45 datasets found
  1. Total population worldwide 1950-2100

    • statista.com
    • ai-chatbox.pro
    Updated Feb 24, 2025
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    Statista (2025). Total population worldwide 1950-2100 [Dataset]. https://www.statista.com/statistics/805044/total-population-worldwide/
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    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    World
    Description

    The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

  2. M

    World Population Growth Rate

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). World Population Growth Rate [Dataset]. https://www.macrotrends.net/global-metrics/countries/wld/world/population-growth-rate
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    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

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

    Time period covered
    Jan 1, 1961 - Dec 31, 2023
    Area covered
    World, World
    Description

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

  3. o

    Geonames - All Cities with a population > 1000

    • public.opendatasoft.com
    • data.smartidf.services
    • +1more
    csv, excel, geojson +1
    Updated Mar 10, 2024
    + more versions
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    (2024). Geonames - All Cities with a population > 1000 [Dataset]. https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/
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    csv, json, geojson, excelAvailable download formats
    Dataset updated
    Mar 10, 2024
    License

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

    Description

    All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

  4. d

    Data from: Anthropogenic Biomes of the World, Version 1

    • catalog.data.gov
    • s.cnmilf.com
    • +2more
    Updated Apr 24, 2025
    + more versions
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    SEDAC (2025). Anthropogenic Biomes of the World, Version 1 [Dataset]. https://catalog.data.gov/dataset/anthropogenic-biomes-of-the-world-version-1
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    Dataset updated
    Apr 24, 2025
    Dataset provided by
    SEDAC
    Area covered
    World, Earth
    Description

    The Anthropogenic Biomes of the World, Version 1 data set describes globally-significant ecological patterns within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture, urbanization, forestry and other land uses. Conventional biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate. Now that humans have fundamentally altered global patterns of ecosystem form, process, and biodiversity, anthropogenic biomes provide a contemporary view of the terrestrial biosphere in its human-altered form. Anthropogenic biomes may also be termed "anthromes" to distinguish them from conventional biome systems, or "human biomes" (a simpler but less precise term). This data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

  5. Standard populations dataset

    • kaggle.com
    Updated Mar 12, 2023
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    Matthias Kleine (2023). Standard populations dataset [Dataset]. https://www.kaggle.com/datasets/matthiaskleine/standard-populations-dataset
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 12, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Matthias Kleine
    Description

    Do you know further standard populations?

    If you know any further standard populations worth integrating in this dataset, please let me know in the discussion part. I would be happy to integrate further data to make this dataset more useful for everybody.

    German "Federal Health Monitoring System" about 'standard populations':

    "Standard populations are "artificial populations" with fictitious age structures, that are used in age standardization as uniform basis for the calculation of comparable measures for the respective reference population(s).

    Use: Age standardizations based on a standard population are often used at cancer registries to compare morbidity or mortality rates. If there are different age structures in populations of different regions or in a population in one region over time, the comparability of their mortality or morbidity rates is only limited. For interregional or inter-temporal comparisons, therefore, an age standardization is necessary. For this purpose the age structure of a reference population, the so-called standard population, is assumed for the study population. The age specific mortality or morbidity rates of the study population are weighted according to the age structure of the standard population. Selection of a standard population:

    Which standard population is used for comparison basically, does not matter. It is important, however, that

    1. the demographic structure of the standard population is not too dissimilar to that of the reference population and
    2. the comparable rates refer to the same standard."

    Aim of this dataset

    The aim of this dataset is to provide a variety of the most commonly used 'standard populations'.

    Currently, two files with 22 standard populations are provided: - standard_populations_20_age_groups.csv - 20 age groups: '0', '01-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85-89', '90+' - 7 standard populations: 'Standard population Germany 2011', 'Standard population Germany 1987', 'Standard population of Europe 2013', 'Standard population Old Laender 1987', 'Standard population New Laender 1987', 'New standard population of Europe', 'World standard population' - source: German Federal Health Monitoring System

    • standard_populations_19_age_groups.csv
      • 19 age groups: '0', '01-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85+'
      • 15 standard populations: '1940 U.S. Std Million', '1950 U.S. Std Million', '1960 U.S. Std Million', '1970 U.S. Std Million', '1980 U.S. Std Million', '1990 U.S. Std Million', '1991 Canadian Std Million', '1996 Canadian Std Million', '2000 U.S. Std Million', '2000 U.S. Std Population (Census P25-1130)', '2011 Canadian Standard Population', 'European (EU-27 plus EFTA 2011-2030) Std Million', 'European (Scandinavian 1960) Std Million', 'World (Segi 1960) Std Million', 'World (WHO 2000-2025) Std Million'
      • source: National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program

    Terms of use

    No restrictions are known to the author. Standard populations are published by different organisations for public usage.

