100+ datasets found
  1. Mobile internet users worldwide 2020-2029

    • statista.com
    Updated Feb 5, 2025
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    Statista Research Department (2025). Mobile internet users worldwide 2020-2029 [Dataset]. https://www.statista.com/topics/779/mobile-internet/
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    Dataset updated
    Feb 5, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

  2. Number of smartphone users worldwide 2014-2029

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Number of smartphone users worldwide 2014-2029 [Dataset]. https://www.statista.com/forecasts/1143723/smartphone-users-in-the-world
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    World
    Description

    The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like the Americas and Asia.

  3. Number of smartphone users in the United States 2014-2029

    • statista.com
    • ai-chatbox.pro
    Updated May 5, 2025
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    Statista Research Department (2025). Number of smartphone users in the United States 2014-2029 [Dataset]. https://www.statista.com/topics/2711/us-smartphone-market/
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    Dataset updated
    May 5, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    United States
    Description

    The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

  4. High-Frequency Phone Survey on COVID-19 - World Bank LSMS Harmonized Dataset...

    • catalog.ihsn.org
    • microdata.worldbank.org
    Updated Jan 3, 2022
    + more versions
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    Malawi National Statistical Office (NSO) (2022). High-Frequency Phone Survey on COVID-19 - World Bank LSMS Harmonized Dataset - Malawi [Dataset]. https://catalog.ihsn.org/catalog/9901
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    Dataset updated
    Jan 3, 2022
    Dataset provided by
    National Statistical Office of Malawihttp://www.nsomalawi.mw/
    Authors
    Malawi National Statistical Office (NSO)
    Time period covered
    2019 - 2021
    Area covered
    Malawi
    Description

    Abstract

    To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

    The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

    Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
    2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

    Geographic coverage

    National coverage

    Analysis unit

    • Households
    • Individuals

    Universe

    The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Cleaning operations

    Malawi Integrated Household Panel Survey (IHPS) 2019 and Malawi High-Frequency Phone Survey on COVID-19 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

    The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

    Response rate

    See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

  5. countries of the world

    • kaggle.com
    Updated Jan 24, 2023
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    Rob Cobb (2023). countries of the world [Dataset]. https://www.kaggle.com/datasets/robbcobb/countries
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 24, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Rob Cobb
    Area covered
    World
    Description

    Copy of https://www.kaggle.com/datasets/kisoibo/countries-databasesqlite

    Updated the name of the table from 'countries of the world' to 'countries', for ease of writing queries.

    Info about the dataset:

    Content

    Table Total Rows Total Columns countries of the world **0 ** ** 20** Country, Region, Population, Area (sq. mi.), Pop. Density (per sq. mi.), Coastline (coast/area ratio), Net migration, Infant mortality (per 1000 births), GDP ($ per capita), Literacy (%), Phones (per 1000), Arable (%), Crops (%), Other (%), Climate, Birthrate, Deathrate, Agriculture, Industry, Service

    Acknowledgements

    Acknowledgements Source: All these data sets are made up of data from the US government. Generally they are free to use if you use the data in the US. If you are outside of the US, you may need to contact the US Govt to ask. Data from the World Factbook is public domain. The website says "The World Factbook is in the public domain and may be used freely by anyone at anytime without seeking permission." https://www.cia.gov/library/publications/the-world-factbook/docs/faqs.html

    Inspiration

    When making visualisations related to countries, sometimes it is interesting to group them by attributes such as region, or weigh their importance by population, GDP or other variables.

  6. COVID-19 High Frequency Phone Survey of Households 2020 - World Bank LSMS...

    • catalog.ihsn.org
    • microdata.worldbank.org
    Updated Jan 3, 2022
    + more versions
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    Central Statistics Agency of Ethiopia (2022). COVID-19 High Frequency Phone Survey of Households 2020 - World Bank LSMS Harmonized Dataset - Ethiopia [Dataset]. https://catalog.ihsn.org/catalog/9897
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    Dataset updated
    Jan 3, 2022
    Dataset provided by
    Central Statistical Agencyhttps://ess.gov.et/
    Authors
    Central Statistics Agency of Ethiopia
    Time period covered
    2018 - 2021
    Area covered
    Ethiopia
    Description

    Abstract

    To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

    The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

    Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales. 2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

    Geographic coverage

    National coverage

    Analysis unit

    • Households
    • Individuals

    Universe

    The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Cleaning operations

    Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

    The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

    Response rate

    See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

  7. 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.

