Dataset Overview

This dataset contains global COVID-19 case and death data by country, collected directly from the official World Health Organization (WHO) COVID-19 Dashboard. It provides a comprehensive view of the pandemic’s impact worldwide, covering the period up to 2025. The dataset is intended for researchers, analysts, and anyone interested in understanding the progression and global effects of COVID-19 through reliable, up-to-date information.

Source Information

• Website: WHO COVID-19 Dashboard
• Organization: World Health Organization (WHO)
• Data Coverage: Global (by country/territory)
• Time Period: Up to 2025

The World Health Organization is the United Nations agency responsible for international public health. The WHO COVID-19 Dashboard is a trusted source that aggregates official reports from countries and territories around the world, providing daily updates on cases, deaths, and other key metrics related to COVID-19.

Dataset Contents

• Country/Region: The name of the country or territory.
• Date: Reporting date.
• New Cases: Number of new confirmed COVID-19 cases.
• Cumulative Cases: Total confirmed COVID-19 cases to date.
• New Deaths: Number of new confirmed deaths due to COVID-19.
• Cumulative Deaths: Total deaths reported to date.
• Additional fields may include population, rates per 100,000, and more (see data files for details).

How to Use

This dataset can be used for: - Tracking the spread and trends of COVID-19 globally and by country - Modeling and forecasting pandemic progression - Comparative analysis of the pandemic’s impact across countries and regions - Visualization and reporting

Data Reliability

The data is sourced from the WHO, widely regarded as the most authoritative source for global health statistics. However, reporting practices and data completeness may vary by country and may be subject to revision as new information becomes available.

Acknowledgements

Special thanks to the WHO for making this data publicly available and to all those working to collect, verify, and report COVID-19 statistics.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

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 Earth. The dataset can be utilized to gain insights into gender-based income distribution within the Earth population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Earth, among individuals aged 15 years and older with income, there were 380 men and 236 women in the workforce. Among them, 217 men were engaged in full-time, year-round employment, while 79 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.29% fell within the income range of under $24,999, while 34.18% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 23.96% 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 Earth median household income by race. You can refer the same here

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 Black Earth town. The dataset can be utilized to gain insights into gender-based income distribution within the Black Earth town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Black Earth town, among individuals aged 15 years and older with income, there were 202 men and 148 women in the workforce. Among them, 118 men were engaged in full-time, year-round employment, while 63 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.78% 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: 39.83% of men in full-time roles earned incomes exceeding $100,000, while 36.51% 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 Black Earth town median household income by race. You can refer the same here

Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

The World Health Organization reported 6932591 Coronavirus Deaths since the epidemic began. In addition, countries reported 766440796 Coronavirus Cases. This dataset provides - World Coronavirus Deaths- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

About this dataset

Context

The dataset tabulates the population of Black Earth town by race. It includes the population of Black Earth town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Black Earth town across relevant racial categories.

Key observations

The percent distribution of Black Earth town population by race (across all racial categories recognized by the U.S. Census Bureau): 95.17% are white, 2.42% are Asian and 2.42% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Black Earth town
• Population: The population of the racial category (excluding ethnicity) in the Black Earth town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Black Earth town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Black Earth town Population by Race & Ethnicity. You can refer the same here

Historical chart and dataset showing World death rate by year from 1950 to 2025.

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 Earth. 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 Earth, the median income for all workers aged 15 years and older, regardless of work hours, was $37,763 for males and $16,019 for females.

These income figures highlight a substantial gender-based income gap in Earth. Women, regardless of work hours, earn 42 cents for each dollar earned by men. This significant gender pay gap, approximately 58%, underscores concerning gender-based income inequality in the city of Earth.

- Full-time workers, aged 15 years and older: In Earth, among full-time, year-round workers aged 15 years and older, males earned a median income of $49,236, while females earned $35,750, leading to a 27% gender pay gap among full-time workers. This illustrates that women earn 73 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Earth.

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 Earth median household income by race. You can refer the same here

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 Black Earth town. 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 Black Earth town, the median income for all workers aged 15 years and older, regardless of work hours, was $68,125 for males and $58,750 for females.

Based on these incomes, we observe a gender gap percentage of approximately 14%, indicating a significant disparity between the median incomes of males and females in Black Earth town. Women, regardless of work hours, still earn 86 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Black Earth town, among full-time, year-round workers aged 15 years and older, males earned a median income of $93,000, while females earned $78,542, leading to a 16% gender pay gap among full-time workers. This illustrates that women earn 84 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Black Earth town offers better opportunities for women in non-full-time positions.

