100+ datasets found
  1. N

    Hustler, WI annual income distribution by work experience and gender...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
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    Neilsberg Research (2025). Hustler, WI annual income distribution by work experience and gender dataset: Number of individuals ages 15+ with income, 2023 // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/baad8edb-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Hustler, Wisconsin
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time, Number of males working full time for a given income bracket, Number of males working part time for a given income bracket, Number of females working full time for a given income bracket, Number of females working part time for a given income bracket
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To portray the number of individuals for both the genders (Male and Female), within each income bracket we conducted an initial analysis and categorization of the American Community Survey data. Households are categorized, and median incomes are reported based on the self-identified gender of the head of the household. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Hustler. The dataset can be utilized to gain insights into gender-based income distribution within the Hustler population, aiding in data analysis and decision-making..

    Key observations

    • Employment patterns: Within Hustler, among individuals aged 15 years and older with income, there were 63 men and 60 women in the workforce. Among them, 39 men were engaged in full-time, year-round employment, while 19 women were in full-time, year-round roles.
    • Annual income under $24,999: Of the male population working full-time, none fell within the income range of under $24,999, while none of the female population working full-time was represented in the same income bracket.
    • Annual income above $100,000: 41.03% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
    • Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)
    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Income brackets:

    • $1 to $2,499 or loss
    • $2,500 to $4,999
    • $5,000 to $7,499
    • $7,500 to $9,999
    • $10,000 to $12,499
    • $12,500 to $14,999
    • $15,000 to $17,499
    • $17,500 to $19,999
    • $20,000 to $22,499
    • $22,500 to $24,999
    • $25,000 to $29,999
    • $30,000 to $34,999
    • $35,000 to $39,999
    • $40,000 to $44,999
    • $45,000 to $49,999
    • $50,000 to $54,999
    • $55,000 to $64,999
    • $65,000 to $74,999
    • $75,000 to $99,999
    • $100,000 or more

    Variables / Data Columns

    • Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
    • Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
    • Part-Time Males: The count of males employed part-time and earning within a specified income bracket
    • Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
    • Part-Time Females: The count of females employed part-time and earning within a specified income bracket

    Employment type classifications include:

    • Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
    • Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Hustler median household income by race. You can refer the same here

  2. 🌍Work-from-Anywhere Salary Insight (2024)

    • kaggle.com
    Updated May 18, 2025
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    Atharva Soundankar (2025). 🌍Work-from-Anywhere Salary Insight (2024) [Dataset]. https://www.kaggle.com/datasets/atharvasoundankar/work-from-anywhere-salary-insight-2024
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 18, 2025
    Dataset provided by
    Kaggle
    Authors
    Atharva Soundankar
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    🧠 About the Data

    This dataset explores how remote work opportunities intersect with salaries, experience, and employment types across industries. It contains clean, structured records of 500 hypothetical employees in remote or hybrid job roles, suitable for salary modeling, HR analytics, or industry-based salary insights.

    📌 Column Descriptions

    ColumnDescription
    CompanyName of the organization where the individual is employed
    Job TitleDesignation of the employee (e.g., Software Engineer, Product Manager)
    IndustrySector of employment (e.g., Technology, Finance, Healthcare)
    LocationCity and/or country of the job or the headquarters
    Employment TypeFull-time, Part-time, Contract, or Internship
    Experience LevelJob seniority: Entry, Mid, Senior, or Lead
    Remote FlexibilityIndicates whether the job is Remote, Hybrid, or Onsite
    Salary (Annual)Annual gross salary before tax
    CurrencyCurrency in which the salary is paid (e.g., USD, EUR, INR)
    Years of ExperienceTotal years of professional experience the employee has

    📈 Potential Use Cases

    • Predictive modeling for salary based on role, experience, and location
    • Salary benchmarking per industry or employment type
    • Visualizing remote vs onsite salary disparities
    • Market research for HR and hiring trends
    • Exploratory analysis on global employment models
  3. LinkedIn Jobs Datasets

    • brightdata.com
    .json, .csv, .xlsx
    Updated Apr 4, 2024
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    Bright Data (2024). LinkedIn Jobs Datasets [Dataset]. https://brightdata.com/products/datasets/linkedin/jobs
    Explore at:
    .json, .csv, .xlsxAvailable download formats
    Dataset updated
    Apr 4, 2024
    Dataset authored and provided by
    Bright Datahttps://brightdata.com/
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    The LinkedIn Jobs Listing dataset emerges as a comprehensive resource for individuals navigating the contemporary job market. With a focus on critical employment details, the dataset encapsulates key facets of job listings, including titles, company names, locations, and employment specifics such as seniority levels and functions. This wealth of information is instrumental for job seekers looking to align their skills and aspirations with the right opportunities. The inclusion of direct application links and real-time application numbers enhances the dataset's utility, offering users a streamlined approach to engaging with potential employers. Beyond aiding job seekers, the dataset serves as a valuable tool for analysts and researchers, providing nuanced insights into industry trends and the evolving demands of the job market. The temporal aspect, captured through job posting timestamps, allows for the observation of job trends over time. Moreover, the dataset's integration of company details, including unique identifiers and LinkedIn profile links, enables a deeper exploration of hiring organizations. Whether for job seekers or analysts, the LinkedIn Jobs Listing dataset emerges as a versatile and informative repository, empowering users with the knowledge to make informed decisions in their professional pursuits.

