100+ datasets found
  1. Amount of data created, consumed, and stored 2010-2023, with forecasts to...

    • statista.com
    Updated Jun 30, 2025
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    Statista (2025). Amount of data created, consumed, and stored 2010-2023, with forecasts to 2028 [Dataset]. https://www.statista.com/statistics/871513/worldwide-data-created/
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    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 2024
    Area covered
    Worldwide
    Description

    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.

  2. Coca-Cola Complete updated stocks Dataset

    • kaggle.com
    Updated Mar 15, 2025
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    M Atif Latif (2025). Coca-Cola Complete updated stocks Dataset [Dataset]. https://www.kaggle.com/datasets/matiflatif/coca-cola-complete-stocks-dataweekly-updated
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 15, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    M Atif Latif
    License

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

    Description

    Coca-Cola (KO) Stock Price Data (1919-2025).

    Overview:

    This dataset provides historical stock price data for The Coca-Cola Company (NYSE: KO) from September 6, 1919, to January 31, 2025. Extracted from Yahoo Finance, this dataset is valuable for stock market analysis, long-term trend evaluation, and financial modeling.

    Dataset Features:

    Date: The trading date in YYYY-MM-DD format.

    Open: Opening price of Coca-Cola stock on the respective day.

    High: Highest price recorded during the trading session.

    Low: Lowest price recorded during the trading session.

    Close: Closing price of the stock at the end of the trading session.

    Adj Close: Adjusted closing price, accounting for stock splits and dividends.

    Volume: Total number of shares traded on that day.

    Dataset Statistics:

    Total Records: 15,877 rows.

    Number of Features: 7 columns.

    Time Span: Over 100 years of stock data (1919-2025)

    Potential Use Cases:

    Long-Term Market Trend Analysis – Analyze Coca-Cola’s stock performance over a century. Financial Forecasting – Train machine learning models to predict future stock prices. Volatility Analysis – Assess price fluctuations over different market cycles. Investment Strategy Development – Backtest various trading strategies.

    Data Source:

    This dataset has been extracted from Yahoo Finance.

    License & Citation:

    This dataset is publicly available for educational and research purposes. Please cite Yahoo Finance and Muhammad Atif Latif when using it in any analysis.

    More Stocks Datasets.

    Click here for more Datasets

  3. N

    Lake County, OH Population Pyramid Dataset: Age Groups, Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Lake County, OH Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lake-county-oh-population-by-age/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 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
    Lake County, Ohio
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
    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 measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the data for the Lake County, OH population pyramid, which represents the Lake County population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

    Key observations

    • Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Lake County, OH, is 24.9.
    • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Lake County, OH, is 33.3.
    • Total dependency ratio for Lake County, OH is 58.2.
    • Potential support ratio, which is the number of youth (working age population) per elderly, for Lake County, OH is 3.0.
    Content

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

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group for the Lake County population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Lake County for the selected age group is shown in the following column.
    • Population (Female): The female population in the Lake County for the selected age group is shown in the following column.
    • Total Population: The total population of the Lake County for the selected age group is shown in the following column.

    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 Lake County Population by Age. You can refer the same here

  4. Vital Signs: Life Expectancy – by ZIP Code

    • data.bayareametro.gov
    csv, xlsx, xml
    Updated Apr 12, 2017
    + more versions
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    State of California, Department of Health: Death Records (2017). Vital Signs: Life Expectancy – by ZIP Code [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Life-Expectancy-by-ZIP-Code/xym8-u3kc
    Explore at:
    csv, xlsx, xmlAvailable download formats
    Dataset updated
    Apr 12, 2017
    Dataset provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    Authors
    State of California, Department of Health: Death Records
    Description

    VITAL SIGNS INDICATOR Life Expectancy (EQ6)

    FULL MEASURE NAME Life Expectancy

    LAST UPDATED April 2017

    DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

    DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

    California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

    U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

    U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

    CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

    METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

    Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

    For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

    ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

  5. N

    Knox County, OH Population Pyramid Dataset: Age Groups, Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Knox County, OH Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/knox-county-oh-population-by-age/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 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
    Knox County, Ohio
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
    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 measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the data for the Knox County, OH population pyramid, which represents the Knox County population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

    Key observations

    • Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Knox County, OH, is 30.5.
    • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Knox County, OH, is 30.0.
    • Total dependency ratio for Knox County, OH is 60.5.
    • Potential support ratio, which is the number of youth (working age population) per elderly, for Knox County, OH is 3.3.
    Content

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

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group for the Knox County population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Knox County for the selected age group is shown in the following column.
    • Population (Female): The female population in the Knox County for the selected age group is shown in the following column.
    • Total Population: The total population of the Knox County for the selected age group is shown in the following column.