  8. w

    COVID-19 National Longitudinal Phone Survey 2020 – World Bank LSMS...

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Oct 25, 2021
    + more versions
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    National Bureau of Statistics (NBS) (2021). COVID-19 National Longitudinal Phone Survey 2020 – World Bank LSMS Harmonized Dataset - Nigeria [Dataset]. https://microdata.worldbank.org/index.php/catalog/3856
    Explore at:
    Dataset updated
    Oct 25, 2021
    Dataset authored and provided by
    National Bureau of Statistics (NBS)
    Time period covered
    2018 - 2021
    Area covered
    Nigeria
    Description

    Abstract

    To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

    The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

    Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
    2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

    Geographic coverage

    National coverage

    Analysis unit

    • Households
    • Individuals

    Universe

    The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Cleaning operations

    Nigeria General Household Survey, Panel (GHS-Panel) 2018-2019 and Nigeria COVID-19 National Longitudinal Phone Survey (COVID-19 NLPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

    The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

    Response rate

    See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

  9. Mobile internet usage reach in North America 2020-2029

    • statista.com
    Updated Feb 5, 2025
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    Statista Research Department (2025). Mobile internet usage reach in North America 2020-2029 [Dataset]. https://www.statista.com/topics/779/mobile-internet/
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    Dataset updated
    Feb 5, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

  10. 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/
    Explore at:
    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

  11. Worldwide COVID-19 Data from WHO (2025 Edition)

    • kaggle.com
    Updated Jul 3, 2025
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    Adil Shamim (2025). Worldwide COVID-19 Data from WHO (2025 Edition) [Dataset]. https://www.kaggle.com/datasets/adilshamim8/worldwide-covid-19-data-from-who
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 3, 2025
    Dataset provided by
    Kaggle
    Authors
    Adil Shamim
    Description

    Dataset Overview

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

    Source Information

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

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

    Dataset Contents

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

    How to Use

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

    Data Reliability

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

    Acknowledgements

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

  12. d

    815 Million Global Contact Data - B2B / Email / Mobile Phone / LinkedIn URL...

    • datarade.ai
    .json, .csv
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    RampedUp Global Data Solutions, 815 Million Global Contact Data - B2B / Email / Mobile Phone / LinkedIn URL - RampedUp [Dataset]. https://datarade.ai/data-products/global-contact-data-personal-and-professional-840-million-rampedup-global-data-solutions
    Explore at:
    .json, .csvAvailable download formats
    Dataset authored and provided by
    RampedUp Global Data Solutions
    Area covered
    Grenada, Haiti, Pakistan, Sint Eustatius and Saba, Bolivia (Plurinational State of), Uganda, Chad, Ireland, Greece, United States Minor Outlying Islands
    Description

    Sign Up for a free trial: https://rampedup.io/sign-up-%2F-log-in - 7 Days and 50 Credits to test our quality and accuracy.

    These are the fields available within the RampedUp Global dataset.

    CONTACT DATA: Personal Email Address - We manage over 115 million personal email addresses Professional Email - We manage over 200 million professional email addresses Home Address - We manage over 20 million home addresses Mobile Phones - 65 million direct lines to decision makers Social Profiles - Individual Facebook, Twitter, and LinkedIn Local Address - We manage 65M locations for local office mailers, event-based marketing or face-to-face sales calls.

