100+ datasets found
  1. Global online users concerns about misuse of personal data Q3 2024, by...

    • statista.com
    Updated Jun 23, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Global online users concerns about misuse of personal data Q3 2024, by country [Dataset]. https://www.statista.com/statistics/1382850/concerns-personal-data-misuse-by-country/
    Explore at:
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    As of the third quarter of 2024, **** percent of internet users in Spain stated being concerned about companies' misuse of their personal data. Portugal ranked second, with ** percent, while Brazil followed, with around **** percent of online users worried about improper use of their personal online data.

  2. Data from: Internet users

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Apr 6, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Office for National Statistics (2021). Internet users [Dataset]. https://www.ons.gov.uk/businessindustryandtrade/itandinternetindustry/datasets/internetusers
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Apr 6, 2021
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

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

    Description

    Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

  3. Number of data users in the EU and UK 2016-2020 and 2025

    • statista.com
    Updated Jul 6, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2020). Number of data users in the EU and UK 2016-2020 and 2025 [Dataset]. https://www.statista.com/statistics/1134965/number-of-data-users-eu-uk/
    Explore at:
    Dataset updated
    Jul 6, 2020
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    European Union, United Kingdom
    Description

    According to statistics published by the European Commission, the number of data users are estimated to reach ******* in the ** European Union countries and the United Kingdom in 2020. The number of data users in these countries is forecast to grow in the coming years, reaching ******* by 2025 in the baseline scenario.

    The source defines data users as organizations that generate, exploit, collect and analyze digital data intensively and use what they learn to improve their business. They represent the demand side of the data market.

  4. z

    Requirements data sets (user stories)

    • zenodo.org
    • data.mendeley.com
    txt
    Updated Jan 13, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Fabiano Dalpiaz; Fabiano Dalpiaz (2025). Requirements data sets (user stories) [Dataset]. http://doi.org/10.17632/7zbk8zsd8y.1
    Explore at:
    txtAvailable download formats
    Dataset updated
    Jan 13, 2025
    Dataset provided by
    Mendeley Data
    Authors
    Fabiano Dalpiaz; Fabiano Dalpiaz
    License

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

    Description

    A collection of 22 data set of 50+ requirements each, expressed as user stories.

    The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

    The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

    This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

    Overview of the datasets [data and links added in December 2024]

    The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

    Public administration and transparency

    g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

    g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

    g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

    g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

    g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

    (Research) data and meta-data management

    g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

    g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

    g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

    g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

    g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

    g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

    g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
    born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its

  5. Types of data users are willing to share with advertisers in the U.S 2022,...

    • statista.com
    Updated Jul 7, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Types of data users are willing to share with advertisers in the U.S 2022, by age [Dataset]. https://www.statista.com/statistics/1421574/data-types-shared-with-advertiser-us-by-age/
    Explore at:
    Dataset updated
    Jul 7, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2022
    Area covered
    United States
    Description

    During a December 2022 survey among smartphone users aged 18 years or more who feel comfortable sharing their data with advertisers in the United States, over half of the respondents aged up to ** (** percent) said they were willing to share information about their interests. The same age group also indicated willingness to share their shopping habits at 35 percent.

  6. d

    Resources for All Users

    • catalog.data.gov
    • datasets.ai
    Updated Mar 14, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    data.wa.gov (2025). Resources for All Users [Dataset]. https://catalog.data.gov/dataset/resources-for-all-users
    Explore at:
    Dataset updated
    Mar 14, 2025
    Dataset provided by
    data.wa.gov
    Description

    This page pulls together resources for various types of data.wa.gov users, including developers, publishers and data users.

  7. Data associated with the figures in the GLIMPSE 1.1 Users' Guide

    • catalog.data.gov
    • datasets.ai
    Updated Apr 6, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. EPA Office of Research and Development (ORD) (2024). Data associated with the figures in the GLIMPSE 1.1 Users' Guide [Dataset]. https://catalog.data.gov/dataset/data-associated-with-the-figures-in-the-glimpse-1-1-users-guide
    Explore at:
    Dataset updated
    Apr 6, 2024
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    The GLIMPSE Users' Guide includes many figures, including those that illustrate the tutorials and key results from the Reference Case scenario. This dataset includes the underlying data depicted in those figures.

  8. Users data

    • kaggle.com
    Updated Jul 27, 2017
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    karthickveerakumar (2017). Users data [Dataset]. https://www.kaggle.com/karthickveerakumar/users-data/metadata
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 27, 2017
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    karthickveerakumar
    Description

    Context

    There's a story behind every dataset and here's your opportunity to share yours.

    Content

    What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

    Acknowledgements

    We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

    Inspiration

    Your data will be in front of the world's largest data science community. What questions do you want to see answered?