    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 Knox County Population by Age. You can refer the same here

  6. d

    COVID-19 case rate per 100,000 population and percent test positivity in the...

    • catalog.data.gov
    • data.ct.gov
    • +1more
    Updated Aug 12, 2023
    + more versions
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    data.ct.gov (2023). COVID-19 case rate per 100,000 population and percent test positivity in the last 7 days by town - ARCHIVE [Dataset]. https://catalog.data.gov/dataset/covid-19-case-rate-per-100000-population-and-percent-test-positivity-in-the-last-7-days-by
    Explore at:
    Dataset updated
    Aug 12, 2023
    Dataset provided by
    data.ct.gov
    Description

    DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county). This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity). A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case. These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities. These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020. Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

  7. Vital Signs: Life Expectancy – Bay Area

    • data.bayareametro.gov
    csv, xlsx, xml
    Updated Apr 7, 2017
    Share
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    State of California, Department of Health: Death Records (2017). Vital Signs: Life Expectancy – Bay Area [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Life-Expectancy-Bay-Area/emjt-svg9
    Explore at:
    xlsx, xml, csvAvailable download formats
    Dataset updated
    Apr 7, 2017
    Dataset provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    Authors
    State of California, Department of Health: Death Records
    Area covered
    San Francisco Bay Area
    Description

    VITAL SIGNS INDICATOR Life Expectancy (EQ6)

    FULL MEASURE NAME Life Expectancy

    LAST UPDATED April 2017

    DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

    DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

    California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

    CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

    METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

    Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

    Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

  8. d

    COVID-19 Vaccination Coverage, Citywide

    • catalog.data.gov
    • data.cityofchicago.org
    • +1more
    Updated Sep 20, 2025
    + more versions
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    data.cityofchicago.org (2025). COVID-19 Vaccination Coverage, Citywide [Dataset]. https://catalog.data.gov/dataset/covid-19-vaccination-coverage-citywide
    Explore at:
    Dataset updated
    Sep 20, 2025
    Dataset provided by
    data.cityofchicago.org
    Description

    NOTE: This dataset replaces two previous ones. Please see below. Chicago residents who are up to date with COVID-19 vaccines, based on the reported address, race-ethnicity, sex, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). “Up to date” refers to individuals who meet the CDC’s updated COVID-19 vaccination criteria based on their age and prior vaccination history. For surveillance purposes, up to date is defined based on the following criteria: People ages 5 years and older: · Are up to date when they receive 1+ doses of a COVID-19 vaccine during the current season. Children ages 6 months to 4 years: · Children who have received at least two prior COVID-19 vaccine doses are up to date when they receive one additional dose of COVID-19 vaccine during the current season, regardless of vaccine product. · Children who have received only one prior COVID-19 vaccine dose are up to date when they receive one additional dose of the current season's Moderna COVID-19 vaccine or two additional doses of the current season's Pfizer-BioNTech COVID-19 vaccine. · Children who have never received a COVID-19 vaccination are up to date when they receive either two doses of the current season's Moderna vaccine or three doses of the current season's Pfizer-BioNTech vaccine. This dataset takes the place of two previous datasets, which cover doses administered from December 15, 2020 through September 13, 2023 and are marked has historical: - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Vaccinations-Chicago-Residents/2vhs-cf6b - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccinations-by-Age-and-Race-Ethnicity/37ac-bbe3. Data Notes: Weekly cumulative totals of people up to date are shown for each combination of race-ethnicity, sex, and age group. Note that race-ethnicity, age, and sex all have an option for “All” so care should be taken when summing rows. Coverage percentages are calculated based on the cumulative number of people in each race-ethnicity/age/sex population subgroup who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller demographic groupings with smaller populations. Additionally, the medical provider may report incorrect demographic information for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage. All coverage percentages are capped at 99%. Weekly cumulative counts and coverage percentages are reported from the week ending Saturday, September 16, 2023 onward through the Saturday prior to the dataset being updated. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. The Chicago Department of Public Health uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Individuals may receive vaccinations that are not recorded in the Illinois immunization registry, I-CARE, such as those administered in another state, causing underestimation of the number individuals who are up to date. Inconsistencies in records of separate doses administered to the same person, such as slight variations in dates of birth, can result in duplicate records for a person and underestimate the number of people who are up to date.