    JOB DATA: Job Title - Standardized titles for ease of use and selection Company Name - The Contact's current employer Job Function - The Company Department associated with the job role Title Level - The Level in the Company associated with the job role Job Start Date - Identify people new to their role as a potential buyer

    EMPLOYER DATA: Websites - Company Website, Root Domain, or Full Domain Addresses - Standardized Address, City, Region, Postal Code, and Country Phone - E164 phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, and Twitter

    FIRMOGRAPHIC DATA: Industry - 420 classifications for categorizing the company’s main field of business Sector - 20 classifications for categorizing company industries 4 Digit SIC Code - 239 classifications and their definitions 6 Digit NAICS - 452 classifications and their definitions Revenue - Estimated revenue and bands from 1M to over 1B Employee Size - Exact employee count and bands Email Open Scores - Aggregated data at the domain level showing relationships between email opens and corporate domains. IP Address -Company level IP Addresses associated to Domains from a DNS lookup

    CONSUMER DATA: Education - Alma Mater, Degree, Graduation Date Skills - Accumulated Skills associated with work experience
    Interests - Known interests of contact Connections - Number of social connections. Followers - Number of social followers

    Download our data dictionary: https://rampedup.io/our-data

  13. An inertial and positioning dataset for the walking activity

    • data.niaid.nih.gov
    • search.dataone.org
    • +2more
    zip
    Updated Nov 1, 2024
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    Sara Caramaschi; Carl Magnus Olsson; Elizabeth Orchard; Jackson Molloy; Dario Salvi (2024). An inertial and positioning dataset for the walking activity [Dataset]. http://doi.org/10.5061/dryad.n2z34tn5q
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 1, 2024
    Dataset provided by
    Oxford University Hospitals NHS Trust
    Malmö University
    Authors
    Sara Caramaschi; Carl Magnus Olsson; Elizabeth Orchard; Jackson Molloy; Dario Salvi
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    We are publishing a walking activity dataset including inertial and positioning information from 19 volunteers, including reference distance measured using a trundle wheel. The dataset includes a total of 96.7 Km walked by the volunteers, split into 203 separate tracks. The trundle wheel is of two types: it is either an analogue trundle wheel, which provides the total amount of meters walked in a single track, or it is a sensorized trundle wheel, which measures every revolution of the wheel, therefore recording a continuous incremental distance.
    Each track has data from the accelerometer and gyroscope embedded in the phones, location information from the Global Navigation Satellite System (GNSS), and the step count obtained by the device. The dataset can be used to implement walking distance estimation algorithms and to explore data quality in the context of walking activity and physical capacity tests, fitness, and pedestrian navigation. Methods The proposed dataset is a collection of walks where participants used their own smartphones to capture inertial and positioning information. The participants involved in the data collection come from two sites. The first site is the Oxford University Hospitals NHS Foundation Trust, United Kingdom, where 10 participants (7 affected by cardiovascular diseases and 3 healthy individuals) performed unsupervised 6MWTs in an outdoor environment of their choice (ethical approval obtained by the UK National Health Service Health Research Authority protocol reference numbers: 17/WM/0355). All participants involved provided informed consent. The second site is at Malm ̈o University, in Sweden, where a group of 9 healthy researchers collected data. This dataset can be used by researchers to develop distance estimation algorithms and how data quality impacts the estimation.

    All walks were performed by holding a smartphone in one hand, with an app collecting inertial data, the GNSS signal, and the step counting. On the other free hand, participants held a trundle wheel to obtain the ground truth distance. Two different trundle wheels were used: an analogue trundle wheel that allowed the registration of a total single value of walked distance, and a sensorized trundle wheel which collected timestamps and distance at every 1-meter revolution, resulting in continuous incremental distance information. The latter configuration is innovative and allows the use of temporal windows of the IMU data as input to machine learning algorithms to estimate walked distance. In the case of data collected by researchers, if the walks were done simultaneously and at a close distance from each other, only one person used the trundle wheel, and the reference distance was associated with all walks that were collected at the same time.The walked paths are of variable length, duration, and shape. Participants were instructed to walk paths of increasing curvature, from straight to rounded. Irregular paths are particularly useful in determining limitations in the accuracy of walked distance algorithms. Two smartphone applications were developed for collecting the information of interest from the participants' devices, both available for Android and iOS operating systems. The first is a web-application that retrieves inertial data (acceleration, rotation rate, orientation) while connecting to the sensorized trundle wheel to record incremental reference distance [1]. The second app is the Timed Walk app [2], which guides the user in performing a walking test by signalling when to start and when to stop the walk while collecting both inertial and positioning data. All participants in the UK used the Timed Walk app.