  9. g

    MTA Open Data User Personas | gimi9.com

    • gimi9.com
    Updated Jul 6, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2023). MTA Open Data User Personas | gimi9.com [Dataset]. https://gimi9.com/dataset/ny_a9g7-3ygf/
    Explore at:
    Dataset updated
    Jul 6, 2023
    License

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

    Description

    User personas are a human-centered design tool that help open data program administrators design programs offerings for the full community open data users for maximum reach and impact. User personas help keep real people in mind when designing program offerings and can identify user segments in the open data community that have the potential to use open data to help solve problems. The Metropolitan Transportation Authority (MTA) is excited to share our open data user personas which were designed in collaboration with our existing open data community through multiple stakeholder workshops.

  10. Agency Data on User Facilities

    • data.nasa.gov
    • gimi9.com
    • +4more
    Updated Mar 31, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    nasa.gov (2025). Agency Data on User Facilities [Dataset]. https://data.nasa.gov/dataset/agency-data-on-user-facilities
    Explore at:
    Dataset updated
    Mar 31, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    The purpose of the Aerospace Technical Facility Inventory is to facilitate the sharing of specialized capabilities within the aerospace research/engineering community primarily within NASA, but also throughout the nation and the entire world. A second use is to assist in answering questions regarding NASA capabilities for future missions or various alternative scenarios regarding mission support to help the Agency maintain the right set of assets.

  11. d

    Social Media Data | 30M+ YouTube Creators User Profile Dataset | 30M+...

    • datarade.ai
    Updated Oct 12, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Webautomation (2023). Social Media Data | 30M+ YouTube Creators User Profile Dataset | 30M+ Influencers | Web-Scraped | GDPR compliant [Dataset]. https://datarade.ai/data-products/webautomation-youtube-creators-profile-dataset-10m-influen-webautomation
    Explore at:
    .json, .csv, .xls, .txtAvailable download formats
    Dataset updated
    Oct 12, 2023
    Dataset authored and provided by
    Webautomation
    Area covered
    Guyana, Virgin Islands (British), Lithuania, Belize, Venezuela (Bolivarian Republic of), Bermuda, Gibraltar, Switzerland, Estonia, Portugal
    Description

    Extensive Creator Coverage: Our dataset includes a diverse range of YouTube content creators, spanning various genres, subscriber counts, and regions. Access information on creators from a wide spectrum of content categories.

    Creator Profiles: Explore detailed creator profiles, including biographies, subscriber counts, video counts, and contact information.

    Customizable Data Delivery: The dataset is available in flexible formats, such as CSV, JSON, or API integration, allowing seamless integration with your existing data infrastructure. Customize the data to meet your specific research and analysis needs.

  12. Data from: Login Data Set for Risk-Based Authentication

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Jun 30, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stephan Wiefling; Stephan Wiefling; Paul René Jørgensen; Paul René Jørgensen; Sigurd Thunem; Sigurd Thunem; Luigi Lo Iacono; Luigi Lo Iacono (2022). Login Data Set for Risk-Based Authentication [Dataset]. http://doi.org/10.5281/zenodo.6782156
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 30, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Stephan Wiefling; Stephan Wiefling; Paul René Jørgensen; Paul René Jørgensen; Sigurd Thunem; Sigurd Thunem; Luigi Lo Iacono; Luigi Lo Iacono
    License

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

    Description

    Login Data Set for Risk-Based Authentication

    Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

    This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

    The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

    WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

    Overview

    The data set contains the following features related to each login attempt on the SSO:

    FeatureData TypeDescriptionRange or Example
    IP AddressStringIP address belonging to the login attempt0.0.0.0 - 255.255.255.255
    CountryStringCountry derived from the IP addressUS
    RegionStringRegion derived from the IP addressNew York
    CityStringCity derived from the IP addressRochester
    ASNIntegerAutonomous system number derived from the IP address0 - 600000
    User Agent StringStringUser agent string submitted by the clientMozilla/5.0 (Windows NT 10.0; Win64; ...
    OS Name and VersionStringOperating system name and version derived from the user agent stringWindows 10
    Browser Name and VersionStringBrowser name and version derived from the user agent stringChrome 70.0.3538
    Device TypeStringDevice type derived from the user agent string(mobile, desktop, tablet, bot, unknown)1
    User IDIntegerIdenfication number related to the affected user account[Random pseudonym]
    Login TimestampIntegerTimestamp related to the login attempt[64 Bit timestamp]
    Round-Trip Time (RTT) [ms]IntegerServer-side measured latency between client and server1 - 8600000
    Login SuccessfulBooleanTrue: Login was successful, False: Login failed(true, false)
    Is Attack IPBooleanIP address was found in known attacker data set(true, false)
    Is Account TakeoverBooleanLogin attempt was identified as account takeover by incident response team of the online service(true, false)

    Data Creation

    As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

    The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

    • The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

    • The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

    • The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

    Regarding the Data Values

    Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

    You can recognize them by the following values:

    • ASNs with values >= 500.000

    • IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

    Study Reproduction

    Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

    The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

    See RESULTS.md for more details.