  9. Z

    LAU1 dataset

    • data.niaid.nih.gov
    • zenodo.org
    Updated Nov 29, 2024
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    Páleník, Michal (2024). LAU1 dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6165135
    Explore at:
    Dataset updated
    Nov 29, 2024
    Dataset authored and provided by
    Páleník, Michal
    License

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

    Description

    Statistical open data on LAU regions of Slovakia, Czech Republic, Poland, Hungary (and other countries in the future). LAU1 regions are called counties, okres, okresy, powiat, járás, járási, NUTS4, LAU, Local Administrative Units, ... and there are 733 of them in this V4 dataset. Overall, we cover 733 regions which are described by 137.828 observations (panel data rows) and more than 1.760.229 data points.

    This LAU dataset contains panel data on population, on age structure of inhabitants, on number and on structure of registered unemployed. Dataset prepared by Michal Páleník. Output files are in json, shapefiles, xls, ods, json, topojson or CSV formats. Downloadable at zenodo.org.

    This dataset consists of:

    data on unemployment (by gender, education and duration of unemployment),

    data on vacancies,

    open data on population in Visegrad counties (by age and gender),

    data on unemployment share.

    Combined latest dataset

    dataset of the latest available data on unemployment, vacancies and population

    dataset includes map contours (shp, topojson or geojson format), relation id in OpenStreetMap, wikidata entry code,

    it also includes NUTS4 code, LAU1 code used by national statistical office and abbreviation of the region (usually license plate),

    source of map contours is OpenStreetMap, licensed under ODbL

    no time series, only most recent data on population and unemployment combined in one output file

    columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies, pop_period, TOTAL, Y15-64, Y15-64-females, local_lau, osm_id, abbr, wikidata, population_density, area_square_km, way

    Slovakia – SK: 79 LAU1 regions, data for 2024-10-01, 1.659 data,

    Czech Republic – CZ: 77 LAU1 regions, data for 2024-10-01, 1.617 data,

    Poland – PL: 380 LAU1 regions, data for 2024-09-01, 6.840 data,

    Hungary – HU: 197 LAU1 regions, data for 2024-10-01, 2.955 data,

    13.071 data in total.

    column/number of observations description SK CZ PL HU

    period period (month and year) the data is for 79 77 380 197

    lau LAU code of the region 79 77 380 197

    name name of the region in local language 79 77 380 197

    registered_unemployed number of unemployed registered at labour offices 79 77 380 197

    registered_unemployed_females number of unemployed women 79 77 380 197

    disponible_unemployed unemployed able to accept job offer 79 77 0 0

    low_educated unmployed without secondary school (ISCED 0 and 1) 79 77 380 197

    long_term unemployed for longer than 1 year 79 77 380 0

    unemployment_inflow inflow into unemployment 79 77 0 0

    unemployment_outflow outflow from unemployment 79 77 0 0

    below_25 number of unemployed below 25 years of age 79 77 380 197

    over_55 unemployed older than 55 years 79 77 380 197

    vacancies number of vacancies reported by labour offices 79 77 380 0

    pop_period date of population data 79 77 380 197

    TOTAL total population 79 77 380 197

    Y15-64 number of people between 15 and 64 years of age, population in economically active age 79 77 380 197

    Y15-64-females number of women between 15 and 64 years of age 79 77 380 197

    local_lau region's code used by local labour offices 79 77 380 197

    osm_id relation id in OpenStreetMap database 79 77 380 197

    abbr abbreviation used for this region 79 77 380 0

    wikidata wikidata identification code 79 77 380 197

    population_density population density 79 77 380 197

    area_square_km area of the region in square kilometres 79 77 380 197

    way geometry, polygon of given region 79 77 380 197

    Unemployment dataset

    time series of unemployment data in Visegrad regions

    by gender, duration of unemployment, education level, age groups, vacancies,

    columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies

    Slovakia – SK: 79 LAU1 regions, data for 334 periods (1997-01-01 ... 2024-10-01), 202.082 data,

    Czech Republic – CZ: 77 LAU1 regions, data for 244 periods (2004-07-01 ... 2024-10-01), 147.528 data,

    Poland – PL: 380 LAU1 regions, data for 189 periods (2005-03-01 ... 2024-09-01), 314.100 data,