    The data collected during the walk is from the Inertial Measurement Unit (IMU) of the phone and, when available, the Global Navigation Satellite System (GNSS). In addition, the step count information is retrieved by the sensors embedded in each participant’s smartphone. With the dataset, we provide a descriptive table with the characteristics of each recording, including brand and model of the smartphone, duration, reference total distance, types of signals included and additionally scoring some relevant parameters related to the quality of the various signals. The path curvature is one of the most relevant parameters. Previous literature from our team, in fact, confirmed the negative impact of curved-shaped paths with the use of multiple distance estimation algorithms [3]. We visually inspected the walked paths and clustered them in three groups, a) straight path, i.e. no turns wider than 90 degrees, b) gently curved path, i.e. between one and five turns wider than 90 degrees, and c) curved path, i.e. more than five turns wider than 90 degrees. Other features relevant to the quality of collected signals are the total amount of time above a threshold (0.05s and 6s) where, respectively, inertial and GNSS data were missing due to technical issues or due to the app going in the background thus losing access to the sensors, sampling frequency of different data streams, average walking speed and the smartphone position. The start of each walk is set as 0 ms, thus not reporting time-related information. Walks locations collected in the UK are anonymized using the following approach: the first position is fixed to a central location of the city of Oxford (latitude: 51.7520, longitude: -1.2577) and all other positions are reassigned by applying a translation along the longitudinal and latitudinal axes which maintains the original distance and angle between samples. This way, the exact geographical location is lost, but the path shape and distances between samples are maintained. The difference between consecutive points “as the crow flies” and path curvature was numerically and visually inspected to obtain the same results as the original walks. Computations were made possible by using the Haversine Python library.

    Multiple datasets are available regarding walking activity recognition among other daily living tasks. However, few studies are published with datasets that focus on the distance for both indoor and outdoor environments and that provide relevant ground truth information for it. Yan et al. [4] introduced an inertial walking dataset within indoor scenarios using a smartphone placed in 4 positions (on the leg, in a bag, in the hand, and on the body) by six healthy participants. The reference measurement used in this study is a Visual Odometry System embedded in a smartphone that has to be worn at the chest level, using a strap to hold it. While interesting and detailed, this dataset lacks GNSS data, which is likely to be used in outdoor scenarios, and the reference used for localization also suffers from accuracy issues, especially outdoors. Vezovcnik et al. [5] analysed estimation models for step length and provided an open-source dataset for a total of 22 km of only inertial walking data from 15 healthy adults. While relevant, their dataset focuses on steps rather than total distance and was acquired on a treadmill, which limits the validity in real-world scenarios. Kang et al. [6] proposed a way to estimate travelled distance by using an Android app that uses outdoor walking patterns to match them in indoor contexts for each participant. They collect data outdoors by including both inertial and positioning information and they use average values of speed obtained by the GPS data as reference labels. Afterwards, they use deep learning models to estimate walked distance obtaining high performances. Their results share that 3% to 11% of the data for each participant was discarded due to low quality. Unfortunately, the name of the used app is not reported and the paper does not mention if the dataset can be made available.

    This dataset is heterogeneous under multiple aspects. It includes a majority of healthy participants, therefore, it is not possible to generalize the outcomes from this dataset to all walking styles or physical conditions. The dataset is heterogeneous also from a technical perspective, given the difference in devices, acquired data, and used smartphone apps (i.e. some tests lack IMU or GNSS, sampling frequency in iPhone was particularly low). We suggest selecting the appropriate track based on desired characteristics to obtain reliable and consistent outcomes.

    This dataset allows researchers to develop algorithms to compute walked distance and to explore data quality and reliability in the context of the walking activity. This dataset was initiated to investigate the digitalization of the 6MWT, however, the collected information can also be useful for other physical capacity tests that involve walking (distance- or duration-based), or for other purposes such as fitness, and pedestrian navigation.

