33 datasets found
  1. f

    YouTube Dataset on Mobile Streaming for Internet Traffic Modeling, Network...

    • figshare.com
    txt
    Updated Apr 14, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Frank Loh; Florian Wamser; Fabian Poignée; Stefan Geißler; Tobias Hoßfeld (2022). YouTube Dataset on Mobile Streaming for Internet Traffic Modeling, Network Management, and Streaming Analysis [Dataset]. http://doi.org/10.6084/m9.figshare.19096823.v2
    Explore at:
    txtAvailable download formats
    Dataset updated
    Apr 14, 2022
    Dataset provided by
    figshare
    Authors
    Frank Loh; Florian Wamser; Fabian Poignée; Stefan Geißler; Tobias Hoßfeld
    License

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

    Area covered
    YouTube
    Description

    Streaming is by far the predominant type of traffic in communication networks. With thispublic dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3G/4G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

  2. d

    Swash Web Browsing Clickstream Data - 1.5M Worldwide Users - GDPR Compliant

    • datarade.ai
    .csv, .xls
    Updated Jun 27, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Swash (2023). Swash Web Browsing Clickstream Data - 1.5M Worldwide Users - GDPR Compliant [Dataset]. https://datarade.ai/data-products/swash-blockchain-bitcoin-and-web3-enthusiasts-swash
    Explore at:
    .csv, .xlsAvailable download formats
    Dataset updated
    Jun 27, 2023
    Dataset authored and provided by
    Swash
    Area covered
    Saint Vincent and the Grenadines, Monaco, Latvia, Jordan, India, Uzbekistan, Belarus, Jamaica, Liechtenstein, Russian Federation
    Description

    Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

    Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

    User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

    Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

    GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

    Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

    High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

    Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

    Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

  3. Network Traffic Android Malware

    • kaggle.com
    zip
    Updated Sep 12, 2019
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Christian Urcuqui (2019). Network Traffic Android Malware [Dataset]. https://www.kaggle.com/datasets/xwolf12/network-traffic-android-malware
    Explore at:
    zip(116603 bytes)Available download formats
    Dataset updated
    Sep 12, 2019
    Authors
    Christian Urcuqui
    Description

    Introduction

    Android is one of the most used mobile operating systems worldwide. Due to its technological impact, its open-source code and the possibility of installing applications from third parties without any central control, Android has recently become a malware target. Even if it includes security mechanisms, the last news about malicious activities and Android´s vulnerabilities point to the importance of continuing the development of methods and frameworks to improve its security.

    To prevent malware attacks, researches and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.

    In this work, we propose to consider some network layer features as the basis for machine learning models that can successfully detect malware applications, using open datasets from the research community.

    Content

    This dataset is based on another dataset (DroidCollector) where you can get all the network traffic in pcap files, in our research we preprocessed the files in order to get network features that are illustrated in the next article:

    López, C. C. U., Villarreal, J. S. D., Belalcazar, A. F. P., Cadavid, A. N., & Cely, J. G. D. (2018, May). Features to Detect Android Malware. In 2018 IEEE Colombian Conference on Communications and Computing (COLCOM) (pp. 1-6). IEEE.

    Acknowledgements

    Cao, D., Wang, S., Li, Q., Cheny, Z., Yan, Q., Peng, L., & Yang, B. (2016, August). DroidCollector: A High Performance Framework for High Quality Android Traffic Collection. In Trustcom/BigDataSE/I SPA, 2016 IEEE (pp. 1753-1758). IEEE

  4. Z

    Data from: Energy-Saving Strategies for Mobile Web Apps and their...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Mar 13, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Michael Felderer (2023). Energy-Saving Strategies for Mobile Web Apps and their Measurement: Results from a Decade of Research - Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7698282
    Explore at:
    Dataset updated
    Mar 13, 2023
    Dataset provided by
    Benedikt Dornauer
    Michael Felderer
    License

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

    Description

    In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our devices, but also make a significant contribution to energy conservation efforts. For example, if we could save only 5% of the energy used by web apps, we estimate that it would be enough to shut down one of the nuclear reactors in Fukushima. This paper presents a comprehensive overview of energy-saving experiments and related approaches for mobile web apps, relevant for researchers and practitioners. To achieve this objective, we conducted a systematic literature review and identified 44 primary studies for inclusion. Through the mapping and analysis of scientific papers, this work contributes: (1) an overview of the energy-draining aspects of mobile web apps, (2) a comprehensive description of the methodology used for the energy-saving experiments, and (3) a categorization and synthesis of various energy-saving approaches.