    Ethics

    By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

    The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

    Publication

    You can find more details on our conducted study in the following journal article:

    Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022)
    Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono.
    ACM Transactions on Privacy and Security

    Bibtex

    @article{Wiefling_Pump_2022,
     author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi},
     title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}},
     journal = {{ACM} {Transactions} on {Privacy} and {Security}},
     doi = {10.1145/3546069},
     publisher = {ACM},
     year  = {2022}
    }

    License

    This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

    Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

    1. Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

  13. Dishonest Internet users Dataset Data Set

    • kaggle.com
    Updated May 27, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Anala Keshava (2020). Dishonest Internet users Dataset Data Set [Dataset]. https://www.kaggle.com/analakeshava/dishonest-internet-users-dataset-data-set/tasks
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 27, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Anala Keshava
    Description

    The dataset is taken from UCI machine learning repository. The link for the dataset is https://archive.ics.uci.edu/ml/datasets/Dishonest+Internet+users+Dataset . This dataset was used to test an architecture based on a trust model capable to evaluate the trustworthiness of users interacting in pervasive environments.Pervasive environment is an environment which inlcudes several devices.

  14. Russia Active Internet Users: % of Population: NC: Republic of Dagestan

    • ceicdata.com
    Updated Jan 15, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2025). Russia Active Internet Users: % of Population: NC: Republic of Dagestan [Dataset]. https://www.ceicdata.com/en/russia/share-of-active-internet-users-by-region/active-internet-users--of-population-nc-republic-of-dagestan
    Explore at:
    Dataset updated
    Jan 15, 2025
    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, 2014 - Dec 1, 2023
    Area covered
    Russia
    Variables measured
    Internet Statistics
    Description

    Active Internet Users: % of Population: NC: Republic of Dagestan data was reported at 96.200 % in 2024. This records an increase from the previous number of 95.600 % for 2023. Active Internet Users: % of Population: NC: Republic of Dagestan data is updated yearly, averaging 83.600 % from Dec 2014 (Median) to 2024, with 11 observations. The data reached an all-time high of 96.200 % in 2024 and a record low of 46.300 % in 2014. Active Internet Users: % of Population: NC: Republic of Dagestan data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TH001: Share of Active Internet Users: by Region.

  15. Tunisia Internet: No of Users

    • ceicdata.com
    Updated Dec 15, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2024). Tunisia Internet: No of Users [Dataset]. https://www.ceicdata.com/en/tunisia/internet-statistics/internet-no-of-users
    Explore at:
    Dataset updated
    Dec 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, 2002 - Dec 1, 2013
    Area covered
    Tunisia
    Variables measured
    Internet Statistics
    Description

    Tunisia Internet: Number of Users data was reported at 5,660,000.000 Person in 2013. This records an increase from the previous number of 4,470,000.000 Person for 2012. Tunisia Internet: Number of Users data is updated yearly, averaging 1,508,550.000 Person from Dec 2000 (Median) to 2013, with 14 observations. The data reached an all-time high of 5,660,000.000 Person in 2013 and a record low of 250,000.000 Person in 2000. Tunisia Internet: Number of Users data remains active status in CEIC and is reported by Ministry of Information and Communication Technologies. The data is categorized under Global Database’s Tunisia – Table TN.TB001: Internet Statistics.

  16. B

    Brazil No of Internet User: Years of Studies: South: Female: 4 to 7 Years

    • ceicdata.com
    Updated Apr 24, 2021
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2021). Brazil No of Internet User: Years of Studies: South: Female: 4 to 7 Years [Dataset]. https://www.ceicdata.com/en/brazil/number-of-internet-user-by-years-of-studies
    Explore at:
    Dataset updated
    Apr 24, 2021
    Dataset provided by
    CEICdata.com
    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, 2016 - Dec 1, 2017
    Area covered
    Brazil
    Variables measured
    Internet Statistics
    Description

    No of Internet User: Years of Studies: South: Female: 4 to 7 Years data was reported at 2,068.275 Person th in 2017. This records an increase from the previous number of 1,680.216 Person th for 2016. No of Internet User: Years of Studies: South: Female: 4 to 7 Years data is updated yearly, averaging 1,874.245 Person th from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 2,068.275 Person th in 2017 and a record low of 1,680.216 Person th in 2016. No of Internet User: Years of Studies: South: Female: 4 to 7 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Transport and Telecommunication Sector – Table BR.TB008: Number of Internet User: by Years of Studies.