  4. N

    Hustler, WI annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
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    Neilsberg Research (2025). Hustler, WI annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a51dff4b-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Hustler, Wisconsin
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Hustler. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

    Key observations: Insights from 2023

    Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Hustler, the median income for all workers aged 15 years and older, regardless of work hours, was $61,250 for males and $30,000 for females.

    These income figures highlight a substantial gender-based income gap in Hustler. Women, regardless of work hours, earn 49 cents for each dollar earned by men. This significant gender pay gap, approximately 51%, underscores concerning gender-based income inequality in the village of Hustler.

    - Full-time workers, aged 15 years and older: In Hustler, among full-time, year-round workers aged 15 years and older, males earned a median income of $73,906, while females earned $71,250, resulting in a 4% gender pay gap among full-time workers. This illustrates that women earn 96 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the village of Hustler.

    Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Hustler.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

    Gender classifications include:

    • Male
    • Female

    Employment type classifications include:

    • Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
    • Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

    Variables / Data Columns

    • Year: This column presents the data year. Expected values are 2010 to 2023
    • Male Total Income: Annual median income, for males regardless of work hours
    • Male FT Income: Annual median income, for males working full time, year-round
    • Male PT Income: Annual median income, for males working part time
    • Female Total Income: Annual median income, for females regardless of work hours
    • Female FT Income: Annual median income, for females working full time, year-round
    • Female PT Income: Annual median income, for females working part time

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Hustler median household income by race. You can refer the same here

  5. Data Analyst Jobs

    • kaggle.com
    Updated Jul 14, 2020
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    Larxel (2020). Data Analyst Jobs [Dataset]. https://www.kaggle.com/andrewmvd/data-analyst-jobs/notebooks
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 14, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Larxel
    Description

    Abstract

    Looking for a job as Data Analyst? Maybe this dataset can help you.

    About this dataset

    Amidst the pandemic many people lost their jobs, with this dataset it is possible to hone the job search so that more people in need can find employment. This dataset was created by picklesueat and contains more than 2000 job listing for data analyst positions, with features such as: - Salary Estimate - Location - Company Rating - Job Description - and more.

    How to use

    Acknowledgements

    If you use this dataset, please support the author.

    License

    License was not specified at the source

    Splash banner

    Photo by Chris Liverani on Unsplash

    Splash Icon

    Icon by Eucalyp available on flaticon.com

  6. T

    United States Unemployment Rate

    • tradingeconomics.com
    • pt.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 3, 2025
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    TRADING ECONOMICS (2025). United States Unemployment Rate [Dataset]. https://tradingeconomics.com/united-states/unemployment-rate
    Explore at:
    excel, xml, csv, jsonAvailable download formats
    Dataset updated
    Jul 3, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1948 - Jun 30, 2025
    Area covered
    United States
    Description

    Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  7. d

    Data from: Business Owners

    • catalog.data.gov
    • data.cityofchicago.org
    • +2more
    Updated Jul 5, 2025
    + more versions
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    data.cityofchicago.org (2025). Business Owners [Dataset]. https://catalog.data.gov/dataset/business-owners
    Explore at:
    Dataset updated
    Jul 5, 2025
    Dataset provided by
    data.cityofchicago.org
    Description

    This dataset contains the owner information for all the accounts listed in the Business License Dataset, and is sorted by Account Number. To identify the owner of a business, you will need the account number or legal name, which may be obtained from theBusiness Licenses dataset: https://data.cityofchicago.org/dataset/Business-Licenses/r5kz-chrr. Data Owner: Business Affairs & Consumer Protection. Time Period: 2002 to present. Frequency: Data is updated daily.

  8. Interview-Based Stress Assessment Dataset

    • zenodo.org
    • data.niaid.nih.gov
    Updated Dec 25, 2024
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    Kiuchi Keita; Kiuchi Keita (2024). Interview-Based Stress Assessment Dataset [Dataset]. http://doi.org/10.5281/zenodo.10440413
    Explore at:
    Dataset updated
    Dec 25, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Kiuchi Keita; Kiuchi Keita
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    # Title
    Interview-Based Stress Assessment Dataset

    # Overview
    The dataset includes stress evaluations (6 grades) assessed by interviews of 50 Japanese workers (49 completed follow-up), as well as self-reported stress and attribute information and personality information measured at the pre and one-month follow-up.