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 Black Earth town median household income by race. You can refer the same here

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 White Earth township. The dataset can be utilized to gain insights into gender-based income distribution within the White Earth township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within White Earth township, among individuals aged 15 years and older with income, there were 247 men and 353 women in the workforce. Among them, 84 men were engaged in full-time, year-round employment, while 81 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.33% fell within the income range of under $24,999, while 7.41% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 8.33% of men in full-time roles earned incomes exceeding $100,000, while 16.05% 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 White Earth township median household income by race. You can refer the same here

Private Forest Wind Damage Assessment Spatial Database - May 2025. Published by Department of Agriculture, Food and the Marine. Available under the license Licence Not Specified (notspecified).Following Storm Darragh and Storm Éowyn during the winter of 2024/2025, and noting that many forests have been windblown around the country, Minister for Agriculture, Food and the Marine, Martin Heydon, and Minister of State for Forestry, Horticulture and Farm Safety, Michael Healy-Rae, invited key stakeholders to join department officials on a taskforce to ensure that storm-damaged forests were managed safely and appropriately. A Forestry Windblow Taskforce was set up to quantify forest damage and to identify approaches to facilitate the mobilisation of wind damaged timber.

Part of the Taskforce’s work involved initiating a detailed mapping assessment using high-resolution satellite imagery to provide information at a local or forest stand level scale. The detailed assessment of windblow damage was undertaken using high resolution satellite imagery from SkySat, and supplemented with pre and post storm Sentinel-2 and PlanetScope satellite data. In addition, drone imagery was also acquired for a number of specific locations.

The mapping exercise relied on the tasking of SkySat imagery during cloud-free weather conditions to acquire the necessary imagery data. The mapping was conducted largely between early February and the beginning of April 2025. The mapping effort focused on a target area of interest where the damage was deemed most likely to have occurred. These target areas were forests stands that were predominantly coniferous species and at least 15 years of age. These age and species criteria were used to filter both Coillte’s sub-compartment database and DAFM’s private forest dataset to confine the wind damage mapping exercise to the most relevant forests.

The windblow mapping exercise utilised a range of available EO datasets of varying spatial and temporal resolution which included: SkySat: 75% (c. 0.50 m resolution), Sentinel-2/PlanetScope: 20% (c. 10 m resolution/c. 3 m resolution), and drone imagery: 5% ( c. 0.2 m resolution).

The national estimate of private wind damage area (11,414 ha) as included in the private forest wind damage spatial database is within approximately +/- 500 hectares of the actual windblown private forest area. This uncertainty is due in part to the fact that for some parts of the country, SkySat satellite imagery has not yet been acquired. It is expected that there will be an ongoing refinement of the private forest windblow area estimate when new SkySat or other Earth Observation data becomes available over the coming months.

As part of this mapping exercise, “older” windblown areas, i.e. windblown forest areas that are more than 4 years old, were also identified and mapped. It is estimated these damage forest areas represent between 750 and 1,000 hectares of the total national area estimate of private wind damaged forest.

The area of wind damage in broadleaf stands may be greater than identified in the private forest wind damage database given the focus in the mapping exercise on coniferous species that were at least 15 years of age. This is also due in part to the fact that the identification of windblow in broadleaf stands is more challenging, particularly if the damage impacts individual trees.

The output from the mapping assessment is an ESRI Shapefile polygon database of wind damaged, privately owned forest areas greater than or equal to 0.1 hectares. The Shapefile is provided in the Irish Transverse Mercator geographic coordinate system. The main attribute included the spatial database table is area in hectares for each wind damaged forest area delineated.

These data are provisional in that they are a record of DAFM data holding in relation to private wind damaged forest at this time (May 2025). They are not published as legal definitions of the current actuality with regard to their geographic extent. They may contain errors and omissions and it should also be noted that the data cannot be taken as being absolutely current. Therefore they should be treated as indicative of the actual geographic situation. The Department of Agriculture, Food and the Marine will accept no liability for any loss or damage suffered by those using this data for any purpose whatsoever....

