14 datasets found
  1. N

    United States Age Group Population Dataset: A complete breakdown of United...

    • neilsberg.com
    csv, json
    Updated Sep 16, 2023
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    Neilsberg Research (2023). United States Age Group Population Dataset: A complete breakdown of United States age demographics from 0 to 85 years, distributed across 18 age groups [Dataset]. https://www.neilsberg.com/research/datasets/5fd2b2bb-3d85-11ee-9abe-0aa64bf2eeb2/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Sep 16, 2023
    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
    United States
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 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) 2017-2021 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the 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 United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

    Key observations

    The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 in consideration
    • Population: The population for the specific age group in the United States is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of United States 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 United States Population by Age. You can refer the same here

  2. 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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    Life expectancy at various ages, by population group and sex, Canada [Dataset]. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1310013401
    Explore at:
    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 ...).

  3. d

    Early Indicators of Later Work Levels Disease and Death (EI) - Union Army...

    • dknet.org
    • neuinfo.org
    • +3more
    Updated Oct 18, 2019
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    (2019). Early Indicators of Later Work Levels Disease and Death (EI) - Union Army Samples Public Health and Ecological Datasets [Dataset]. http://identifiers.org/RRID:SCR_008921
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    Dataset updated
    Oct 18, 2019
    Description

    A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

  4. d

    Demographics

    • catalog.data.gov
    • datasets.ai
    • +4more
    Updated Nov 22, 2024
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    Lake County Illinois GIS (2024). Demographics [Dataset]. https://catalog.data.gov/dataset/demographics-0be32
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    Dataset updated
    Nov 22, 2024
    Dataset provided by
    Lake County Illinois GIS
    Description

    Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

  5. FiveThirtyEight Police Locals Dataset

    • kaggle.com
    Updated Mar 26, 2019
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    FiveThirtyEight (2019). FiveThirtyEight Police Locals Dataset [Dataset]. https://www.kaggle.com/fivethirtyeight/fivethirtyeight-police-locals-dataset/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 26, 2019
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    FiveThirtyEight
    License

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

    Description

    Content

    Police Residence

    This folder contains data behind the story Most Police Don’t Live In The Cities They Serve.

    Includes the cities with the 75 largest police forces, with the exception of Honolulu for which data is not available. All calculations are based on data from the U.S. Census.

    The Census Bureau numbers are potentially going to differ from other counts for three reasons:

    1. The census category for police officers also includes sheriffs, transit police and others who might not be under the same jurisdiction as a city’s police department proper. The census category won’t include private security officers.
    2. The census data is estimated from 2006 to 2010; police forces may have changed in size since then.
    3. There is always a margin of error in census numbers; they are estimates, not complete counts.

    How to read police-locals.csv

    HeaderDefinition
    cityU.S. city
    police_force_sizeNumber of police officers serving that city
    allPercentage of the total police force that lives in the city
    whitePercentage of white (non-Hispanic) police officers who live in the city
    non-whitePercentage of non-white police officers who live in the city
    blackPercentage of black police officers who live in the city
    hispanicPercentage of Hispanic police officers who live in the city
    asianPercentage of Asian police officers who live in the city

    Note: When a cell contains ** it means that there are fewer than 100 police officers of that race serving that city.

    Context

    This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

    • Update Frequency: This dataset is updated daily.

    Acknowledgements

    This dataset is maintained using GitHub's API and Kaggle's API.

    This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

  6. c

    Poverty Status by County - Datasets - CTData.org

    • data.ctdata.org
    Updated Mar 16, 2016
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    (2016). Poverty Status by County - Datasets - CTData.org [Dataset]. http://data.ctdata.org/dataset/poverty-status-by-county
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    Dataset updated
    Mar 16, 2016
    License

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

    Description

    The Census Bureau determines that a person is living in poverty when his or her total household income compared with the size and composition of the household is below the poverty threshold. The Census Bureau uses the federal government's official definition of poverty to determine the poverty threshold. Beginning in 2000, individuals were presented with the option to select one or more races. In addition, the Census asked individuals to identify their race separately from identifying their Hispanic origin. The Census has published individual tables for the races and ethnicities provided as supplemental information to the main table that does not dissaggregate by race or ethnicity. Race categories include the following - White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, Some other race, and Two or more races. We are not including specific combinations of two or more races as the counts of these combinations are small. Ethnic categories include - Hispanic or Latino and White Non-Hispanic. This data comes from the American Community Survey (ACS) 5-Year estimates, table B17001. The ACS collects these data from a sample of households on a rolling monthly basis. ACS aggregates samples into one-, three-, or five-year periods. CTdata.org generally carries the five-year datasets, as they are considered to be the most accurate, especially for geographic areas that are the size of a county or smaller.Poverty status determined is the denominator for the poverty rate. It is the population for which poverty status was determined so when poverty is calculated they exclude institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years of age.Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, number of children, and age of householder.number of children, and age of householder.