    # Data Source
    Interviews were conducted between December 2022 and January 2023. The average follow-up period was 34.2 days.
    The main variables were interview-based stress evaluation, with self-reported stress (stress load, mental symptoms and physical symptoms from the Brief Job Stress Questionnaire), well-being (life satisfaction and happiness), and burnout were measured pre and 1 month later. Interview-based stress evaluations were conducted by two occupational health professionals in addition to an evaluation by the interviewer, a psychologist.

    # Data Description
    ## main variables are total (time 1 self-reported stress), burnout, wellbeing, meanStressEv (mean overall stress ratings of interviewer and two evaluators), T2_loadAll, T2_mental, T2_physical, T2_burnout, and T2_wellbeing

    no: Record number or identifier.
    age: Age of the individual in years.
    gender: Gender of the individual. Possible values include 'male', 'female', etc.
    height_cm: Height of the individual in centimeters.
    weight_kg: Weight of the individual in kilograms.
    BMI: Body Mass Index, calculated based on height and weight.
    drinking_freq: Frequency of alcohol consumption. Example values might be 'daily', 'weekly', 'monthly', etc.
    smoking_habits: Smoking habits of the individual. Possible values include 'smoker', 'non-smoker', etc.
    money_spending_hobby: Attitude towards spending money on hobbies. Describes how much an individual spends on their hobbies.
    employment_status: Current employment status. Possible values include 'employed', 'unemployed', 'self-employed', etc.
    full_time: employment_status
    part_time: employment_status
    discretionary: employment_status
    side_job: This variable likely indicates whether the individual has a side job in addition to their primary employment. The values could be binary (yes/no) or provide more detail about the nature of the side job.
    work_type: This variable probably categorizes the type of work the individual is engaged in. It could include categories such as 'full-time', 'part-time', 'contract', 'freelance', etc.
    fixedHours: This variable might indicate whether the individual's work schedule has fixed hours. It could be a binary variable (yes/no) indicating the presence or absence of a fixed work schedule.
    rotationalShifts: This variable likely denotes whether the individual works in rotational shifts. It could be a binary (yes/no) variable or provide details on the shift rotation pattern.
    flexibleShifts: This variable possibly reflects if the individual has flexible shift options in their work. This could involve varying start and end times or the ability to switch shifts.
    flexTime: This variable might indicate the presence of 'flextime' in the individual's work arrangement, allowing them to choose their working hours within certain limits.
    adjustableWorkHours: This variable probably denotes whether the individual has the ability to adjust their work hours, suggesting a degree of flexibility in their work schedule.
    discretionaryWork: This variable could indicate whether the individual's work involves a degree of discretion or autonomy in decision-making or task execution.
    nightShift: This variable likely indicates if the individual works night shifts. It could be a simple binary (yes/no) or provide details about the frequency or regularity of night shifts.
    remote_work_freq: This variable probably measures the frequency of remote work. It could include categories like 'never', 'sometimes', 'often', or 'always'.
    primary_job_industry: This variable likely categorizes the industry sector of the individual's primary job. It could include sectors like 'technology', 'healthcare', 'education', 'finance', etc.
    ind: industry
    ind.manu–ind.gove: binary coding of industry
    primary_job_role: This variable likely represents the specific role or position held by the individual in their primary job. It could include titles like 'manager', 'engineer', 'teacher', etc.
    job: job
    job.admi–job.carClPa: binary coding of job
    job_duration_years: This variable probably indicates the duration of the individual's current job in years. It typically measures the length of time an individual has been in their current job role.
    years: Without additional context, this variable could represent various time-related aspects, such as years of experience in a particular field, age in years, or years in a specific role. It generally signifies a duration or period in years.
    months: Similar to 'years', this variable could refer to a duration in months. It might represent age in months (for younger individuals), months of experience, or months spent in a current role or activity.
    job_duration_months: This variable is likely to indicate the total duration of the individual's current job in months. It's a more precise measure compared to 'job_duration_years', especially for shorter employment periods.
    working_days_per_week: This variable probably denotes the number of days the individual works in a typical week. It helps to understand the work pattern, whether it's a standard five-day workweek or otherwise.
    work_hours_per_day: This variable likely measures the average number of hours the individual works each day. It can be used to assess work-life balance and overall workload.
    job_workload: This variable might represent the overall workload associated with the individual's job. This could be subjective (based on the individual's perception) or objective (based on quantifiable measures like hours worked or tasks completed).
    job_qualitative_load: This variable likely assesses the qualitative aspects of the job's workload, such as the level of mental or emotional stress, complexity of tasks, or level of responsibility.
    job_control: This variable probably measures the degree of control or autonomy the individual has in their job. It could assess how much freedom they have in making decisions, planning their work, or the flexibility in how they perform their duties.
    hirou_1–hirou_7: Working Conditions of Fatigue Accumulation Checklist
    hirou_kinmu: Sum of Working Conditions of Fatigue Accumulation Checklist
    WH_1–WH_2: Items related to workaholic
    workaholic: Sum of items related to workaholic
    WE_1–WE_3: Items related to work engagement
    engagement: Sum of items related to work engagement
    relationship_stress: This variable likely measures stress stemming from personal relationships, possibly including family, romantic partners, or friends.
    future_uncertainty_stress: This variable probably captures stress related to uncertainties about the future, such as career prospects, financial stability, or life goals.
    discrimination_stress: This variable indicates stress experienced due to discrimination, possibly based on factors like race, gender, age, or other personal characteristics.
    financial_stress: This variable measures stress related to financial matters, such as income, expenses, debt, or overall financial security.
    health_stress: This variable likely assesses stress concerning personal health or the health of loved ones.
    commuting_stress: This variable measures stress associated with daily commuting, such as traffic, travel time, or transportation issues.
    irregular_lifestyle: This variable probably indicates the presence of an irregular lifestyle, potentially including erratic sleep patterns, eating habits, or work schedules.
    living_env_stress: This variable likely measures stress related to the living environment, which could include housing conditions, neighborhood safety, or noise levels.
    unrewarded_efforts: This variable probably assesses feelings of stress or dissatisfaction due to efforts that are perceived as unrewarded or unacknowledged.
    other_stressors: This variable might capture additional stress factors not covered by other specific variables.
    coping: This variable likely assesses the individual's coping mechanisms or strategies in response to stress.
    support: This variable measures the level of support the individual perceives or receives, possibly from friends, family, or professional services.
    weekday_bedtime: This variable likely indicates the typical bedtime of the individual on weekdays.
    weekday_wakeup: This variable represents the typical time the individual wakes up on weekdays.
    holiday_bedtime: This variable indicates the typical bedtime of the individual on holidays or non-workdays.
    holiday_wakeup: This variable measures the typical wake-up time of the individual on holidays or non-workdays.
    avg_sleep_duration: This variable likely represents the average duration of sleep the individual gets, possibly averaged over a certain period.
    weekday_bedtime_posix: This variable might represent the weekday bedtime in POSIX time format.
    weekday_wakeup_posix: Similar to bedtime, this represents the weekday wakeup time in POSIX time format.
    holiday_bedtime_posix: This variable likely indicates the holiday bedtime in POSIX time format.
    holiday_wakeup_posix: This represents the holiday wakeup time in POSIX time format.
    weekday_bedtime_posix_hms: This variable could be the weekday bedtime in POSIX time format, specifically in hours, minutes, and seconds.
    weekday_wakeup_posix_hms: This variable might represent the weekday wakeup time in POSIX time format in hours, minutes, and seconds.
    holiday_bedtime_posix_hms: The holiday bedtime in POSIX time format, detailed to hours, minutes, and seconds.
    holiday_wakeup_posix_hms: The holiday wakeup time in POSIX time format, in hours, minutes, and