    The article related to this dataset will be published in the proceedings of the IEEE MetroXRAINE 2024 conference, held in St. Albans, UK, 21-23 October.

    This research is partially funded by the Swedish Knowledge Foundation and the Internet of Things and People research center through the Synergy project Intelligent and Trustworthy IoT Systems.

  14. i

    Global Financial Inclusion (Global Findex) Database 2021 - Hong Kong SAR,...

    • catalog.ihsn.org
    • datacatalog.ihsn.org
    • +1more
    Updated Dec 16, 2022
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    Development Research Group, Finance and Private Sector Development Unit (2022). Global Financial Inclusion (Global Findex) Database 2021 - Hong Kong SAR, China [Dataset]. https://catalog.ihsn.org/catalog/10449
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    Dataset updated
    Dec 16, 2022
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2021
    Area covered
    Hong Kong
    Description

    Abstract

    The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

    The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

    The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

    Geographic coverage

    National coverage

    Analysis unit

    Individual

    Kind of data

    Observation data/ratings [obs]

    Sampling procedure

    In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

    In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

    In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

    The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

    For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

    Sample size for Hong Kong SAR, China is 1003.

    Mode of data collection

    Landline and mobile telephone

    Research instrument

    Questionnaires are available on the website.

    Sampling error estimates

    Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

  15. People Data Labs - Person Dataset

    • datarade.ai
    .json, .csv
    Updated Jul 6, 2020
    + more versions
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    People Data Labs (2020). People Data Labs - Person Dataset [Dataset]. https://datarade.ai/data-products/global-license
    Explore at:
    .json, .csvAvailable download formats
    Dataset updated
    Jul 6, 2020
    Dataset provided by
    People Data Labs Inc.
    Authors
    People Data Labs
    Area covered
    Maldives, Anguilla, Kenya, Wallis and Futuna, Antarctica, United States of America, Afghanistan, Guinea, Bosnia and Herzegovina, Tunisia
    Description

    People Data Labs is an aggregator of B2B person and company data. We source our globally compliant person dataset via our "Data Union".

    The "Data Union" is our proprietary data sharing co-op. Customers opt-in to sharing their data and warrant that their data is fully compliant with global data privacy regulations. Some data sources are provided as a one time dump, others are refreshed every time we do a new data build. Our data sources come from a variety of verticals including HR Tech, Real Estate Tech, Identity/Anti-Fraud, Martech, and others. People Data Labs works with customers on compliance based topics. If a customer wishes to ensure anonymity, we work with them to anonymize the data.

    Our person data has over 100 fields including resume data (work history, education), contact information (email, phone), demographic info (name, gender, birth date) and social profile information (linkedin, github, twitter, facebook, etc...).

  16. Iran IR: Internet Users: Individuals: % of Population

    • ceicdata.com
    Updated Mar 15, 2024
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    CEICdata.com (2024). Iran IR: Internet Users: Individuals: % of Population [Dataset]. https://www.ceicdata.com/en/iran/telecommunication/ir-internet-users-individuals--of-population
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    Dataset updated
    Mar 15, 2024
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Dec 1, 2005 - Dec 1, 2016
    Area covered
    Iran
    Variables measured
    Phone Statistics
    Description

    Iran IR: Internet Users: Individuals: % of Population data was reported at 60.416 % in 2017. This records an increase from the previous number of 53.227 % for 2016. Iran IR: Internet Users: Individuals: % of Population data is updated yearly, averaging 8.100 % from Dec 1990 (Median) to 2017, with 25 observations. The data reached an all-time high of 60.416 % in 2017 and a record low of 0.000 % in 1990. Iran IR: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

  17. d

    Phone Number Data | APAC | 100M+ B2B Mobile Phone Numbers | 95%+ Accuracy

    • datarade.ai
    .json, .csv
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    Forager.ai, Phone Number Data | APAC | 100M+ B2B Mobile Phone Numbers | 95%+ Accuracy [Dataset]. https://datarade.ai/data-products/apac-b2b-mobile-data-90m-95-accuracy-api-bi-weekly-up-forager-ai
    Explore at:
    .json, .csvAvailable download formats
    Dataset provided by
    Forager.ai
    Area covered
    Bhutan, Uruguay, San Marino, Georgia, El Salvador, Bahamas, Belarus, Libya, Ghana, Burkina Faso
    Description

    Global B2B Mobile Phone Number Database | 100M+ Verified Contacts | 95% Accuracy Forager.ai provides the world’s most reliable mobile phone number data for businesses that refuse to compromise on quality. With 100 million+ professionally verified mobile numbers refreshed every 3 weeks, our database ensures 95% accuracy – so your teams never waste time on dead-end leads.