  5. How to choose the right product for your client?

    • kaggle.com
    Updated Mar 23, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Julia Beyers (2020). How to choose the right product for your client? [Dataset]. https://www.kaggle.com/juliabeyers/how-to-choose-the-right-product-for-your-client/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 23, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Julia Beyers
    Description

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2F186cf4f6172ca2c696819b7b09931bd3%2Fimage3.jpg?generation=1584955857130173&alt=media" alt="">

    The presence of business in the digital space is a must now. Indeed, there’s hardly any company, be it a small startup or an international corporation, that wouldn’t be available online. For this, the company may use one of two options — to develop an app or a website, or both.

    In the case of a limited budget, business owners often have to make a choice. Thus, considering that mobile traffic bypassed the desktop’s in 2016 and continues to grow, it becomes obvious that the business should become accessible and convenient for smartphone users. But what is better a responsive website or a mobile application?

    Entrepreneurs often turn to development companies to ask this question. Lacking sufficient knowledge, they hope to get answers to their questions from people with experience in this field. So, we decided to compile a guide that will give you clear and understandable information.

    Mobile app

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2F0541557795519f24d812f78dfb51867e%2Fimage4.png?generation=1584955894277647&alt=media" alt="">

    Let's look at the stats. It will help you understand why a mobile app may be the obvious choice for your client.

    In 2019, smartphone users installed about 204 billion(!) applications on their devices. On average, this is more than 26 applications per inhabitant of the planet Earth. And if this is not enough evidence, here’s one more point. The expected revenue of mobile applications will be $189 billion in 2020.

    It sounds impressive, but this does not mean that a mobile application is something indispensable for every business. Not at all. Let's go through the pros and cons of a mobile application and try to understand when it is needed.

    Pros

    • A new level of interaction. Mobile applications are a more convenient method of interaction. They load and process content faster. One more useful feature is notifications. Perhaps, applications are the best way to inform users about new updates, promotions, and other news (who will read long letters in the mail?).
    • Personalized targeting. Mobile applications are ideal for products or services that need to be used on an ongoing basis. The options like creating accounts, entering profile information, etc., make applications more personalized than websites. All this allows the business to target their audience more accurately without wasting money.
    • Offline usage. That’s another major advantage. Applications can provide users with access to content without an internet connection.

    Cons

    • Development costs. In order to reach the maximum audience with a mobile app, it is necessary to cover two main operating systems — iOS and Android. Development for each OS can be too expensive for small business owners and they will have to make difficult choices. The way out of this situation is cross-platform development. Why? Because there’s no need to guess which platform targets prefer using — iOS or Android. Instead, you create just one app that runs seamlessly on both platforms.

    • Maintenance. The application is a technical product that needs constant support. Upgrades should be carried out in a timely manner. Often, users need to personally update applications by downloading a new version, which is annoying. Regular bug-fixing for various devices (smartphones, tablets) and different operating systems might be a real problem. Plus, any update should be confirmed by the store where the application is placed.

    • Suitable for businesses that provide interactive and personalized content (refers to all lifestyle and healthcare solutions), require regular app usage (for instance, to-do lists), rely on visual interaction and so on. For games, like Angry Birds, creating an app is also a wise choice.

    Website

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2Fd4f5bf1fdd0d0e65fae38c7251f56f13%2Fimage1.jpg?generation=1584955919738648&alt=media" alt="">

    In order to be convenient for users of mobile devices, a website should be responsive. We want to make an emphasis on this since it is critically important. Most of the traffic on the Internet comes from mobile devices, so your website should be adaptable, or in other words, mobile-friendly. If a mobile user needs to zoom in all the necessary elements and text to see something, they will immediately quit your website.