  9. A

    ‘Job Classification Dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Sep 30, 2021
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘Job Classification Dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-job-classification-dataset-151c/03ce55a1/?iid=038-911&v=presentation
    Explore at:
    Dataset updated
    Sep 30, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Job Classification Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/HRAnalyticRepository/job-classification-dataset on 30 September 2021.

    --- Dataset description provided by original source is as follows ---

    Context

    This is a dataset containing some fictional job class specs information. Typically job class specs have information which characterize the job class- its features, and a label- in this case a pay grade - something to predict that the features are related to.

    Content

    The data is a static snapshot. The contents are ID column - a sequential number Job Family ID Job Family Description Job Class ID Job Class Description PayGrade- numeric Education Level Experience Organizational Impact Problem Solving Supervision Contact Level Financial Budget PG- Alpha label for PayGrade

    Acknowledgements

    This data is purely fictional

    Inspiration

    The intent is to use machine learning classification algorithms to predict PG from Educational level through to Financial budget information.

    Typically job classification in HR is time consuming and cumbersome as a manual activity. The intent is to show how machine learning and People Analytics can be brought to bear on this task.

    --- Original source retains full ownership of the source dataset ---

  10. T

    United States Employment Rate

    • tradingeconomics.com
    • pt.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States Employment Rate [Dataset]. https://tradingeconomics.com/united-states/employment-rate
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1948 - Jun 30, 2025
    Area covered
    United States
    Description

    Employment Rate in the United States remained unchanged at 59.70 percent in June. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

  11. d

    B2B Marketing Data | B2B Leads Data | 181M+ Records | Decision Makers,...