  6. World Bank: GHNP Data

    • kaggle.com
    zip
    Updated Mar 20, 2019
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    World Bank (2019). World Bank: GHNP Data [Dataset]. https://www.kaggle.com/theworldbank/world-bank-health-population
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    zip(0 bytes)Available download formats
    Dataset updated
    Mar 20, 2019
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    License

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

    Description

    Context

    The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

    Content

    This dataset combines key health statistics from a variety of sources to provide a look at global health and population trends. It includes information on nutrition, reproductive health, education, immunization, and diseases from over 200 countries.

    Update Frequency: Biannual

    For more information, see the World Bank website.

    Fork this kernel to get started with this dataset.

    Acknowledgements

    https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics

    https://cloud.google.com/bigquery/public-data/world-bank-hnp

    Dataset Source: World Bank. 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.

    Citation: The World Bank: Health Nutrition and Population Statistics

    Banner Photo by @till_indeman from Unplash.

    Inspiration

    What’s the average age of first marriages for females around the world?

  7. Gridded Population of the World, v.4

    • pacific-data.sprep.org
    • solomonislands-data.sprep.org
    • +13more
    tiff
    Updated Nov 2, 2022
    + more versions
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    Center for International Earth Science Information Network - CIESIN - Columbia University (2022). Gridded Population of the World, v.4 [Dataset]. https://pacific-data.sprep.org/dataset/gridded-population-world-v4
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    tiff(369581807), tiff(369421940), tiff(369652849), tiff(369722113), tiff(369514106)Available download formats
    Dataset updated
    Nov 2, 2022
    Dataset provided by
    Pacific Regional Environment Programmehttps://www.sprep.org/
    Authors
    Center for International Earth Science Information Network - CIESIN - Columbia University
    License

    Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
    License information was derived automatically

    Area covered
    World, 552.10693359375 -86.244179470475)), 552.10693359375 84.640776810146, -172.11181640625 84.640776810146, POLYGON ((-172.11181640625 -86.244179470475, Global
    Description

    The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution.

    Purpose: To provide estimates of population density for the years 2000, 2005, 2010, 2015, and 2020, based on counts consistent with national censuses and population registers, as raster data to facilitate data integration.

    Recommended Citation(s)*: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. Accessed DAY MONTH YEAR.

  8. f

    ORBIT: A real-world few-shot dataset for teachable object recognition...

    • city.figshare.com
    bin
    Updated May 31, 2023
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    Daniela Massiceti; Lida Theodorou; Luisa Zintgraf; Matthew Tobias Harris; Simone Stumpf; Cecily Morrison; Edward Cutrell; Katja Hofmann (2023). ORBIT: A real-world few-shot dataset for teachable object recognition collected from people who are blind or low vision [Dataset]. http://doi.org/10.25383/city.14294597.v3
    Explore at:
    binAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    City, University of London
    Authors
    Daniela Massiceti; Lida Theodorou; Luisa Zintgraf; Matthew Tobias Harris; Simone Stumpf; Cecily Morrison; Edward Cutrell; Katja Hofmann
    License

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

    Description

    Object recognition predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset, grounded in a real-world application of teachable object recognizers for people who are blind/low vision. We provide a full, unfiltered dataset of 4,733 videos of 588 objects recorded by 97 people who are blind/low-vision on their mobile phones, and a benchmark dataset of 3,822 videos of 486 objects collected by 77 collectors. The code for loading the dataset, computing all benchmark metrics, and running the baseline models is available at https://github.com/microsoft/ORBIT-DatasetThis version comprises several zip files:- train, validation, test: benchmark dataset, organised by collector, with raw videos split into static individual frames in jpg format at 30FPS- other: data not in the benchmark set, organised by collector, with raw videos split into static individual frames in jpg format at 30FPS (please note that the train, validation, test, and other files make up the unfiltered dataset)- *_224: as for the benchmark, but static individual frames are scaled down to 224 pixels.- *_unfiltered_videos: full unfiltered dataset, organised by collector, in mp4 format.