  7. d

    Mass Killings in America, 2006 - present

    • data.world
    csv, zip
    Updated Jul 31, 2025
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    The Associated Press (2025). Mass Killings in America, 2006 - present [Dataset]. https://data.world/associatedpress/mass-killings-public
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    zip, csvAvailable download formats
    Dataset updated
    Jul 31, 2025
    Authors
    The Associated Press
    Time period covered
    Jan 1, 2006 - Jul 28, 2025
    Area covered
    Description

    THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON JULY 30

    OVERVIEW

    2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

    In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

    A total of 229 people died in mass killings in 2019.

    The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

    One-third of the offenders died at the scene of the killing or soon after, half from suicides.

    About this Dataset

    The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

    The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

    This data will be updated periodically and can be used as an ongoing resource to help cover these events.

    Using this Dataset

    To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

    Mass killings by year

    Mass shootings by year

    To get these counts just for your state:

    Filter killings by state

    Definition of "mass murder"

    Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

    This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

    Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

    Methodology

    Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

    Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

    In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

    Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

    Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

    This project started at USA TODAY in 2012.

    Contacts

    Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

  8. Find Ryan White HIV/AIDS Medical Care Providers

    • datasets.ai
    • healthdata.gov
    • +4more
    21
    Updated Sep 8, 2024
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    U.S. Department of Health & Human Services (2024). Find Ryan White HIV/AIDS Medical Care Providers [Dataset]. https://datasets.ai/datasets/find-ryan-white-hiv-aids-medical-care-providers
    Explore at:
    21Available download formats
    Dataset updated
    Sep 8, 2024
    Dataset provided by
    United States Department of Health and Human Serviceshttp://www.hhs.gov/
    Authors
    U.S. Department of Health & Human Services
    Description

    The Find Ryan White HIV/AIDS Medical Care Providers tool is a locator that helps people living with HIV/AIDS access medical care and related services. Users can search for Ryan White-funded medical care providers near a specific complete address, city and state, state and county, or ZIP code.

    Search results are sorted by distance away and include the Ryan White HIV/AIDS facility name, address, approximate distance from the search point, telephone number, website address, and a link for driving directions.

    HRSA's Ryan White program funds an array of grants at the state and local levels in areas where most needed. These grants provide medical and support services to more than a half million people who otherwise would be unable to afford care.

  9. FiveThirtyEight Hate Crimes Dataset

    • kaggle.com
    Updated Apr 26, 2019
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    FiveThirtyEight (2019). FiveThirtyEight Hate Crimes Dataset [Dataset]. https://www.kaggle.com/datasets/fivethirtyeight/fivethirtyeight-hate-crimes-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 26, 2019
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    FiveThirtyEight
    License

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

    Description

    Content

    Hate Crimes

    This folder contains data behind the story Higher Rates Of Hate Crimes Are Tied To Income Inequality.

    HeaderDefinition
    stateState name
    median_household_incomeMedian household income, 2016
    share_unemployed_seasonalShare of the population that is unemployed (seasonally adjusted), Sept. 2016
    share_population_in_metro_areasShare of the population that lives in metropolitan areas, 2015
    share_population_with_high_school_degreeShare of adults 25 and older with a high-school degree, 2009
    share_non_citizenShare of the population that are not U.S. citizens, 2015
    share_white_povertyShare of white residents who are living in poverty, 2015
    gini_indexGini Index, 2015
    share_non_whiteShare of the population that is not white, 2015
    share_voters_voted_trumpShare of 2016 U.S. presidential voters who voted for Donald Trump
    hate_crimes_per_100k_splcHate crimes per 100,000 population, Southern Poverty Law Center, Nov. 9-18, 2016
    avg_hatecrimes_per_100k_fbiAverage annual hate crimes per 100,000 population, FBI, 2010-2015

    Sources: Kaiser Family Foundation Kaiser Family Foundation Kaiser Family Foundation Census Bureau Kaiser Family Foundation Kaiser Family Foundation Census Bureau Kaiser Family Foundation United States Elections Project Southern Poverty Law Center FBI

    Correction

    Please see the following commit: https://github.com/fivethirtyeight/data/commit/fbc884a5c8d45a0636e1d6b000021632a0861986

    Context

    This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

    • Update Frequency: This dataset is updated daily.