    • datarade.ai
    Updated Jul 27, 2023
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    Exellius Systems (2023). B2B Marketing Data | B2B Leads Data | 181M+ Records | Decision Makers, Executives, CEO, MD | 20+ Attributes, Direct E-mail & Phone [Dataset]. https://datarade.ai/data-products/exellius-systems-decision-makers-executives-b2b-contact-data-exellius-systems
    Explore at:
    .xml, .csv, .xls, .txtAvailable download formats
    Dataset updated
    Jul 27, 2023
    Dataset authored and provided by
    Exellius Systems
    Area covered
    Togo, Yemen, Papua New Guinea, Bangladesh, State of, Antarctica, Albania, Kiribati, Ghana, Somalia
    Description

    Transform Your Business with Our Comprehensive B2B Marketing Data Our B2B Marketing Data is designed to be a cornerstone for data-driven professionals looking to optimize their business strategies. With an unwavering commitment to data integrity and quality, our dataset empowers you to make informed decisions, enhance your outreach efforts, and drive business growth.

    Why Choose Our B2B Marketing Data? Unmatched Data Integrity and Quality Our data is meticulously sourced and validated through rigorous processes to ensure its accuracy, relevance, and reliability. This commitment to excellence guarantees that you are equipped with the most up-to-date information, empowering your business to thrive in a competitive landscape.

    Versatile and Strategic Applications This versatile dataset caters to a wide range of business needs, including:

    Lead Generation: Identify and connect with potential clients who align with your business goals. Market Segmentation: Tailor your marketing efforts by segmenting your audience based on industry, company size, or geographical location. Personalized Marketing Campaigns: Craft personalized outreach strategies that resonate with your target audience, increasing engagement and conversion rates. B2B Communication Strategies: Enhance your communication efforts with direct access to decision-makers, ensuring your message reaches the right people. Comprehensive Data Attributes Our B2B Marketing Data offers more than just basic contact information. With over 20+ attributes, you gain in-depth insights into:

    Decision-Maker Roles: Understand the responsibilities and influence of key figures within an organization, such as CEOs, executives, and other senior management. Industry Affiliations: Analyze industry-specific data to tailor your approach to the unique dynamics of each sector. Contact Information: Direct email addresses and phone numbers streamline communication, enabling you to engage with your audience effectively and efficiently. Expansive Global Coverage Our dataset spans a wide array of countries, providing a truly global perspective for your business initiatives. Whether you're looking to expand into new markets or strengthen your presence in existing ones, our data ensures comprehensive coverage across the following regions:

    North America: United States, Canada, Mexico Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more South America: Brazil, Argentina, Chile, Colombia, and more Africa: South Africa, Nigeria, Kenya, Egypt, and more Australia and Oceania: Australia, New Zealand Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more Industry-Wide Reach Our B2B Marketing Data covers an extensive range of industries, ensuring that no matter your focus, you have access to the insights you need:

    Finance and Banking Technology Healthcare Manufacturing Retail Education Energy Real Estate Telecommunications Hospitality Transportation and Logistics Government and Public Sector Non-Profit Organizations And many more… Comprehensive Employee and Revenue Size Information Our dataset includes detailed records on company size and revenue, offering you the ability to:

    Employee Size: From small businesses with a handful of employees to large multinational corporations, we provide data across all scales. Revenue Size: Analyze companies based on their revenue brackets, allowing for precise market segmentation and targeted marketing efforts. Seamless Integration with Broader Data Offerings Our B2B Marketing Data is not just a standalone product; it integrates seamlessly with our broader suite of premium datasets. This integration enables you to create a holistic and customized approach to your data-driven initiatives, ensuring that every aspect of your business strategy is informed by the most accurate and comprehensive data available.

    Elevate Your Business with Data-Driven Precision Optimize your marketing strategies with our high-quality, reliable, and scalable B2B Marketing Data. Identify new opportunities, understand market dynamics, and connect with key decision-makers to drive your business forward. With our dataset, you’ll stay ahead of the competition and foster meaningful business relationships that lead to sustained growth.

    Unlock the full potential of your business with our B2B Marketing Data – the ultimate resource for growth, reliability, and scalability.

  12. A

    ‘HR Analytics: Job Change of Data Scientists’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘HR Analytics: Job Change of Data Scientists’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-hr-analytics-job-change-of-data-scientists-db67/latest
    Explore at:
    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘HR Analytics: Job Change of Data Scientists’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context and Content

    A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Many people signup for their training. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. Information related to demographics, education, experience are in hands from candidates signup and enrollment.