  9. Forest proximate people - 5km cutoff distance (Global - 100m)

    • data.amerigeoss.org
    http, wmts
    Updated Oct 24, 2022
    + more versions
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    Food and Agriculture Organization (2022). Forest proximate people - 5km cutoff distance (Global - 100m) [Dataset]. https://data.amerigeoss.org/dataset/8ed893bd-842a-4866-a655-a0a0c02b79b5
    Explore at:
    http, wmtsAvailable download formats
    Dataset updated
    Oct 24, 2022
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    License

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

    Description

    The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.

    For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L. Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: A new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

    Contact points:

    Maintainer: Leticia Pina

    Maintainer: Sarah E., Castle

    Data lineage:

    The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 5 kilometers of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 5-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

    References:

    Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

    Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

    WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

    Online resources:

    GEE asset for "Forest proximate people - 5km cutoff distance"

  10. h

    meta-shepherd-human-data

    • huggingface.co
    Updated Aug 23, 2023
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    Philipp Schmid (2023). meta-shepherd-human-data [Dataset]. https://huggingface.co/datasets/philschmid/meta-shepherd-human-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 23, 2023
    Authors
    Philipp Schmid
    License

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

    Description

    Dataset Card for "meta-shepherd-human-data"

    Original Dataset: https://github.com/facebookresearch/Shepherd

      Example
    

    Question: Where on the planet would you expect a bald eagle to live?

    Here are the options: Option 1: colorado Option 2: outside Option 3: protection Option 4: zoo exhibit Option 5: world

    Please choose the correct option and justify your choice:

    Answer: Bald eagles are found throughout most of North America, from Alaska and Canada south to… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/meta-shepherd-human-data.

  11. a

    PerCapita CO2 Footprint InDioceses FULL

    • hub.arcgis.com
    • catholic-geo-hub-cgisc.hub.arcgis.com
    Updated Sep 23, 2019
    + more versions
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    burhansm2 (2019). PerCapita CO2 Footprint InDioceses FULL [Dataset]. https://hub.arcgis.com/content/95787df270264e6ea1c99ffa6ff844ff
    Explore at:
    Dataset updated
    Sep 23, 2019
    Dataset authored and provided by
    burhansm2
    License

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

    Area covered
    Description

    PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

  12. a

    Global Human Footprint Index

    • hub.arcgis.com
    • climate.esri.ca
    • +2more
    Updated Jul 13, 2015
    + more versions
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    Columbia (2015). Global Human Footprint Index [Dataset]. https://hub.arcgis.com/maps/65518e782be04e7db31de65d53d591a9
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    Dataset updated
    Jul 13, 2015
    Dataset authored and provided by
    Columbia
    Area covered
    Description

    Global Human Footprint Index represents the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset SummaryThe Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers of human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced–the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).Recommended CitationWildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.

  13. i

    BLE-WBAN: RF real-world dataset of BLE devices in human-centric healthcare...

    • ieee-dataport.org
    Updated Aug 6, 2024
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    SeyedMohammad Kashani (2024). BLE-WBAN: RF real-world dataset of BLE devices in human-centric healthcare environments [Dataset]. https://ieee-dataport.org/documents/ble-wban-rf-real-world-dataset-ble-devices-human-centric-healthcare-environments
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    Dataset updated
    Aug 6, 2024
    Authors
    SeyedMohammad Kashani
    License

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

    Area covered
    World
    Description

    obtaining large

  14. d

    Raw Stressor Data: A Global Map of Human Impact on Marine Ecosystems, 2008

    • search.dataone.org
    • knb.ecoinformatics.org
    • +1more
    Updated Dec 7, 2018
    + more versions
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    Benjamin Halpern; Shaun Walbridge; Kimberly Selkoe; Carrie Kappel; Fiorenza Micheli; Caterina D'Agrosa; John Bruno; Kenneth Casey; Colin Ebert; Helen Fox; Rod Fujita; Dennis Heinemann; Hunter Lenihan; Elizabeth Madin; Matthew Perry; Elizabeth Selig; Mark Spalding; Robert Steneck; Reg Watson (2018). Raw Stressor Data: A Global Map of Human Impact on Marine Ecosystems, 2008 [Dataset]. http://doi.org/10.5063/F1JW8C4R
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    Dataset updated
    Dec 7, 2018
    Dataset provided by
    Knowledge Network for Biocomplexity
    Authors
    Benjamin Halpern; Shaun Walbridge; Kimberly Selkoe; Carrie Kappel; Fiorenza Micheli; Caterina D'Agrosa; John Bruno; Kenneth Casey; Colin Ebert; Helen Fox; Rod Fujita; Dennis Heinemann; Hunter Lenihan; Elizabeth Madin; Matthew Perry; Elizabeth Selig; Mark Spalding; Robert Steneck; Reg Watson
    Time period covered
    Jan 1, 2008
    Area covered
    Earth
    Description