  17. Worldwide digital population 2025

    • statista.com
    • ai-chatbox.pro
    Updated Apr 1, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Worldwide digital population 2025 [Dataset]. https://www.statista.com/statistics/617136/digital-population-worldwide/
    Explore at:
    Dataset updated
    Apr 1, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 2025
    Area covered
    World
    Description

    As of February 2025, 5.56 billion individuals worldwide were internet users, which amounted to 67.9 percent of the global population. Of this total, 5.24 billion, or 63.9 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 20254. In The Netherlands, Norway and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide – over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a ten percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most significant usage penetration, 98 percent. In comparison, the worldwide average for the age group 15–24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

  18. P

    Portugal Internet users - data, chart | TheGlobalEconomy.com

    • theglobaleconomy.com
    csv, excel, xml
    Updated May 18, 2015
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Globalen LLC (2015). Portugal Internet users - data, chart | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/Portugal/Internet_users/
    Explore at:
    csv, excel, xmlAvailable download formats
    Dataset updated
    May 18, 2015
    Dataset authored and provided by
    Globalen LLC
    License

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

    Time period covered
    Dec 31, 1990 - Dec 31, 2023
    Area covered
    Portugal
    Description

    Portugal: Internet users, percent of population: The latest value from 2023 is 85.79 percent, an increase from 84.5 percent in 2022. In comparison, the world average is 87.67 percent, based on data from 59 countries. Historically, the average for Portugal from 1990 to 2023 is 39.64 percent. The minimum value, 0 percent, was reached in 1990 while the maximum of 85.79 percent was recorded in 2023.

  19. A web tracking data set of online browsing behavior of 2,148 users

    • zenodo.org
    • data.niaid.nih.gov
    application/gzip, txt +1
    Updated May 14, 2021
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Juhi Kulshrestha; Juhi Kulshrestha; Marcos Oliveira; Marcos Oliveira; Orkut Karacalik; Denis Bonnay; Claudia Wagner; Orkut Karacalik; Denis Bonnay; Claudia Wagner (2021). A web tracking data set of online browsing behavior of 2,148 users [Dataset]. http://doi.org/10.5281/zenodo.4757574
    Explore at:
    zip, txt, application/gzipAvailable download formats
    Dataset updated
    May 14, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Juhi Kulshrestha; Juhi Kulshrestha; Marcos Oliveira; Marcos Oliveira; Orkut Karacalik; Denis Bonnay; Claudia Wagner; Orkut Karacalik; Denis Bonnay; Claudia Wagner
    License

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

    Description

    This anonymized data set consists of one month's (October 2018) web tracking data of 2,148 German users. For each user, the data contains the anonymized URL of the webpage the user visited, the domain of the webpage, category of the domain, which provides 41 distinct categories. In total, these 2,148 users made 9,151,243 URL visits, spanning 49,918 unique domains. For each user in our data set, we have self-reported information (collected via a survey) about their gender and age.

    We acknowledge the support of Respondi AG, which provided the web tracking and survey data free of charge for research purposes, with special thanks to François Erner and Luc Kalaora at Respondi for their insights and help with data extraction.

    The data set is analyzed in the following paper:

    • Kulshrestha, J., Oliveira, M., Karacalik, O., Bonnay, D., Wagner, C. "Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data." Proceedings of the International AAAI Conference on Web and Social Media. 2021. https://arxiv.org/abs/2012.15112.

    The code used to analyze the data is also available at https://github.com/gesiscss/web_tracking.

    If you use data or code from this repository, please cite the paper above and the Zenodo link.

  20. C

    Open Data User Guide

    • data.cnra.ca.gov
    • data.ca.gov
    • +5more
    Updated Apr 18, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    California Natural Resources Agency (2022). Open Data User Guide [Dataset]. https://data.cnra.ca.gov/dataset/open-data-user-guide
    Explore at:
    html, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Apr 18, 2022
    Dataset provided by
    CA Nature Organization
    Authors
    California Natural Resources Agency
    License

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

    Description

    This guide will introduce the open data resources available in the CA Nature website and familiarize you with key features and capabilities of the site.

    CA Nature is an online Geographic Information System (or GIS), that collects a suite of publicly accessible interactive digital mapping tools and data.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Statista (2025). Global online users concerns about misuse of personal data Q3 2024, by country [Dataset]. https://www.statista.com/statistics/1382850/concerns-personal-data-misuse-by-country/
Organization logo

Global online users concerns about misuse of personal data Q3 2024, by country

Explore at:
Dataset updated
Jun 23, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Worldwide
Description

As of the third quarter of 2024, **** percent of internet users in Spain stated being concerned about companies' misuse of their personal data. Portugal ranked second, with ** percent, while Brazil followed, with around **** percent of online users worried about improper use of their personal online data.

Search
Clear search
Close search
Google apps
Main menu