    This dataset designed to understand the factors that lead a person to leave current job for HR researches too. By model(s) that uses the current credentials,demographics,experience data you will predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision.

    The whole data divided to train and test . Target isn't included in test but the test target values data file is in hands for related tasks. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target

    Note: - The dataset is imbalanced. - Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. - Missing imputation can be a part of your pipeline as well.

    # Features #
    - enrollee_id : Unique ID for candidate

    • city: City code

    • city_ development _index : Developement index of the city (scaled)

    • gender: Gender of candidate

    • relevent_experience: Relevant experience of candidate

    • enrolled_university: Type of University course enrolled if any

    • education_level: Education level of candidate

    • major_discipline :Education major discipline of candidate

    • experience: Candidate total experience in years

    • company_size: No of employees in current employer's company

    • company_type : Type of current employer

    • last_new_job: Difference in years between previous job and current job

    • training_hours: training hours completed

    • target: 0 – Not looking for job change, 1 – Looking for a job change

    Inspiration

    --- Original source retains full ownership of the source dataset ---

  13. d

    City of Tempe 2023 Business Survey Data

    • catalog.data.gov
    • s.cnmilf.com
    • +9more
    Updated Sep 20, 2024
    + more versions
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    City of Tempe (2024). City of Tempe 2023 Business Survey Data [Dataset]. https://catalog.data.gov/dataset/city-of-tempe-2023-business-survey-data
    Explore at:
    Dataset updated
    Sep 20, 2024
    Dataset provided by
    City of Tempe
    Area covered
    Tempe
    Description

    These data include the individual responses for the City of Tempe Annual Business Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Business Survey results are used as indicators for city performance measures. The performance measures with indicators from the Business Survey include the following (as of 2023):1. Financial Stability and Vitality5.01 Quality of Business ServicesThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.Additional InformationSource: Business SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData DictionaryMethods:The survey is mailed to a random sample of businesses in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used.To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city.Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.The data are used by the ETC Institute in the final published PDF report.

  14. N

    Hustler, WI annual median income by work experience and sex dataset : Aged...

    • neilsberg.com
    csv, json
    Updated Jan 9, 2024
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    Neilsberg Research (2024). Hustler, WI annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars) [Dataset]. https://www.neilsberg.com/research/datasets/94a7d9fa-9816-11ee-99cf-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jan 9, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Hustler, Wisconsin
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 2010-2022 5-Year Estimates. To portray the income for both the genders (Male and Female), we conducted an initial analysis and categorization of the data. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Hustler. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

    Key observations: Insights from 2021

    Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Hustler, the median income for all workers aged 15 years and older, regardless of work hours, was $47,440 for males and $31,076 for females.

    These income figures highlight a substantial gender-based income gap in Hustler. Women, regardless of work hours, earn 66 cents for each dollar earned by men. This significant gender pay gap, approximately 34%, underscores concerning gender-based income inequality in the village of Hustler.

    - Full-time workers, aged 15 years and older: In Hustler, among full-time, year-round workers aged 15 years and older, males earned a median income of $52,919, while females earned $60,126

    Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.14 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

    https://i.neilsberg.com/ch/hustler-wi-income-by-gender.jpeg" alt="Hustler, WI gender based income disparity">

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

    Gender classifications include:

    • Male
    • Female

    Employment type classifications include:

    • Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
    • Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

    Variables / Data Columns

    • Year: This column presents the data year. Expected values are 2010 to 2022
    • Male Total Income: Annual median income, for males regardless of work hours
    • Male FT Income: Annual median income, for males working full time, year-round
    • Male PT Income: Annual median income, for males working part time
    • Female Total Income: Annual median income, for females regardless of work hours
    • Female FT Income: Annual median income, for females working full time, year-round
    • Female PT Income: Annual median income, for females working part time

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Hustler median household income by gender. You can refer the same here

  15. L

    Job Applicants by Gender and Ethnicity

    • data.lacity.org
    • catalog.data.gov
    • +1more
    application/rdfxml +5
    Updated Dec 1, 2016
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    Personnel Department (2016). Job Applicants by Gender and Ethnicity [Dataset]. https://data.lacity.org/Administration-Finance/Job-Applicants-by-Gender-and-Ethnicity/mkf9-fagf
    Explore at:
    json, application/rssxml, tsv, csv, xml, application/rdfxmlAvailable download formats
    Dataset updated
    Dec 1, 2016
    Dataset authored and provided by
    Personnel Department
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    City of LA job applicants by the job they applied for and demographic information.

    We are currently undergoing a data inventory to improve usability on the site. We're aware that this dataset is out of date but wanted to err on the side of making incomplete data available. Thank you for your patience, please contact the dataset owner or mayor.opendata@lacity.org with questions or ideas.