    Acknowledgements

    This dataset is maintained using GitHub's API and Kaggle's API.

    This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

  10. ZMMU MSU, White Sea Branch

    • gbif.org
    Updated Jul 28, 2025
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    Nikolai Neretin; Anna Zhadan; Glafira Kolbasova; Tatiana Neretina; Alexey Kotenev; Alexander Tzetlin; Mikhail Kalyakin; Nikolai Neretin; Anna Zhadan; Glafira Kolbasova; Tatiana Neretina; Alexey Kotenev; Alexander Tzetlin; Mikhail Kalyakin (2025). ZMMU MSU, White Sea Branch [Dataset]. http://doi.org/10.15468/h3ee9d
    Explore at:
    Dataset updated
    Jul 28, 2025
    Dataset provided by
    Global Biodiversity Information Facilityhttps://www.gbif.org/
    Lomonosov Moscow State University
    Authors
    Nikolai Neretin; Anna Zhadan; Glafira Kolbasova; Tatiana Neretina; Alexey Kotenev; Alexander Tzetlin; Mikhail Kalyakin; Nikolai Neretin; Anna Zhadan; Glafira Kolbasova; Tatiana Neretina; Alexey Kotenev; Alexander Tzetlin; Mikhail Kalyakin
    License

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

    Area covered
    White Sea
    Description

    The White Sea branch of the Zoological Museum of Moscow State University is based on the Nikolai Pertsov White Sea Biological Station. The collection consists mainly of fixed sea invertebrates from the White Sea, as well as from other seas and oceans. The majority of the material is available for molecular genetic research: all material fixed with ethanol is stored at -20°C or -80°С. The collection has over 15 thousand units, over 70 thousand specimens, and over 800 species, including type materials for over 12 species. The collection accepts material for storage and provides material upon request, and is integrated into the Noah’s Arc depository—an automatically updating catalogue of the collection is available online on the sites of the Noah’s Arc project and of the Global Biodiversity Information Facility (GBIF). The White Sea branch of the collection is maintained thanks to the efforts of the curator, employees and friends of the biological station, and museum volunteers. All collection units receive a unique number, with the international index ZMMU WS (Zoological Museum of Moscow University; White Sea branch). Collection homepage: (eng) https://wsbs-msu.ru/wsbs-msu-white-sea-biological-station/white-sea-branch-of-the-zoological-museum-of-moscow-state-university/ (rus) https://wsbs-msu.ru/scientific-collection/

    Acknowledgements:

    We are grateful to people, who participate in collecting, taxonomical identification and catalogisation of the material. Also we thank people, who help us with database service. Also we are grateful to organizers and teachers of GBIF seminar in Russia (2018, May 15-17, IO RAS): N. Ivanova, M. Shashkov, N. Budaeva, D. Schigel, A. Gebruk, A. Rogacheva, S. Pamerlon, L. Smirnova, S. Matland, N. Filippova

    Material was cataloged by: E.A. Alieva, M. Chromova, E. Degtyareva, I.A. Ekimova, V. Emelianenko, M. Everett, N. Filippova, A.N. Isaychev, Yu. Khramova, A.I. Kokorin, G.D. Kolbasova, N. Kulikov, A. Martinez Garcia, A.L. Mikhlina, N.Y. Neretin, E Nikitenko, B.V. Osadchenko, M. Pronina, N.N. Rimskaya-Korsakova, T.D. Shcherbakova, A.N. Stupnikova, A.K. Tabachnick, D.D. Vinogradov, E.V. Vortcepneva, A. S. Yakusheff, D. Yurikova, A.E. Zhadan.