    Hungary – HU: 197 LAU1 regions, data for 106 periods (2016-01-01 ... 2024-10-01), 104.408 data,

    768.118 data in total.

    column/number of observations description SK CZ PL HU

    period period (month and year) the data is for 26 386 18 788 71 772 20 882

    lau LAU code of the region 26 386 18 788 71 772 20 882

    name name of the region in local language 26 386 18 788 71 772 20 882

    registered_unemployed number of unemployed registered at labour offices 26 386 18 788 71 772 20 882

    registered_unemployed_females number of unemployed women 26 386 18 788 62 676 20 882

    disponible_unemployed unemployed able to accept job offer 25 438 18 788 0 0

    low_educated unmployed without secondary school (ISCED 0 and 1) 11 771 9855 41 388 20 881

    long_term unemployed for longer than 1 year 24 253 9855 41 388 0

    unemployment_inflow inflow into unemployment 26 149 16 478 0 0

    unemployment_outflow outflow from unemployment 26 149 16 478 0 0

    below_25 number of unemployed below 25 years of age 11 929 9855 17 100 20 881

    over_55 unemployed older than 55 years 11 929 9855 17 100 20 882

    vacancies number of vacancies reported by labour offices 11 692 18 788 62 676 0

    Population dataset

    time series on population by gender and 5 year age groups in V4 counties

    columns: period, lau, name, gender, TOTAL, Y00-04, Y05-09, Y10-14, Y15-19, Y20-24, Y25-29, Y30-34, Y35-39, Y40-44, Y45-49, Y50-54, Y55-59, Y60-64, Y65-69, Y70-74, Y75-79, Y80-84, Y85-89, Y90-94, Y_GE95, Y15-64

    Slovakia – SK: 79 LAU1 regions, data for 28 periods (1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 152.628 data,

    Czech Republic – CZ: 78 LAU1 regions, data for 24 periods (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 125.862 data,

    Poland – PL: 382 LAU1 regions, data for 29 periods (1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 626.941 data,

    Hungary – HU: 197 LAU1 regions, data for 11 periods (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 86.680 data,

    992.111 data in total.

    column/number of observations description SK CZ PL HU

    period period (month and year) the data is for 6636 5574 32 883 4334

    lau LAU code of the region 6636 5574 32 883 4334

    name name of the region in local language 6636 5574 32 883 4334

    gender gender (male or female) 6636 5574 32 883 4334

    TOTAL total population 6636 5574 32 503 4334

    Y00-04 inhabitants between 00 to 04 years inclusive 6636 5574 32 503 4334

    Y05-09 number of inhabitants between 05 to 09 years of age 6636 5574 32 503 4334

    Y10-14 number of people between 10 to 14 years inclusive 6636 5574 32 503 4334

    Y15-19 number of inhabitants between 15 to 19 years of age 6636 5574 32 503 4334

    Y20-24 number of people between 20 to 24 years inclusive 6636 5574 32 503 4334

    Y25-29 number of inhabitants between 25 to 29 years of age 6636 5574 32 503 4334

    Y30-34 inhabitants between 30 to 34 years inclusive 6636 5574 32 503 4334

    Y35-39 number of inhabitants between 35 to 39 years of age 6636 5574 32 503 4334

    Y40-44 inhabitants between 40 to 44 years inclusive 6636 5574 32 503 4334

    Y45-49 number of inhabitants younger than 49 and older than 45 years 6636 5574 32 503 4334

    Y50-54 inhabitants between 50 to 54 years inclusive 6636 5574 32 503 4334

    Y55-59 number of inhabitants between 55 to 59 years of age 6636 5574 32 503 4334

    Y60-64 inhabitants between 60 to 64 years inclusive 6636 5574 32 503 4334

    Y65-69 number of inhabitants younger than 69 and older than 65 years 6636 5574 32 503 4334

    Y70-74 inhabitants between 70 to 74 years inclusive 6636 5574 24 670 4334

    Y75-79 number of inhabitants between 75 to 79 years of age 6636 5574 24 670 4334

    Y80-84 number of people between 80 to 84 years inclusive 6636 5574 24 670 4334

    Y85-89 number of inhabitants younger than 89 and older than 85 years 6636 5574 0 0