  16. Job Data from Dice.com

    • kaggle.com
    zip
    Updated Apr 25, 2020
    + more versions
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    PromptCloud (2020). Job Data from Dice.com [Dataset]. https://www.kaggle.com/promptcloud/job-data-from-dicecom
    Explore at:
    zip(16794 bytes)Available download formats
    Dataset updated
    Apr 25, 2020
    Authors
    PromptCloud
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    This dataset was created by PromptCloud and Datastock. This dataset has 30K record counts in the file and has been taken of Dice.com USA of all Job Data.

    You can download the full dataset here.

    Content

    This file contains: - uniq_id, - crawl_timestamp, - url, job_title, - company_name, - city, state, - country, - post_date, - job_description, - job_type, - company_description, - contact_person, - job_board, - geo, - site_name, - domain, - postdate_yyyymmdd, - postdate_in_indexname_format, - inferred_city, - inferred_state, - inferred_country, - fitness_score 

    Acknowledgements

    We wouldn't be here without the help of our in house web scraping team at PromptCloud and Datastock. We owe it to them.

    Inspiration

    This dataset was created for the people who want to know more about job data and for various purposes.

  17. d

    Small Business Contact Data | Global Coverage | +95% Email and Phone Data...

    • datarade.ai
    .json, .csv
    Updated Feb 27, 2024
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    Forager.ai (2024). Small Business Contact Data | Global Coverage | +95% Email and Phone Data Accuracy | Bi-weekly Refresh Rate | 50+ Data Points [Dataset]. https://datarade.ai/data-products/small-business-contact-data-bi-weekly-updates-linkedin-in-forager-ai
    Explore at:
    .json, .csvAvailable download formats
    Dataset updated
    Feb 27, 2024
    Dataset provided by
    Forager.ai
    Area covered
    Macedonia (the former Yugoslav Republic of), Oman, Cayman Islands, Namibia, Vanuatu, Slovenia, Belgium, Virgin Islands (British), Colombia, Japan
    Description

    Forager.ai's Small Business Contact Data set is a comprehensive collection of over 695M professional profiles. With an unmatched 2x/month refresh rate, we ensure the most current and dynamic data in the industry today. We deliver this data via JSONL flat-files or PostgreSQL database delivery, capturing publicly available information on each profile.

    | Volume and Stats |

    Every single record refreshed 2x per month, setting industry standards. First-party data curation powering some of the most renowned sales and recruitment platforms. Delivery frequency is hourly (fastest in the industry today). Additional datapoints and linkages available. Delivery formats: JSONL, PostgreSQL, CSV. | Datapoints |

    Over 150+ unique datapoints available! Key fields like Current Title, Current Company, Work History, Educational Background, Location, Address, and more. Unique linkage data to other social networks or contact data available. | Use Cases |

    Sales Platforms, ABM Vendors, Intent Data Companies, AdTech and more:

    Deliver the best end-customer experience with our people feed powering your solution! Be the first to know when someone changes jobs and share that with end-customers. Industry-leading data accuracy. Connect our professional records to your existing database, find new connections to other social networks, and contact data. Hashed records also available for advertising use-cases. Venture Capital and Private Equity:

    Track every company and employee with a publicly available profile. Keep track of your portfolio's founders, employees and ex-employees, and be the first to know when they move or start up. Keep an eye on the pulse by following the most influential people in the industries and segments you care about. Provide your portfolio companies with the best data for recruitment and talent sourcing. Review departmental headcount growth of private companies and benchmark their strength against competitors. HR Tech, ATS Platforms, Recruitment Solutions, as well as Executive Search Agencies:

    Build products for industry-specific and industry-agnostic candidate recruiting platforms. Track person job changes and immediately refresh profiles to avoid stale data. Identify ideal candidates through work experience and education history. Keep ATS systems and candidate profiles constantly updated. Link data from this dataset into GitHub, LinkedIn, and other social networks. | Delivery Options |

    Flat files via S3 or GCP PostgreSQL Shared Database PostgreSQL Managed Database REST API Other options available at request, depending on scale required | Other key features |

    Over 120M US Professional Profiles. 150+ Data Fields (available upon request) Free data samples, and evaluation. Tags: Professionals Data, People Data, Work Experience History, Education Data, Employee Data, Workforce Intelligence, Identity Resolution, Talent, Candidate Database, Sales Database, Contact Data, Account Based Marketing, Intent Data.