    Material was identified by: T. Alvestad, T.I. Antokhina, E.V. Bogomolova, M. Capa, M. Cherneva, E. Degtyareva, P.Y. Dgebuadze, I.A. Ekimova, N. Ershova, M. Everett, N. Filippova, D. Gaeva, E. Gerasimova, S.A. Gorin, A.N. Isaychev, O. Katanova, A.I. Kokorin, G.D. Kolbasova, J. Kongsrud, K.N. Kosobokova, N. Kulikov, P. Kuznetsov, N. Lavesque, A. Lavrov, M.V. Mardashova, A. Martinez Garcia, A.L. Mikhlina, V.O. Mokievsky, E. Naduvaeva, I. Nekhaev, N.Y. Neretin, D. Nikishina, E.D. Nikitenko, B.V. Osadchenko, E. Oug, M. Petrova, F. Plandin, M.V. Plyuscheva, E.V. Popova, K. Pronkina, A.A. Prudkovsky, V.I. Radashevsky, N.N. Rimskaya-Korsakova, D.M. Saidov, T.D. Shcherbakova, A.K. Shmyglya, N. Shunatova, A.V. Sikorski, M. Sinkova, students, A.N. Stupnikova, A.K. Tabachnick, A.B. Tzetlin, E.V. Vortcepneva, T. Worsfold, A. S. Yakusheff, D. Yurikova, A.E. Zhadan.

    IT support and help: A. Kotenev (MSU), D.N. Vershinin (MSU), Specify Software support team (T. Miller, G. Garett), IT-specialists of Living systems depository "Noah's Ark" (O. Platko, A. Klyukvin), S.K. Garushyants (IITP RAS, Moscow), E. Zotova (MSU), A. Demkiv (MSU), V. Moskalenko (MSU), D. Ilyushin (MSU), Data processing was supported by a GBIF - FinBIF data mobilization grant for European Russia (Russia-02) https://www.gbif.org/project/1r7RSp5iLqqKYssg88WIW2/gbifru-data-mobilization-activities#about

    Other participants (as students or volunteers): Students of the Faculty of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University; Students of the Invertebrate Zoology Department of Biological Faculty, M.V. Lomonosov Moscow State University; A.A. Babitskaya, M.Y Neretin, O.S. Kozlovskaya, N.S. Kozlovskaya

    White Sea Branch of ZMMU was supported by the Russian Scientifc Foundation, grant 14-50-00029, Russian Foundation of Basic Research, grants № 18-05-60158, 19-04-00501, 15-29-02447

  11. A

    Climate Ready Boston Social Vulnerability

    • data.boston.gov
    • cloudcity.ogopendata.com
    • +3more
    Updated Sep 21, 2017
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    Boston Maps (2017). Climate Ready Boston Social Vulnerability [Dataset]. https://data.boston.gov/dataset/climate-ready-boston-social-vulnerability
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    arcgis geoservices rest api, html, csv, kml, geojson, zipAvailable download formats
    Dataset updated
    Sep 21, 2017
    Dataset provided by
    BostonMaps
    Authors
    Boston Maps
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    Boston
    Description
    Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses.

    Source:

    The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.

    Population Definitions:

    Older Adults:
    Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.
    Attribute label: OlderAdult

    Children:
    Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.
    Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.
    Attribute label: TotChild

    People of Color:
    People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups as
    well. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.
    Attribute label: POC2

    Limited English Proficiency:
    Without adequate English skills, residents can miss crucial information on how to prepare
    for hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more socially
    isolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.
    Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.
    Attribute label: LEP

    Low to no Income:
    A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.
    Attribute label: Low_to_No

    People with Disabilities:
    People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty.
    Attribute label: TotDis

    Medical Illness:
    Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.
    Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.
    Attribute label: MedIllnes

    Other attribute definitions:
    GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census Tract
    AREA_SQFT: Tract area (in square feet)
    AREA_ACRES: Tract area (in acres)
    POP100_RE: Tract population count
    HU100_RE: Tract housing unit count
    Name: Boston Neighborhood
  12. Statewide Death Profiles

    • data.chhs.ca.gov
    • data.ca.gov
    • +3more
    csv, zip
    Updated Jul 28, 2025
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    California Department of Public Health (2025). Statewide Death Profiles [Dataset]. https://data.chhs.ca.gov/dataset/statewide-death-profiles
    Explore at:
    csv(4689434), csv(16301), csv(5034), csv(463460), csv(2026589), csv(5401561), csv(164006), csv(200270), csv(419332), zip, csv(385695)Available download formats
    Dataset updated
    Jul 28, 2025
    Dataset authored and provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    Description