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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 Blue Earth City township. The dataset can be utilized to gain insights into gender-based income distribution within the Blue Earth City township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Blue Earth City township, among individuals aged 15 years and older with income, there were 198 men and 182 women in the workforce. Among them, 122 men were engaged in full-time, year-round employment, while 78 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.20% fell within the income range of under $24,999, while 10.26% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 25.41% of men in full-time roles earned incomes exceeding $100,000, while 11.54% 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 Blue Earth City township median household income by race. You can refer the same here

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 Blue Earth County. The dataset can be utilized to gain insights into gender-based income distribution within the Blue Earth County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Blue Earth County, among individuals aged 15 years and older with income, there were 27,065 men and 26,869 women in the workforce. Among them, 13,222 men were engaged in full-time, year-round employment, while 10,271 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.21% fell within the income range of under $24,999, while 13.32% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 19.80% of men in full-time roles earned incomes exceeding $100,000, while 8.16% 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 Blue Earth County median household income by race. You can refer the same here

The GBIF Backbone Taxonomy is a single, synthetic management classification with the goal of covering all names GBIF is dealing with. It's the taxonomic backbone that allows GBIF to integrate name based information from different resources, no matter if these are occurrence datasets, species pages, names from nomenclators or external sources like EOL, Genbank or IUCN. This backbone allows taxonomic search, browse and reporting operations across all those resources in a consistent way and to provide means to crosswalk names from one source to another.

It is updated regulary through an automated process in which the Catalogue of Life acts as a starting point also providing the complete higher classification above families. Additional scientific names only found in other authoritative nomenclatural and taxonomic datasets are then merged into the tree, thus extending the original catalogue and broadening the backbones name coverage. The GBIF Backbone taxonomy also includes identifiers for Operational Taxonomic Units (OTUs) drawn from the barcoding resources iBOL and UNITE.

International Barcode of Life project (iBOL), Barcode Index Numbers (BINs). BINs are connected to a taxon name and its classification by taking into account all names applied to the BIN and picking names with at least 80% consensus. If there is no consensus of name at the species level, the selection process is repeated moving up the major Linnaean ranks until consensus is achieved.

UNITE - Unified system for the DNA based fungal species, Species Hypotheses (SHs). SHs are connected to a taxon name and its classification based on the determination of the RefS (reference sequence) if present or the RepS (representative sequence). In the latter case, if there is no match in the UNITE taxonomy, the lowest rank with 100% consensus within the SH will be used.

The GBIF Backbone Taxonomy is available for download at https://hosted-datasets.gbif.org/datasets/backbone/ in different formats together with an archive of all previous versions.

The following 105 sources have been used to assemble the GBIF backbone with number of names given in brackets:

• Catalogue of Life Checklist - 4766428 names
• International Barcode of Life project (iBOL) Barcode Index Numbers (BINs) - 635951 names
• UNITE - Unified system for the DNA based fungal species linked to the classification - 611208 names
• The Paleobiology Database - 212054 names
• World Register of Marine Species - 188857 names
• The Interim Register of Marine and Nonmarine Genera - 183894 names
• The World Checklist of Vascular Plants (WCVP) - 131891 names
• GBIF Backbone Taxonomy - 114350 names
• TAXREF - 109374 names
• The Leipzig catalogue of vascular plants - 75380 names
• ZooBank - 73549 names
• Integrated Taxonomic Information System (ITIS) - 68377 names
• Plazi.org taxonomic treatments database - 61346 names
• Genome Taxonomy Database r207 - 60545 names
• International Plant Names Index - 52329 names
• Fauna Europaea - 45077 names
• The National Checklist of Taiwan (Catalogue of Life in Taiwan, TaiCoL) - 36193 names
• Dyntaxa. Svensk taxonomisk databas - 35892 names
• The Plant List with literature - 32692 names
• United Kingdom Species Inventory (UKSI) - 29643 names
• Artsnavnebasen - 29208 names
• The IUCN Red List of Threatened Species - 21221 names
• Afromoths, online database of Afrotropical moth species (Lepidoptera) - 13961 names
• Brazilian Flora 2020 project - Projeto Flora do Brasil 2020 - 13829 names
• Prokaryotic Nomenclature Up-to-Date (PNU) - 10079 names
• Checklist Dutch Species Register - Nederlands Soortenregister - 8814 names
• ICTV Master Species List (MSL) - 7852 names
• Cockroach Species File - 6020 names
• GRIN Taxonomy - 5882 names
• Taxon list of fungi and fungal-like organisms from Germany compiled by the DGfM - 4570 names
• Catalogue of Afrotropical Bees - 3623 names
• Catalogue of Tenebrionidae (Coleoptera) of North America - 3327 names
• Checklist of Beetles (Coleoptera) of Canada and Alaska. Second Edition. - 3312 names
• Systema Dipterorum - 2850 names
• Catalogue of the Pterophoroidea of the World - 2807 names
• The Clements Checklist - 2675 names
• Taxon list of Hymenoptera from Germany compiled in the context of the GBOL project - 2496 names
• IOC World Bird List, v13.2 - 2366 names
• Official Lists and Indexes of Names in Zoology - 2310 names
• National checklist of all species occurring in Denmark - 1922 names
• Myriatrix - 1876 names
• Database of Vascular Plants of Canada (VASCAN) - 1822 names
• Taxon list of vascular plants from Bavaria, Germany compiled in the context of the BFL project - 1771 names
• Orthoptera Species File - 1742 names
• A list of the terrestrial fungi, flora and fauna of Madeira and Selvagens archipelagos - 1602 names
• Aphid Species File - 1565 names
• World Spider Catalog - 1561 names
• Taxon list of Jurassic Pisces of the Tethys Palaeo-Environment compiled at the SNSB-JME - 1270 names
• Backbone Family Classification Patch - 1143 names
• GBIF Algae Classification - 1100 names
• International Cichorieae Network (ICN): Cichorieae Portal - 975 names
• Psocodea Species File - 803 names
• New Zealand Marine Macroalgae Species Checklist - 787 names
• Annotated checklist of endemic species from the Western Balkans - 754 names
• Taxon list of animals with German names (worldwide) compiled at the SMNS - 503 names
• Catalogue of the Alucitoidea of the World - 472 names
• Lygaeoidea Species File - 462 names
• Catálogo de Plantas y Líquenes de Colombia - 422 names
• GBIF Backbone Patch - 317 names
• Phasmida Species File - 259 names
• Cortinariaceae fetched from the Index Fungorum API - 234 names
• Coreoidea Species File - 233 names
• GTDB supplement - 139 names
• Mantodea Species File - 119 names
• Endemic species in Taiwan - 93 names
• Taxon list of Araneae from Germany compiled in the context of the GBOL project - 88 names
• Species of Hominidae - 78 names
• Taxon list of Sternorrhyncha from Germany compiled in the context of the GBOL project - 77 names
• Taxon list of mosses from Germany compiled in the context of the GBOL project - 75 names
• Mammal Species of the World - 73 names
• Plecoptera Species File - 71 names
• Species Fungorum Plus - 64 names
• Catalogue of the type specimens of Cosmopterigidae (Lepidoptera: Gelechioidea) from research collections of the Zoological Institute, Russian Academy of Sciences - 47 names
• Species named after famous people - 41 names
• Dermaptera Species File - 36 names
• Taxon list of Trichoptera from Germany compiled in the context of the GBOL project - 34 names
• True Fruit Flies (Diptera, Tephritidae) of the Afrotropical Region - 33 names
• Range and Regularities in the Distribution of Earthworms of the Earthworms of the USSR Fauna. Perel, 1979 - 32 names
• Taxon list of Diplura from Germany compiled in the context of the GBOL project - 30 names
• Lista de referencia de especies de aves de Colombia - 2022 - 24 names
• Taxon list of Auchenorrhyncha from Germany compiled in the context of the GBOL project - 20 names
• Catalogue of the type specimens of Polycestinae (Coleoptera: Buprestidae) from research collections of the Zoological Institute, Russian Academy of Sciences - 19 names
• Taxon list of Thysanoptera from Germany compiled in the context of the GBOL project - 19 names
• Lista de especies de vertebrados registrados en jurisdicción del Departamento del Huila - 18 names
• Taxon list of Microcoryphia (Archaeognatha) from Germany compiled in the context of the GBOL project - 15 names
• Catalogue of the type specimens of Bufonidae and Megophryidae (Amphibia: Anura) from research collections of the Zoological Institute, Russian Academy of Sciences - 12 names
• Grylloblattodea Species File - 11 names
• Coleorrhyncha Species File - 9 names
• Taxon list of liverworts from Germany compiled in the context of the GBOL project - 9 names
• Embioptera Species File - 7 names
• Taxon list of Pisces and Cyclostoma from Germany compiled in the context of the GBOL project - 6 names
• Taxon list of Pteridophyta from Germany compiled in the context of the GBOL project - 6 names
• Taxon list of Siphonaptera from Germany compiled in the context of the GBOL project - 5 names
• The Earthworms of the Fauna of Russia. Perel, 1997 - 5 names
• Taxon list of Zygentoma from Germany compiled in the context of the GBOL project - 4 names
• Asiloid Flies: new taxa of Diptera: Apioceridae, Asilidae, and Mydidae - 3 names
• Taxon list of Protura from Germany compiled in the context of the GBOL project - 3 names
• Taxon list of hornworts from Germany compiled in the context of the GBOL project - 2 names
• Chrysididae Species File - 1 names
• Taxon list of Dermaptera from Germany compiled in the context of the GBOL project - 1 names
• Taxon list of Diplopoda from Germany in the context of the GBOL project - 1 names
• Taxon list of Orthoptera (Grashoppers) from Germany compiled at the SNSB - 1 names
• Taxon list of Pscoptera from Germany compiled in the context of the GBOL project - 1 names
• Taxon list of Pseudoscorpiones from Germany compiled in the context of the GBOL project - 1 names
• Taxon list of Raphidioptera from Germany compiled in the context of the GBOL project - 1 names