    On the other hand, a responsive website has the following benefits.

    Pros

    • Maintenance. Maintaining a website is less costly. When compared to applications where the user mu...
  6. g

    The major statistical data of natural referencing | gimi9.com

    • gimi9.com
    Updated Nov 30, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2024). The major statistical data of natural referencing | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_65f594ba5cf5f141524928b6/
    Explore at:
    Dataset updated
    Nov 30, 2024
    License

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

    Description

    This dataset gathers the most crucial SEO statistics for the year, providing an overview of the dominant trends and best practices in the field of search engine optimization. Aimed at digital marketing professionals, site owners, and SEO analysts, this collection of information serves as a guide to navigate the evolving SEO landscape with confidence and accuracy. Mode of Data Production: The statistics have been carefully selected and compiled from a variety of credible and recognized sources in the SEO industry, including research reports, web traffic data analytics, and consumer and marketing professional surveys. Each statistic was checked for reliability and relevance to current trends. Categories Included: User search behaviour: Statistics on the evolution of search modes, including voice and mobile search. Mobile Optimisation: Data on the importance of site optimization for mobile devices. Importance of Backlinks: Insights on the role of backlinks in SEO ranking and the need to prioritize quality. Content quality: Statistics highlighting the importance of relevant and engaging content for SEO. Search engine algorithms: Information on the impact of algorithm updates on SEO strategies. Usefulness of the Data: This dataset is designed to help users quickly understand current SEO dynamics and apply that knowledge in optimizing their digital marketing strategies. It provides a solid foundation for benchmarking, strategic planning, and informed decision-making in the field of SEO. Update and Accessibility: To ensure relevance and timeliness, the dataset will be regularly updated with new information and emerging trends in the SEO world.

  7. R

    Indian Traffic Sign Dataset

    • universe.roboflow.com
    zip
    Updated Sep 11, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    DataCluster Labs (2023). Indian Traffic Sign Dataset [Dataset]. https://universe.roboflow.com/datacluster-labs-agryi/indian-traffic-sign-vvx9y
    Explore at:
    zipAvailable download formats
    Dataset updated
    Sep 11, 2023
    Dataset authored and provided by
    DataCluster Labs
    License

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

    Variables measured
    Traffic Signals Bounding Boxes
    Description

    Indian Traffic Sign Image Dataset

    Datasets for Indian traffic signs

    About Dataset

    **This dataset is collected by Datacluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: s*ales@datacluster.ai* **

    This dataset is an extremely challenging set of over 2000+ original Indian Traffic Sign images captured and crowdsourced from over 400+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at DC Labs.

    Dataset Features 1. Dataset size : 2000+ 2. Captured by : Over 400+ crowdsource contributors 3. Resolution : 100% of images HD and above (1920x1080 and above) 4. Location : Captured with 400+ cities accross India 5. Diversity : Various lighting conditions like day, night, varied distances, view points etc. 6. Device used : Captured using mobile phones in 2020-2021 7. Usage : Traffic sign detection, Self-driving systems, traffic detection, sign detection, etc.

    Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

    The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

  8. Attitudes towards the internet in Mexico 2025

    • statista.com
    Updated Apr 11, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Umair Bashir (2025). Attitudes towards the internet in Mexico 2025 [Dataset]. https://www.statista.com/topics/1145/internet-usage-worldwide/
    Explore at:
    Dataset updated
    Apr 11, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Umair Bashir
    Description

    When asked about "Attitudes towards the internet", most Mexican respondents pick "It is important to me to have mobile internet access in any place" as an answer. 56 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

  9. B

    ITU World Telecommunication/ICT Indicators database

    • borealisdata.ca
    • dataone.org
    Updated Apr 13, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Union ational Telecommunication Union (ITU) (2022). ITU World Telecommunication/ICT Indicators database [Dataset]. http://doi.org/10.5683/SP3/ESWWF6
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 13, 2022
    Dataset provided by
    Borealis
    Authors
    Union ational Telecommunication Union (ITU)
    License

    https://borealisdata.ca/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.5683/SP3/ESWWF6https://borealisdata.ca/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.5683/SP3/ESWWF6