    This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

    The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

    The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

  13. Population Projections for Napa County

    • data.countyofnapa.org
    application/rdfxml +5
    Updated Aug 10, 2023
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    California Department of Finance (2023). Population Projections for Napa County [Dataset]. https://data.countyofnapa.org/w/sjku-zj9t/default?cur=5lvCEgbTfgE&from=i57KEYaw4ON
    Explore at:
    json, csv, xml, application/rssxml, application/rdfxml, tsvAvailable download formats
    Dataset updated
    Aug 10, 2023
    Dataset authored and provided by
    California Department of Financehttps://dof.ca.gov/
    Area covered
    Napa County
    Description

    Data Source: CA Department of Finance, Demographic Research Unit

    Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

    This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.

    Data dashboard featuring this data: Napa County Demographics https://data.countyofnapa.org/stories/s/bu3n-fytj

    How was the data collected? Population projections use the following demographic balancing equation: Current Population = Previous Population + (Births - Deaths) +Net Migration

    Previous Population: the starting point for the population projection estimates is the 2020 US Census, informed by the Population Estimates Program data.

    Births and Deaths: birth and death totals came from the California Department of Public Health, Vital Statistics Branch, which maintains birth and death records for California.

    Net Migration: multiple sources of administrative records were used to estimate net migration, including driver’s license address changes, IRS tax return data, Medicare and Medi-Cal enrollment, federal immigration reports, elementary school enrollments, and group quarters population.

    Who was included and excluded from the data? Previous Population: The goal of the US Census is to reflect all populations residing in a given geographic area. Results of two analyses done by the US Census Bureau showed that the 2020 Census total population counts were consistent with recent counts despite the challenges added by the pandemic. However, some populations were undercounted (the Black or African American population, the American Indian or Alaska Native population living on a reservation, the Hispanic or Latino population, and people who reported being of Some Other Race), and some were overcounted (the Non-Hispanic White population and the Asian population). Children, especially children younger than 4, were also undercounted.

    Births and Deaths: Birth records include all people who are born in California as well as births to California residents that happened out of state. Death records include people who died while in California, as well as deaths of California residents that occurred out of state. Because birth and death record data comes from a registration process, the demographic information provided may not be accurate or complete.

    Net Migration: each of the multiple sources of administrative records that were used to estimate net migration include and exclude different groups. For details about methodology, see https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf.

    Where was the data collected?  Data is collected throughout California. This subset of data includes Napa County.

    When was the data collected? This subset of Napa County data is from Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

    These 2019 baseline projections incorporate the latest historical population, birth, death, and migration data available as of July 1, 2020. Historical trends from 1990 through 2020 for births, deaths, and migration are examined. County populations by age, sex, and race/ethnicity are projected to 2060.

    Why was the data collected?  The population projections were prepared under the mandate of the California Government Code (Cal. Gov't Code § 13073, 13073.5).

    Where can I learn more about this data? https://dof.ca.gov/Forecasting/Demographics/Projections/ https://dof.ca.gov/wp-content/uploads/sites/352/Forecasting/Demographics/Documents/P3_Dictionary.txt https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf

  14. Leading causes of death, total population, by age group

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +1more
    Updated Feb 19, 2025
    + more versions
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    Government of Canada, Statistics Canada (2025). Leading causes of death, total population, by age group [Dataset]. http://doi.org/10.25318/1310039401-eng
    Explore at:
    Dataset updated
    Feb 19, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

  15. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Neilsberg Research (2023). United States Age Group Population Dataset: A complete breakdown of United States age demographics from 0 to 85 years, distributed across 18 age groups [Dataset]. https://www.neilsberg.com/research/datasets/5fd2b2bb-3d85-11ee-9abe-0aa64bf2eeb2/

United States Age Group Population Dataset: A complete breakdown of United States age demographics from 0 to 85 years, distributed across 18 age groups

Explore at:
json, csvAvailable download formats
Dataset updated
Sep 16, 2023
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
United States
Variables measured
Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 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) 2017-2021 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the 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 United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 in consideration
  • Population: The population for the specific age group in the United States is shown in this column.
  • % of Total Population: This column displays the population of each age group as a proportion of United States 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 United States Population by Age. You can refer the same here

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