100+ datasets found
  1. Share of U.S. mobile website traffic 2015-2023

    • statista.com
    Updated Mar 25, 2024
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    Statista (2024). Share of U.S. mobile website traffic 2015-2023 [Dataset]. https://www.statista.com/statistics/683082/share-of-website-traffic-coming-from-mobile-devices-usa/
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    Dataset updated
    Mar 25, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.

  2. g

    Food Hygiene Ratings website traffic

    • fsadata.github.io
    • cloud.csiss.gmu.edu
    • +2more
    csv
    Updated Jan 16, 2020
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    (2020). Food Hygiene Ratings website traffic [Dataset]. https://fsadata.github.io/food-hygiene-ratings-website-traffic/
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    csvAvailable download formats
    Dataset updated
    Jan 16, 2020
    Description

    A breakdown of the Food Hygiene Ratings website traffic data showing total number of visits, unique visitors and page views.

  3. Data from: Analysis of the Quantitative Impact of Social Networks General...

    • figshare.com
    • produccioncientifica.ucm.es
    doc
    Updated Oct 14, 2022
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    David Parra; Santiago Martínez Arias; Sergio Mena Muñoz (2022). Analysis of the Quantitative Impact of Social Networks General Data.doc [Dataset]. http://doi.org/10.6084/m9.figshare.21329421.v1
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    docAvailable download formats
    Dataset updated
    Oct 14, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    David Parra; Santiago Martínez Arias; Sergio Mena Muñoz
    License

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

    Description

    General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
    Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

    Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

  4. DoorDash.com: web traffic worldwide 2025

    • statista.com
    Updated Jul 15, 2025
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    Statista (2025). DoorDash.com: web traffic worldwide 2025 [Dataset]. https://www.statista.com/statistics/1452396/doordash-web-traffic-worldwide/
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    Dataset updated
    Jul 15, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 2025
    Area covered
    United States, Worldwide
    Description

    In June 2025, DoorDash's website, doordash.com, had just under 72 million visitors globally, recording a bounce rate of approximately 34.2 percent. For comparison, web traffic figures of UberEats show lower monthly visits.

  5. Most visited price comparison websites in Hungary 2021, by traffic share

    • statista.com
    Updated Apr 13, 2023
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    Statista (2023). Most visited price comparison websites in Hungary 2021, by traffic share [Dataset]. https://www.statista.com/statistics/1312875/hungary-traffic-share-of-the-most-popular-price-comparison-websites/
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    Dataset updated
    Apr 13, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    Hungary
    Description

    Árukereső was the most popular price comparison portal in Hungary in 2021, based on the traffic share measured by SimilarWeb. Árgép was the second most visited price comparison site over the same time period.

  6. Popular online marketplace websites annual traffic growth Australia 2025

    • statista.com
    Updated Apr 11, 2025
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    Statista (2025). Popular online marketplace websites annual traffic growth Australia 2025 [Dataset]. https://www.statista.com/statistics/1609800/australia-online-marketplace-websites-traffic-growth/
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    Dataset updated
    Apr 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 2025
    Area covered
    Australia
    Description

    Across popular online marketplace websites visited by users in Australia in February 2025, temu.com registered the highest growth in its website traffic compared to the previous year, with an annual growth of over 56 percent. In comparison, ebay.com.au saw a decrease in its website traffic compared to the previous year, with an annual decrease of around 11.9 percent.

  7. Share of global mobile website traffic 2015-2024

    • statista.com
    • ai-chatbox.pro
    Updated Jan 28, 2025
    + more versions
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    Statista (2025). Share of global mobile website traffic 2015-2024 [Dataset]. https://www.statista.com/statistics/277125/share-of-website-traffic-coming-from-mobile-devices/
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    Dataset updated
    Jan 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

  8. f

    Summary of results comparing Google Analytics and SimilarWeb for total...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). Summary of results comparing Google Analytics and SimilarWeb for total visits, unique visitors, bounce rate, and average session duration. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t006
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    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

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

    Description

    Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

  9. f

    Comparison of definitions of total visits, unique visitors, bounce rate, and...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

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

    Description

    Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb.

  10. DataForSEO Labs API for keyword research and search analytics, real-time...

    • datarade.ai
    .json
    Updated Jun 4, 2021
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    DataForSEO (2021). DataForSEO Labs API for keyword research and search analytics, real-time data for all Google locations and languages [Dataset]. https://datarade.ai/data-products/dataforseo-labs-api-for-keyword-research-and-search-analytics-dataforseo
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    .jsonAvailable download formats
    Dataset updated
    Jun 4, 2021
    Dataset provided by
    Authors
    DataForSEO
    Area covered
    Armenia, Morocco, Mauritania, Micronesia (Federated States of), Cocos (Keeling) Islands, Tokelau, Isle of Man, Korea (Democratic People's Republic of), Azerbaijan, Kenya
    Description

    DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

    • Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

    Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

    You will find well-rounded ways to scout the competitors:

    • Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

    All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

    The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

    We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

    We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

  11. Comparison of traffic sources to Slack and Salesforce webpages 2019

    • statista.com
    Updated Feb 21, 2022
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    Statista (2022). Comparison of traffic sources to Slack and Salesforce webpages 2019 [Dataset]. https://www.statista.com/statistics/1025255/worldwide-slack-salesforce-traffic-source-to-webpage/
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    Dataset updated
    Feb 21, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 2019
    Area covered
    Worldwide
    Description

    This statistic shows a comparison of webpage traffic sources of Slack and Salesforce in April 2019. According to data collected by GP Bullhound, ninety-six percent of Slack's webpage traffic during the measured period was direct, compared to Salesforce's more mixed traffic strategy.

  12. Network traffic datasets created by Single Flow Time Series Analysis

    • zenodo.org
    • data.niaid.nih.gov
    csv, pdf
    Updated Jul 11, 2024
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    Josef Koumar; Josef Koumar; Karel Hynek; Karel Hynek; Tomáš Čejka; Tomáš Čejka (2024). Network traffic datasets created by Single Flow Time Series Analysis [Dataset]. http://doi.org/10.5281/zenodo.8035724
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    csv, pdfAvailable download formats
    Dataset updated
    Jul 11, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Josef Koumar; Josef Koumar; Karel Hynek; Karel Hynek; Tomáš Čejka; Tomáš Čejka
    License

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

    Description

    Network traffic datasets created by Single Flow Time Series Analysis

    Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:

    J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.

    This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf

    In the following table is a description of each dataset file:

    File nameDetection problemCitation of original raw dataset
    botnet_binary.csv Binary detection of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.
    botnet_multiclass.csv Multi-class classification of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.
    cryptomining_design.csvBinary detection of cryptomining; the design part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022
    cryptomining_evaluation.csv Binary detection of cryptomining; the evaluation part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022
    dns_malware.csv Binary detection of malware DNS Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021.
    doh_cic.csv Binary detection of DoH

    Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020

    doh_real_world.csv Binary detection of DoH Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022
    dos.csv Binary detection of DoS Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.
    edge_iiot_binary.csv Binary detection of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.
    edge_iiot_multiclass.csvMulti-class classification of IoT malwareMohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.
    https_brute_force.csvBinary detection of HTTPS Brute ForceJan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020
    ids_cic_binary.csvBinary detection of intrusion in IDSIman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.
    ids_cic_multiclass.csv Multi-class classification of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.
    ids_unsw_nb_15_binary.csv Binary detection of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.
    ids_unsw_nb_15_multiclass.csv Multi-class classification of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.
    iot_23.csv Binary detection of IoT malware Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23
    ton_iot_binary.csv Binary detection of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021
    ton_iot_multiclass.csv Multi-class classification of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021
    tor_binary.csv Binary detection of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.
    tor_multiclass.csv Multi-class classification of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.
    vpn_iscx_binary.csv Binary detection of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.
    vpn_iscx_multiclass.csv Multi-class classification of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.
    vpn_vnat_binary.csv Binary detection of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022
    vpn_vnat_multiclass.csvMulti-class classification of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022

  13. f

    Comparison of user, site, and network-centric approaches to web analytics...

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). Comparison of user, site, and network-centric approaches to web analytics data collection showing advantages, disadvantages, and examples of each approach at the time of the study. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

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

    Description

    Comparison of user, site, and network-centric approaches to web analytics data collection showing advantages, disadvantages, and examples of each approach at the time of the study.

  14. Wolt.com: web traffic worldwide 2025

    • statista.com
    Updated Jul 4, 2025
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    Statista (2025). Wolt.com: web traffic worldwide 2025 [Dataset]. https://www.statista.com/statistics/1559777/wolt-web-traffic-worldwide/
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    Dataset updated
    Jul 4, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2025
    Area covered
    Finland, Worldwide
    Description

    In January 2025, Wolt's website, wolt.com, had just under 11 million visitors globally, recording a bounce rate of approximately 32 percent. Wolt was acquired by DoorDash in May 2022. For comparison, web traffic figures of DoorDash show nearly 72 monthly visitors.

