directors, talent acquisition specialists, or employee relations managers, Success.ai ensures accurate and effective outreach.

Why Choose Success.ai’s HR Professionals Data?

1. Comprehensive Contact Information:
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for HR leaders worldwide.

3. Data accuracy is backed by AI validation to ensure 99% reliability.

4. Global Reach Across HR Functions:

5. Includes profiles of HR directors, recruiters, payroll specialists, and training managers.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets:

8. Real-time updates provide the latest information about HR professionals in decision-making roles.

9. Ethical and Compliant:

10. Adheres to GDPR, CCPA, and other global privacy regulations for ethical use of data.

Data Highlights: - 170M+ Verified Professional Profiles: Includes HR professionals from diverse industries. - 50M Work Emails: Verified and AI-validated for seamless communication. - 30M Company Profiles: Rich insights to support detailed targeting. - 700M Global Professional Profiles: Enriched data for broad business objectives.

Key Features of the Dataset:

• HR Decision-Maker Profiles: Identify and connect with HR professionals at all levels, including C-suite HR leaders.
• Advanced Filters for Precision Targeting: Filter by industry, company size, location, and specific HR roles for precise results.
• AI-Driven Enrichment: Profiles enriched with actionable data for personalized engagement.

Strategic Use Cases:

1. Recruitment Solutions and HR Services:
2. Offer your HR technology, software, or services directly to decision-makers.

3. Build relationships with professionals managing recruitment, payroll, or employee engagement.

4. Corporate Training and Development:

5. Reach training managers to promote learning solutions, workshops, and skill-building programs.

6. Showcase personalized employee development initiatives.

7. Targeted Marketing Campaigns:

8. Design campaigns to promote HR-focused tools, resources, or consultancy services.

9. Leverage verified contact data for higher engagement and conversions.

10. HR Tech Solutions:

11. Present HR software, automation tools, or cloud solutions to relevant decision-makers.

12. Target professionals managing HR digital transformation.

Why Choose Success.ai?

1. Best Price Guarantee: Enjoy premium-quality datasets at competitive pricing.
2. Seamless Integration: Integrate data into your CRM using APIs or download datasets in preferred formats.
3. Data Accuracy with AI Validation: Confidence in 99% accuracy for all profiles included in the dataset.
4. Customizable and Scalable Solutions: Tailor data to your specific industry or HR role requirements.

APIs for Enhanced Functionality

1. Data Enrichment API: Enrich existing records with verified HR contact data.
2. Lead Generation API: Automate lead generation for HR-specific campaigns and initiatives.

Leverage B2B Contact Data for Human Resources Professionals Worldwide to connect with HR leaders and decision-makers in your target market. Success.ai offers verified work emails, phone numbers, and continuously updated profiles to ensure effective outreach and impactful communication.

With AI-validated accuracy and a Best Price Guarantee, Success.ai provides the ultimate solution for accessing and engaging global HR professionals. Contact us now to elevate your business strategy with precise and reliable data!

No one beats us on price. Period.

The 2020 Public Service Employee Survey (PSES) was administered by Advanis, on behalf of the Office of the Chief Human Resources Officer, Treasury Board of Canada. This comprehensive survey measured federal government employees’ opinions about their engagement, leadership, workforce, workplace, workplace well-being, compensation, diversity and inclusion, and the impacts of COVID-19. The 2020 Public Service Employee Survey was conducted from November 30, 2020 to January 29, 2021. A total of 188,786 employees in 87 federal departments and agencies responded to the 2020 Public Service Employee Survey, for a response rate of 61%. The 2020 Public Service Employee Survey datasets contain the results of the survey by year (2020, 2019 and 2018) for the Public Service and departments/agencies, and the results broken down by demographic characteristics (e.g., age, gender) and organizational units. Results for 2019 and 2018 are only provided for questions repeated in the 2020 Public Service Employee Survey.