    Why Our Data Wins ✅ Accuracy You Can Trust 95% of mobile numbers are verified against live carrier records and tied to current job roles. Say goodbye to “disconnected number” voicemails.

    ✅ Depth Beyond Digits Each contact includes 150+ data points:

    Direct mobile numbers

    Current job title, company, and department

    Full career history + education background

    Location data + LinkedIn profiles

    Company size, industry, and revenue

    ✅ Freshness Guaranteed Bi-weekly updates combat job-hopping and role changes – critical for sales teams targeting decision-makers.

    ✅ Ethically Sourced & Compliant First-party collected data with full GDPR/CCPA compliance.

    Who Uses This Data?

    Sales Teams: Cold-call C-suite prospects with verified mobile numbers.

    Marketers: Run hyper-personalized SMS/WhatsApp campaigns.

    Recruiters: Source passive candidates with up-to-date contact intel.

    Data Vendors: License premium datasets to enhance your product.

    Tech Platforms: Power your SaaS tools via API with enterprise-grade B2B data.

    Flexible Delivery, Instant Results

    API (REST): Real-time integration for CRMs, dialers, or marketing stacks

    CSV/JSON: Campaign-ready files.

    PostgreSQL: Custom databases for large-scale enrichment

    Compliance: Full audit trails + opt-out management

    Why Forager.ai? → Proven ROI: Clients see 62% higher connect rates vs. industry averages (request case studies). → No Guesswork: Test-drive free samples before committing. → Scalable Pricing: Pay per record, license datasets, or get unlimited API access.

    B2B Mobile Phone Data | Verified Contact Database | Sales Prospecting Lists | CRM Enrichment | Recruitment Phone Numbers | Marketing Automation | Phone Number Datasets | GDPR-Compliant Leads | Direct Dial Contacts | Decision-Maker Data

    Need Proof? Contact us to see why Fortune 500 companies and startups alike trust Forager.ai for mission-critical outreach.

  18. 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?

  19. d

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

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

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

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

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

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

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

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

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

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

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

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

  20. w

    Global Financial Inclusion (Global Findex) Database 2021 - Chad

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Jun 8, 2023
    + more versions
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    Development Research Group, Finance and Private Sector Development Unit (2023). Global Financial Inclusion (Global Findex) Database 2021 - Chad [Dataset]. https://microdata.worldbank.org/index.php/catalog/5849
    Explore at:
    Dataset updated
    Jun 8, 2023
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2022
    Area covered
    Chad
    Description

    Abstract

    The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

    The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

    The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

    Geographic coverage

    Because of security issues and difficult terrain, seven regions are excluded from the sampling: Lac, Ouaddaï, Wadi Fira, Bourkou, Ennedi, Tibesti, Salamat. In addition, the North Kanem and Bahr El Gazal North districts were excluded due to accessibility issues. Quartiers/villages with less than 50 inhabitants are also excluded from sampling. The excluded areas represent 23% of the population.

    Kind of data

    Observation data/ratings [obs]

    Sampling procedure

    In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

    In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

    In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

    The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

    For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

    Sample size for Chad is 1000.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Questionnaires are available on the website.

    Sampling error estimates

    Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

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Statista Research Department (2025). Mobile internet users worldwide 2020-2029 [Dataset]. https://www.statista.com/topics/779/mobile-internet/
Organization logo

Mobile internet users worldwide 2020-2029

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181 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Feb 5, 2025
Dataset provided by
Statistahttp://statista.com/
Authors
Statista Research Department
Description

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

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