  18. T

    United States Job Openings

    • tradingeconomics.com
    • fr.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 1, 2025
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    TRADING ECONOMICS (2025). United States Job Openings [Dataset]. https://tradingeconomics.com/united-states/job-offers
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset updated
    Jul 1, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 2000 - May 31, 2025
    Area covered
    United States
    Description

    Job Offers in the United States increased to 7769 Thousand in May from 7395 Thousand in April of 2025. This dataset provides the latest reported value for - United States Job Openings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  19. s

    Bridge Fund Small Business Dataset

    • information.stpaul.gov
    Updated Oct 12, 2021
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    Saint Paul GIS (2021). Bridge Fund Small Business Dataset [Dataset]. https://information.stpaul.gov/datasets/stpaul::bridge-fund-small-business-dataset/explore
    Explore at:
    Dataset updated
    Oct 12, 2021
    Dataset authored and provided by
    Saint Paul GIS
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    In March 2020, Mayor Carter announced the Saint Paul Bridge Fund to provide emergency relief for families and small businesses most vulnerable to the economic impacts of the COVID-19 pandemic. The program was funded through $3.25 million dollars from the Saint Paul Housing and Redevelopment Authority along with contributions from philanthropic, corporate and individual donors. Through these additional contributions, the fund provided $4.1 million to families and small businesses in Saint Paul.Data previously shared in this space included only the 380 recipients funded through "Phase 1". This dataset includes all three phases that were ultimately rolled out through the Bridge Fund for Small Business program.Nearly 2,000 unique applications applied for a small business grant of $7,50036% were from ACP50 areas (Areas of Concentrated Poverty where 50% or more of the residents are people of color)The applications were reviewed in order of a random number assigned at application close. Of these applications:633 small businesses were awarded a $7,500 grant36% of applications in the city were from ACP50 areas86% of applicants in the city cited they were ordered closed under one of the Governor’s Executive OrdersThis is a dataset of the small businesses that applied for the Bridge Fund and includes:Self-reported survey responsesAward informationGeographic information Additional information about the Saint Paul Bridge Fund may be found at stpaul.gov/bridge-fund.

  20. O*NET Database

    • onetcenter.org
    excel, mysql, oracle +2
    Updated May 20, 2025
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    National Center for O*NET Development (2025). O*NET Database [Dataset]. https://www.onetcenter.org/database.html
    Explore at:
    oracle, sql server, text, mysql, excelAvailable download formats
    Dataset updated
    May 20, 2025
    Dataset provided by
    Occupational Information Network
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Dataset funded by
    US Department of Labor, Employment and Training Administration
    Description

    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)

Share
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Click to copy link
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Neilsberg Research (2025). Hustler, WI annual income distribution by work experience and gender dataset: Number of individuals ages 15+ with income, 2023 // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/baad8edb-f4ce-11ef-8577-3860777c1fe6/

Hustler, WI annual income distribution by work experience and gender dataset: Number of individuals ages 15+ with income, 2023 // 2025 Edition

Explore at:
csv, jsonAvailable download formats
Dataset updated
Feb 27, 2025
Dataset authored and provided by
Neilsberg Research
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Hustler, Wisconsin
Variables measured
Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time, Number of males working full time for a given income bracket, Number of males working part time for a given income bracket, Number of females working full time for a given income bracket, Number of females working part time for a given income bracket
Measurement technique
The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To portray the number of individuals for both the genders (Male and Female), within each income bracket we conducted an initial analysis and categorization of the American Community Survey data. Households are categorized, and median incomes are reported based on the self-identified gender of the head of the household. For additional information about these estimations, please contact us via email at research@neilsberg.com
Dataset funded by
Neilsberg Research
Description
About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Hustler. The dataset can be utilized to gain insights into gender-based income distribution within the Hustler population, aiding in data analysis and decision-making..

Key observations

  • Employment patterns: Within Hustler, among individuals aged 15 years and older with income, there were 63 men and 60 women in the workforce. Among them, 39 men were engaged in full-time, year-round employment, while 19 women were in full-time, year-round roles.
  • Annual income under $24,999: Of the male population working full-time, none fell within the income range of under $24,999, while none of the female population working full-time was represented in the same income bracket.
  • Annual income above $100,000: 41.03% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
  • Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)
Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

  • $1 to $2,499 or loss
  • $2,500 to $4,999
  • $5,000 to $7,499
  • $7,500 to $9,999
  • $10,000 to $12,499
  • $12,500 to $14,999
  • $15,000 to $17,499
  • $17,500 to $19,999
  • $20,000 to $22,499
  • $22,500 to $24,999
  • $25,000 to $29,999
  • $30,000 to $34,999
  • $35,000 to $39,999
  • $40,000 to $44,999
  • $45,000 to $49,999
  • $50,000 to $54,999
  • $55,000 to $64,999
  • $65,000 to $74,999
  • $75,000 to $99,999
  • $100,000 or more

Variables / Data Columns

  • Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
  • Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
  • Part-Time Males: The count of males employed part-time and earning within a specified income bracket
  • Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
  • Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

  • Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
  • Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hustler median household income by race. You can refer the same here

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