Abstract

Since 2021, the Listening to the Kyrgyz Republic (L2KGZ) has been implemented by the World Bank with the support of the UK Government (FCDO) under the Effective Governance for Economic Development project. Around 1,500 households across the country regularly participate in the phone survey that is carried out every month. The L2KGZ collects information on public’s perception of overall socio-economic conditions and policy reforms, migration, employment, access to public service, household income, savings, food security, and coping mechanisms. That helps to inform the Government’s crucial decision-making for delivering essential social and economic reforms, implementing poverty reduction initiatives, and improving the well-being of citizens.

Geographic coverage

National, urban and rural

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the L2KGZ is a subsample of the 2021 baseline survey. The 2021 baseline survey was conducted face-to-face interviews with a nationally and urban/rural representative sample of 4,000 households from 200 PSU by random walk method. The PSU for the L2KGZ baseline survey were villages (ayyils) for rural areas, as well as streets for urban areas, which was the lowest-level of administrative unit in Kyrgyz Republic.

The sample was distributed nationally in the Kyrgyz Republic using a standard 2-stage approach wherein stratification ensured sufficient sample distribution by region and rural/urban areas. Clusters were randomly selected proportionate to size, and households randomly selected within each cluster.

The sampling plan of L2KGZ is represented at the national, regional (oblast), and urban/rural levels and based on official population data prepared and published by the National Statistical Committee of the Kyrgyz Republic (NSC). The sample was grouped into primary sampling units (PSUs), which are geographical areas - region and rural/urban areas. The consulting firm conducted 20 interviews in each PSU. Overall, the 200 PSUs sampled were divided according to the population size of each region, urban and rural areas. Then within each urban and rural area, each PSU was randomly selected with a probability proportional to size (PPS). In the baseline, households were asked to provide phone number of their most knowledgeable member for future contact during short phone interviews (CATI - Computer Assisted Telephone Interview).

After completion of the 2021 baseline survey, L2KGZ monthly interviewers began regularly calling a randomly selected panel of 1,500 households over the phone to conduct short interviews, following a set monthly schedule agreed to by the participating household. Selection was conducted on the basis of the design for the baseline survey: either 5 or 6 households were selected in each PSU. This design ensured that the geographic spread of participants remained as dispersed as in the baseline survey, and minimized the intracluster correlation of respondent characteristics to the best extent possible.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the Zerkalo with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

This dataset provides values for PERSONAL SAVINGS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The largest reported data leakage as of January 2025 was the Cam4 data breach in March 2020, which exposed more than 10 billion data records. The second-largest data breach in history so far, the Yahoo data breach, occurred in 2013. The company initially reported about one billion exposed data records, but after an investigation, the company updated the number, revealing that three billion accounts were affected. The National Public Data Breach was announced in August 2024. The incident became public when personally identifiable information of individuals became available for sale on the dark web. Overall, the security professionals estimate the leakage of nearly three billion personal records. The next significant data leakage was the March 2018 security breach of India's national ID database, Aadhaar, with over 1.1 billion records exposed. This included biometric information such as identification numbers and fingerprint scans, which could be used to open bank accounts and receive financial aid, among other government services.

Cybercrime - the dark side of digitalization As the world continues its journey into the digital age, corporations and governments across the globe have been increasing their reliance on technology to collect, analyze and store personal data. This, in turn, has led to a rise in the number of cyber crimes, ranging from minor breaches to global-scale attacks impacting billions of users – such as in the case of Yahoo. Within the U.S. alone, 1802 cases of data compromise were reported in 2022. This was a marked increase from the 447 cases reported a decade prior. The high price of data protection As of 2022, the average cost of a single data breach across all industries worldwide stood at around 4.35 million U.S. dollars. This was found to be most costly in the healthcare sector, with each leak reported to have cost the affected party a hefty 10.1 million U.S. dollars. The financial segment followed closely behind. Here, each breach resulted in a loss of approximately 6 million U.S. dollars - 1.5 million more than the global average.