    Y90-94 inhabitants between 90 to 94 years inclusive 6636 5574 0 0

    Y_GE95 number of people 95 years or older 6636 3234 0 0

    Y15-64 number of people between 15 and 64 years of age, population in economically active age 6636 5574 32 503 4334

    Notes

    more examples at www.iz.sk

    NUTS4 / LAU1 / LAU codes for HU and PL are created by me, so they can (and will) change in the future; CZ and SK NUTS4 codes are used by local statistical offices, so they should be more stable

    NUTS4 codes are consistent with NUTS3 codes used by Eurostat

    local_lau variable is an identifier used by local statistical office

    abbr is abbreviation of region's name, used for map purposes (usually cars' license plate code; except for Hungary)

    wikidata is code used by wikidata

    osm_id is region's relation number in the OpenStreetMap database

    Example outputs

    you can download data in CSV, xml, ods, xlsx, shp, SQL, postgis, topojson, geojson or json format at 📥 doi:10.5281/zenodo.6165135

    Counties of Slovakia – unemployment rate in Slovak LAU1 regions

    Regions of the Slovak Republic

    Unemployment of Czechia and Slovakia – unemployment share in LAU1 regions of Slovakia and Czechia

    interactive map on unemployment in Slovakia

    Slovakia – SK, Czech Republic – CZ, Hungary – HU, Poland – PL, NUTS3 regions of Slovakia

    download at 📥 doi:10.5281/zenodo.6165135

    suggested citation: Páleník, M. (2024). LAU1 dataset [Data set]. IZ Bratislava. https://doi.org/10.5281/zenodo.6165135

  10. g

    Bureau of Justice; Public Performance Project, Prison Population by State,...

    • geocommons.com
    Updated May 7, 2008
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    data (2008). Bureau of Justice; Public Performance Project, Prison Population by State, USA, 2007 and 2008 [Dataset]. http://geocommons.com/search.html
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    Dataset updated
    May 7, 2008
    Dataset provided by
    Bureau of Justice; Public Performance Project
    data
    Description

    This dataset shows the number of people that are in prison by state in 2006 and 2007. These numbers are then compared to show the difference between the two years and a percentage of change is given as well. This data was brought to our attention by the Pew Charitable Trusts in their report titled, One in 100: Behind Bars in America 2008."" The main emphasis of the article emphasizes the point that in 2007 1 in every 100 Americans were in prison. To note: Many states have not completed their data verification process. Final published figures may differ slightly. The District of Columbia is not included. D.C. prisoners were transferred to federal custody in 2001

  11. Russian Demography Data (1990-2017)

    • kaggle.com
    Updated Jan 30, 2020
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    Maksim Zhdanov (2020). Russian Demography Data (1990-2017) [Dataset]. https://www.kaggle.com/dwdkills/russian-demography/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 30, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Maksim Zhdanov
    License

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

    Area covered
    Russia
    Description

    Context

    You found Russian Demography (1990-2017) Dataset. It contains demographic features like natural population growth, birth rate, urbanization, etc. Data was collected from various Internet resources.

    Content

    Dataset has 2380 rows and 7 columns. Keys for columns:

    • "year" - year (1990-2017)
    • "region" - name of a federal subject of Russia. It could be oblast, republic, krai, autonomous okrug, federal city and a single autonomous oblast
    • "npg" - natural population growth by 1000 people. Calculating as the difference between birth rate and death rate
    • "birth_rate" - number of births by 1000 people
    • "death_rate" - number of deaths by 1000 people
    • "gdw" - general demographic weight (how many people of non-woking age account for 100 people of working age). Working age for men 16-60 years, for women 16-55 years
    • "urbanization" - % of urban population

    Acknowledgements

    ЕМИСС (UIISS) - Unified interdepartmental information and statistical system

    Inspiration

    You can analyze the relationships between various years, find best regions by each feature and compare them.

  12. USA Name Data

    • kaggle.com
    zip
    Updated Feb 12, 2019
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    Data.gov (2019). USA Name Data [Dataset]. https://www.kaggle.com/datasets/datagov/usa-names
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    zip(0 bytes)Available download formats
    Dataset updated
    Feb 12, 2019
    Dataset provided by
    Data.govhttps://data.gov/
    License

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

    Area covered
    United States
    Description

    Context

    Cultural diversity in the U.S. has led to great variations in names and naming traditions and names have been used to express creativity, personality, cultural identity, and values. Source: https://en.wikipedia.org/wiki/Naming_in_the_United_States

    Content

    This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in this data. For others who did apply, records may not show the place of birth, and again their names are not included in the data.