    What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. This dataset contains raw stressor data from 17 different human activities that directly or indirectly have an impact on the ecological communities in the ocean's ecosystems. For more information on specific dataset, see the methods section. All data are projected in WGS 1984 Mollweide.

  15. w

    Dataset of publication dates of book subjects that contain Origins : how the...

    • workwithdata.com
    Updated Nov 7, 2024
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    Work With Data (2024). Dataset of publication dates of book subjects that contain Origins : how the Earth shaped human history [Dataset]. https://www.workwithdata.com/datasets/book-subjects?col=book_subject%2Cj0-publication_date&f=1&fcol0=j0-book&fop0=%3D&fval0=Origins+%3A+how+the+Earth+shaped+human+history&j=1&j0=books
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    Dataset updated
    Nov 7, 2024
    Dataset authored and provided by
    Work With Data
    License

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

    Area covered
    Earth
    Description

    This dataset is about book subjects. It has 2 rows and is filtered where the books is Origins : how the Earth shaped human history. It features 2 columns including publication dates.

  16. countries of the world

    • kaggle.com
    Updated Oct 7, 2018
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    KRANTHI KUMAR (2018). countries of the world [Dataset]. https://www.kaggle.com/kranthikumar11/countries-of-the-world/tasks
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 7, 2018
    Dataset provided by
    Kaggle
    Authors
    KRANTHI KUMAR
    Area covered
    World, Earth
    Description

    Dataset

    This dataset was created by KRANTHI KUMAR

    Released under Other (specified in description)

    Contents

  17. w

    Dataset of books called Between heaven and earth : the religious worlds...

    • workwithdata.com
    Updated Apr 17, 2025
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    Work With Data (2025). Dataset of books called Between heaven and earth : the religious worlds people make and the scholars who study them [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=book&fop0=%3D&fval0=Between+heaven+and+earth+%3A+the+religious+worlds+people+make+and+the+scholars+who+study+them
    Explore at:
    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    License

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

    Area covered
    Earth
    Description

    This dataset is about books. It has 2 rows and is filtered where the book is Between heaven and earth : the religious worlds people make and the scholars who study them. It features 7 columns including author, publication date, language, and book publisher.

  18. 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.

  19. The SESAME Human-Earth Atlas

    • springernature.figshare.com
    zip
    Updated May 13, 2025
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    Abdullah Al Faisal; Maxwell Kaye; Maimoonah Ahmed; Eric Galbraith (2025). The SESAME Human-Earth Atlas [Dataset]. http://doi.org/10.6084/m9.figshare.28432499.v1
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    zipAvailable download formats
    Dataset updated
    May 13, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Abdullah Al Faisal; Maxwell Kaye; Maimoonah Ahmed; Eric Galbraith
    License

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

    Area covered
    Earth
    Description

    The Surface Earth System Analysis and Modeling Environment (SESAME) Human-Earth Atlas includes hundreds of variables capturing both human and non-human aspects of the Earth system on two common spatial grids of 1- and 0.25-degree resolution. The Atlas is structured by common spheres, and many variables resolve changes over time. Many of the national-level tabular human system variables are downscaled to spatial grids using dasymetric mapping, accounting for country boundary changes over time. An associated software toolbox allows users to add raster, point, line, polygon, and tabular datasets, transforming them onto a standardized spatial grid at the desired resolution as well as to work conveniently with jurisdictional (e.g. country) data.

    File Description: atlas: Contains netCDF files at 1-degree resolution in netCDF format. atlas_p25: Contains selected netCDF files at 0.25-degree resolution. genscripts: Original Jupyter notebook scripts used to generate the atlas. SESAME_Atlas_Documentation_v1.pdf: Documentation file for the SESAME Human-Earth Atlas. SESAME_Human-Earth_Atlas_v1.xlsx: Comprehensive summary and documentation for the SESAME Human-Earth Atlas, including details on pre- and post-processing steps.