  15. Traffic Counts in the United States

    • hub.arcgis.com
    Updated Jun 21, 2016
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    Esri (2016). Traffic Counts in the United States [Dataset]. https://hub.arcgis.com/maps/esri::traffic-counts-in-the-united-states/about
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    Dataset updated
    Jun 21, 2016
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Important Note: This item is in mature support as of June 2023 and will be retired in December 2025.This map shows traffic counts in the United States, collected through 2022 in a multiscale map. Traffic counts are widely used for site selection by real estate firms and franchises. Traffic counts are also used by departments of transportation for highway funding. This map is best viewed at large scales where you can click on each point to access up to five different traffic counts over time. At medium to small scales, comparisons along major roads are possible. The Business Basemap has been added to provide context at medium and small scales. It shows the location of businesses in the United States and helps to understand where and why traffic counts are collected and used. The pop-up is configured to display the following information:The most recent traffic countThe street name where the count was collectedThey type of count that was taken. See the methodology document for definitions of count types such as AADT - Average Annual Daily Traffic. Traffic Counts seasonally adjusted to represent the average day of the year. AADT counts represent counts taken Sunday—Saturday.A graph displaying up to five traffic counts taken at the same location over time. Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

  16. Leading meal kit websites by growth in traffic in the U.K. 2024

    • statista.com
    • ai-chatbox.pro
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    Statista (2025). Leading meal kit websites by growth in traffic in the U.K. 2024 [Dataset]. https://www.statista.com/statistics/1272993/fastest-growing-meal-delivery-and-recipe-box-websites-in-the-uk/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 2024
    Area covered
    United Kingdom
    Description

    As of April 2024, the website pastaevangelists.com was the fastest-growing meal delivery website in the United Kingdom (U.K.) in terms of online traffic. That year, the website had an **-percent increase in its online visibility score in comparison to the previous year. The website cookfood.net was the second fastest-growing website, with a year-on-year increase of ** percent in its online traffic score.

  17. Distribution of internet traffic Thailand 2024, by device

    • statista.com
    • ai-chatbox.pro
    Updated Jul 9, 2025
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    Statista (2025). Distribution of internet traffic Thailand 2024, by device [Dataset]. https://www.statista.com/statistics/804041/share-of-internet-traffic-by-device-thailand/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2024
    Area covered
    Thailand
    Description

    According to a report published by DataReportal, as of December 2024, mobile phones had the highest share of web traffic at ***** percent in Thailand. By comparison, the share of web traffic on laptops and desktop computers was around ** percent.

  18. E

    ProtonMail vs Gmail Statistics By Market Analysis, Website Traffic,...

    • electroiq.com
    Updated Jun 23, 2025
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    Electro IQ (2025). ProtonMail vs Gmail Statistics By Market Analysis, Website Traffic, Customers, Price And Storage (2025) [Dataset]. https://electroiq.com/stats/protonmail-vs-gmail-statistics/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Electro IQ
    License

    https://electroiq.com/privacy-policyhttps://electroiq.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Â Introduction

    ProtonMail vs Gmail Statistics: ProtonMail and Gmail are both popular email services, known for their unique features, as well as several advantages and disadvantages. Proton Mail is a Swiss end-to-end encrypted email service operated by Proton AG, which also operates Proton VPN, Proton Drive, Proton Calendar, Proton Pass and Proton Wallet. In contrast, Gmail is a widely used email service provided by Google, offering a webmail interface accessible through a web browser and also available through the official mobile application.

    This article provides a current analysis of both email platforms, drawing on various insights, including a basic comparison, overall market analysis, user base, usability, storage, pricing, and more.

  19. Average Annual Daily Traffic (AADT)

    • caliper.com
    cdf, dwg, dxf, gdb +9
    Updated Jul 25, 2024
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    Caliper Corporation (2024). Average Annual Daily Traffic (AADT) [Dataset]. https://www.caliper.com/mapping-software-data/aadt-traffic-count-data.htm
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    postgresql, postgis, sdo, geojson, shp, cdf, kml, kmz, dxf, dwg, ntf, sql server mssql, gdbAvailable download formats
    Dataset updated
    Jul 25, 2024
    Dataset authored and provided by
    Caliper Corporationhttp://www.caliper.com/
    License

    https://www.caliper.com/license/maptitude-license-agreement.htmhttps://www.caliper.com/license/maptitude-license-agreement.htm

    Time period covered
    2024
    Area covered
    United States
    Description

    Average Annual Daily Traffic data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain data on the total volume of vehicle traffic on a highway or road for a year divided by 365 days.

  20. d

    Valuation Office Website Traffic and Stats

    • datasalsa.com
    csv, json
    Updated May 19, 2025
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    Tailte Éireann – Surveying (2025). Valuation Office Website Traffic and Stats [Dataset]. https://datasalsa.com/dataset/?catalogue=data.gov.ie&name=https-valoff-ie-en-open-data-data-sets-valuation-office-website-traffic-and-stats-csv
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    May 19, 2025
    Dataset authored and provided by
    Tailte Éireann – Surveying
    License

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

    Time period covered
    May 19, 2025
    Description

    Valuation Office Website Traffic and Stats . Published by Tailte Éireann – Surveying. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This dataset provides the number of users to the Valuation Office website....

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Statista (2024). Share of U.S. mobile website traffic 2015-2023 [Dataset]. https://www.statista.com/statistics/683082/share-of-website-traffic-coming-from-mobile-devices-usa/
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Share of U.S. mobile website traffic 2015-2023

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Dataset updated
Mar 25, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States
Description

As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.

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