    All data are from a 100% sample of records on Social Security card applications as of the end of February 2015. To safeguard privacy, the Social Security Administration restricts names to those with at least 5 occurrences.

    Fork this kernel to get started with this dataset.

    Acknowledgements

    https://bigquery.cloud.google.com/dataset/bigquery-public-data:usa_names

    https://cloud.google.com/bigquery/public-data/usa-names

    Dataset Source: Data.gov. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Banner Photo by @dcp from Unplash.

    Inspiration

    What are the most common names?

    What are the most common female names?

    Are there more female or male names?

    Female names by a wide margin?

  13. d

    COVID-19 - Vaccinations by Region, Age, and Race-Ethnicity - Historical

    • catalog.data.gov
    • healthdata.gov
    • +2more
    Updated Dec 16, 2023
    + more versions
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    data.cityofchicago.org (2023). COVID-19 - Vaccinations by Region, Age, and Race-Ethnicity - Historical [Dataset]. https://catalog.data.gov/dataset/covid-19-vaccinations-by-region-age-and-race-ethnicity
    Explore at:
    Dataset updated
    Dec 16, 2023
    Dataset provided by
    data.cityofchicago.org
    Description

    NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Region-HCEZ-/5sc6-ey97. COVID-19 vaccinations administered to Chicago residents by Healthy Chicago Equity Zones (HCEZ) based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f Vaccination Status Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" so care should be taken when summing rows. Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated. Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-year estimates. Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who have each vaccination status as of the date, divided by the estimated number of people in that subgroup. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact its estimates. Data reported in I-CARE only includes doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in another state, the number of people with a completed series or a booster dose is underesti

  14. National population projections table of contents

    • ons.gov.uk
    • cy.ons.gov.uk
    xls
    Updated Aug 7, 2025
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    Office for National Statistics (2025). National population projections table of contents [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/datasets/2014basednationalpopulationprojectionstableofcontents
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    xlsAvailable download formats
    Dataset updated
    Aug 7, 2025
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Tools to locate the dataset tables and supporting documentation for the 2014, 2016, 2018, 2020, 2021 and 2022-based national population projections. Contains links to the principal and (where available) variant projections for the UK and constituent countries for 100 years ahead.

  15. Relationship of those aged 100 years and over (centenarians) to household...

    • ons.gov.uk
    xlsx
    Updated Sep 18, 2023
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    Office for National Statistics (2023). Relationship of those aged 100 years and over (centenarians) to household members, England and Wales [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/ageing/datasets/relationshipofthoseaged100yearsandovercentenarianstohouseholdmembersenglandandwales
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Sep 18, 2023
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    Wales
    Description

    This dataset provides Census 2021 estimates that classify usual residents aged 100 years and over living with others in a private household in England and Wales by relationship. The estimates are as at Census Day, 21 March 2021.

  16. COVID-19 Vaccine Progress Dashboard Data by ZIP Code

    • data.chhs.ca.gov
    • data.ca.gov
    • +1more
    csv, xlsx, zip
    Updated Sep 24, 2025
    + more versions
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    California Department of Public Health (2025). COVID-19 Vaccine Progress Dashboard Data by ZIP Code [Dataset]. https://data.chhs.ca.gov/dataset/covid-19-vaccine-progress-dashboard-data-by-zip-code
    Explore at:
    csv(27663424), csv(9320174), xlsx(7800), csv(5478164), csv(21567128), zip, xlsx(10933)Available download formats
    Dataset updated
    Sep 24, 2025
    Dataset authored and provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    Description

    Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

    Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables.

    Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

    This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

    This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

    This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

    The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

    These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

    For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

  17. n

    AirNow Air Quality Monitoring Data (Current) - Dataset - CKAN

    • nationaldataplatform.org
    Updated Feb 28, 2024
    + more versions
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    (2024). AirNow Air Quality Monitoring Data (Current) - Dataset - CKAN [Dataset]. https://nationaldataplatform.org/catalog/dataset/airnow-air-quality-monitoring-data-current
    Explore at:
    Dataset updated
    Feb 28, 2024
    Description

    This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

  18. N

    Exeter, PA Population Pyramid Dataset: Age Groups, Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
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    Neilsberg Research (2025). Exeter, PA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/524b2bd7-f122-11ef-8c1b-3860777c1fe6/
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    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 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
    Exeter, Pennsylvania
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
    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 measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the data for the Exeter, PA population pyramid, which represents the Exeter population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

    Key observations

    • Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Exeter, PA, is 27.5.
    • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Exeter, PA, is 44.9.
    • Total dependency ratio for Exeter, PA is 72.4.
    • Potential support ratio, which is the number of youth (working age population) per elderly, for Exeter, PA is 2.2.
    Content

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

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group for the Exeter population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Exeter for the selected age group is shown in the following column.
    • Population (Female): The female population in the Exeter for the selected age group is shown in the following column.
    • Total Population: The total population of the Exeter for the selected age group is shown in the following column.