  20. o

    Question Decomposition Meaning Dataset

    • opendatabay.com
    .undefined
    Updated Jul 7, 2025
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    Datasimple (2025). Question Decomposition Meaning Dataset [Dataset]. https://www.opendatabay.com/data/ai-ml/51c7d209-b1e2-4218-bdf1-c935416c3ca4
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    .undefinedAvailable download formats
    Dataset updated
    Jul 7, 2025
    Dataset authored and provided by
    Datasimple
    License

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

    Area covered
    Data Science and Analytics
    Description

    Welcome to BreakData, an innovative dataset designed for exploring language understanding [1]. This dataset provides a wealth of information concerning question decomposition, operators, splits, sources, and allowed tokens, enabling precise question answering [1]. It offers deep insights into human language comprehension and interpretation, proving highly valuable for researchers developing sophisticated AI technologies [1]. The goal of BreakData is to facilitate the development of advanced natural language processing (NLP) models, applicable in various areas such as automated customer support, healthcare chatbots, or automated marketing campaigns [1].

    Columns

    Based on the QDMR Lexicon: Source and Allowed Tokens file, the dataset includes the following columns: * source: This string column indicates the origin of the question [2]. * allowed_tokens: This string column specifies the tokens permitted for the question [2].

    The dataset also comprises other files, such as QDMR files which include questions or statements from common domains like healthcare or banking, requiring interpretation based on a series of operators [3]. These files necessitate the identification of keywords, entities (e.g., time references, monetary amounts, Boolean values), and relationships between them [3]. Additionally, LogicalForms files contain logical forms that serve as building blocks for linking ideas across different sets of incoming variables [3].

    Distribution

    The BreakData dataset is typically provided in CSV format [1, 4]. It is structured into nine distinct files, which include QDMR_train.csv, QDMR_validation.csv, QDMR-highlevel_train.csv, QDMR-highlevel_test.csv, logicalforms_train.csv, logicalforms_validation.csv, QDMRlexicon_train.csv, QDMRLexicon_test.csv, and QDHMLexiconHighLevelTest.csv [1]. While the dataset's structure is clear, specific numbers for rows or records within each file are not detailed in the provided information. The current version of the dataset is 1.0 [5].

    Usage

    This dataset presents an excellent opportunity to explore and comprehend the intricacies of language understanding [1]. It is ideal for training models for a variety of natural language processing (NLP) activities, including: * Question answering systems [1]. * Text analytics [1]. * Automated dialogue systems [1]. * Developing advanced NLP models to analyse questions using decompositions, operators, and splits [6]. * Training machine learning algorithms to predict the semantic meaning of questions based on their decomposition and split [6]. * Conducting text analytics by utilising the allowed tokens dataset to map how people communicate specific concepts across different contexts or topics [6]. * Optimising machine decisions for human-like interactions, leading to improved decision-making in applications like automated customer support, healthcare advice, and marketing campaigns [1, 3].

    Coverage

    The BreakData dataset covers a global region [5]. Its content is drawn from common domains such as healthcare and banking, featuring questions and statements that require linguistic analysis [1, 3]. There are no specific notes on time range or demographic scope beyond these general domains.

    License

    CC0

    Who Can Use It

    This dataset is primarily intended for: * Researchers developing sophisticated models to advance AI technologies [1]. * Data scientists and AI/ML engineers looking to train models for natural language understanding tasks [1]. * Those interested in analysing existing questions or commands with accurate decompositions and operators [1]. * Developers of machine learning models powered by NLP for seamless inference and improved results in customer engagement [3].

    Dataset Name Suggestions

    • BreakData Language Decomposition
    • Question Decomposition Meaning Dataset
    • NLP Language Understanding Hub
    • Semantic Question Analysis Data
    • BreakData NLP Foundation

    Attributes

    Original Data Source: Break (Question Decomposition Meaning)

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Statista (2025). Total population worldwide 1950-2100 [Dataset]. https://www.statista.com/statistics/805044/total-population-worldwide/
Organization logo

Total population worldwide 1950-2100

Explore at:
26 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Feb 24, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
World
Description

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

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