    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 Exeter Population by Age. You can refer the same here

  19. Life expectancy at various ages, by population group and sex, Canada

    • www150.statcan.gc.ca
    • datasets.ai
    • +2more
    Updated Dec 17, 2015
    + more versions
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    Government of Canada, Statistics Canada (2015). Life expectancy at various ages, by population group and sex, Canada [Dataset]. http://doi.org/10.25318/1310013401-eng
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    Dataset updated
    Dec 17, 2015
    Dataset provided by
    Government of Canadahttp://www.gg.ca/
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

  20. 'Climate Just' data - Dataset - data.gov.uk

    • ckan.publishing.service.gov.uk
    Updated Jun 9, 2025
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    ckan.publishing.service.gov.uk (2025). 'Climate Just' data - Dataset - data.gov.uk [Dataset]. https://ckan.publishing.service.gov.uk/dataset/climate-just-data
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    Dataset updated
    Jun 9, 2025
    Dataset provided by
    CKANhttps://ckan.org/
    Description

    The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale. The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage. Climate Just Map Tool includes maps on: Flooding (river/coastal and surface water) Heat Fuel poverty. The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data. Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls Indicators include: Climate Just-Flood disadvantage_2011_Dec2014.xlsx Fluvial flood disadvantage indexPluvial flood disadvantage index (1 in 30 years)Pluvial flood disadvantage index (1 in 100 years)Pluvial flood disadvantage index (1 in 1000 years) Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx Percentage of area at moderate and significant risk of fluvial floodingPercentage of area at risk of surface water flooding (1 in 30 years)Percentage of area at risk of surface water flooding (1 in 100 years)Percentage of area at risk of surface water flooding (1 in 1000 years) Climate Just-SSVI_indices_2011_Dec2014.xlsx Sensitivity - flood and heatAbility to prepare - floodAbility to respond - floodAbility to recover - floodEnhanced exposure - floodAbility to prepare - heatAbility to respond - heatAbility to recover - heatEnhanced exposure - heatSocio-spatial vulnerability index - floodSocio-spatial vulnerability index - heat Climate Just-SSVI_indicators_2011_Dec2014.xlsx % children < 5 years old% people > 75 years old% people with long term ill-health/disability (activities limited a little or a lot)% households with at least one person with long term ill-health/disability (activities limited a little or a lot)% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% households rented from social landlords% households rented from private landlords% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% people with % unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carCrime score (IMD)% area not roadDensity of retail units (count /km2)% change in number of local VAT-based units% people with % not home workers% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (Pounds)% all pensioner households% born outside UK and IrelandInsurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carTravel time to nearest GP by walk/public transport (mins - representative time)% of at risk population (no car) outside of 15 minutes by walk/public transport to nearest GP Number of GPs within 15 minutes by walk/public transport Number of GPs within 15 minutes by car Travel time to nearest hospital by walk/public transport (mins - representative time)Travel time to nearest hospital by car (mins - representative time)% of at risk population outside of 30 minutes by walk/PT to nearest hospitalNumber of hospitals within 30 minutes by walk/public transport Number of hospitals within 30 minutes by car % people with % not home workersChange in median house price 2004-09 (Pounds)% area not green space Area of domestic buildings per area of domestic gardens (m2 per m2)% area not blue spaceDistance to coast (m)Elevation (m)% households with the lowest floor level: Basement or semi-basement% households with the lowest floor level: ground floor% households with the lowest floor level: fifth floor or higher

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Statista (2025). Amount of data created, consumed, and stored 2010-2023, with forecasts to 2028 [Dataset]. https://www.statista.com/statistics/871513/worldwide-data-created/
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Amount of data created, consumed, and stored 2010-2023, with forecasts to 2028

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Dataset updated
Jun 30, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
May 2024
Area covered
Worldwide
Description

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.

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