    Time period covered
    1960
    Area covered
    International
    Description

    The World Telecommunication/ICT Indicators Database contains time series data for the years 1960, 1965, 1970 and annually from 1975 to 2020 for more than 180 telecommunication/ICT statistics covering fixed-telephone networks, mobile-cellular telephone subscriptions, quality of service, Internet (including fixed- and mobile-broadband subscription data), traffic, staff, prices, revenue, investment and statistics on ICT access and use by households and individuals. Selected demographic, macroeconomic and broadcasting statistics are also included. Data are available for over 200 economies. However, it should be noted that since ITU relies primarily on official economy data, availability of data for the different indicators and years varies. Notes explaining data exceptions are also included. The data are collected from an annual questionnaire sent to official economy contacts, usually the regulatory authority or the ministry in charge of telecommunication and ICT. Additional data are obtained from reports provided by telecommunication ministries, regulators and operators and from ITU staff reports. In some cases, estimates are made by ITU staff; these are noted in the database.

  10. Data from: Population estimation from mobile network traffic metadata

    • zenodo.org
    application/gzip
    Updated Jan 24, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ghazaleh Khodabandelou; Vincent Gauthier; Vincent Gauthier; Mounim El Yacoubi; Marco Fiore; Ghazaleh Khodabandelou; Mounim El Yacoubi; Marco Fiore (2020). Population estimation from mobile network traffic metadata [Dataset]. http://doi.org/10.5281/zenodo.1037577
    Explore at:
    application/gzipAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Ghazaleh Khodabandelou; Vincent Gauthier; Vincent Gauthier; Mounim El Yacoubi; Marco Fiore; Ghazaleh Khodabandelou; Mounim El Yacoubi; Marco Fiore
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    Please cite our paper if you publish material based on those datasets

    G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata", in IEEE Trans. on Mobile Computing, 2018 (in Press). 10.1109/TMC.2018.2871156

    Abstract

    Communication-enabled devices that are physically carried by individuals are today pervasive,
    which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. We show that our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. Specifically, it achieves higher accuracy than that granted by previous equivalent solutions. In addition, our approach enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. Our results build on significant real-world mobile network metadata and relevant ground-truth information in multiple urban scenarios.

    Dataset Columns

    This dataset cover one month of data taken during the month of April 2015 for three Italian cities: Rome, Milan, Turin. The raw data has been provided during the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/innovazione/archivio/big-data-challenge-2015.html)

    1. grid_id: the coordinate of the grid can be retrieved with the shapefile of a given city
    2. date: format Y-M-D H:M:S
    4. landuse_label: the land use label has been computed by through method described in [2]
    5. presence: presence data of a given grid id as provided by the Telecom Italia Big Data Challenge
    6. population: Census population of a given grid block as defined by the Istituto nazionale di statistica (ISTAT https://www.istat.it/en/censuses) in 2011
    7. estimation: Dynamics density population estimation (in person) as the result of the method described in [1]
    8. area: surface of the "grid id" considered in km^2
    9. geometry: the shape of the area considered with the EPSG:3003 coordinate system (only with quilt)

    Note

    Due to legal constraints, we cannot share directly the original data from Telecom Italia Big Data Challenge we used to build this dataset.

    Easy access to this dataset with quilt

    Install the dataset repository:

    $ quilt install vgauthier/DynamicPopEstimate

    Use the dataset with a Panda Dataframe

    >>> from quilt.data.vgauthier import DynamicPopEstimate
    >>> import pandas as pd
    >>> df = pd.DataFrame(DynamicPopEstimate.rome())

    Use the dataset with a GeoPanda Dataframe

    >>> from quilt.data.vgauthier import DynamicPopEstimate
    >>> import geopandas as gpd
    >>> df = gpd.DataFrame(DynamicPopEstimate.rome())

    References

    [1] G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Population estimation from mobile network traffic metadata", in proc of the 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1 - 9, 2016.

    [2] A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 2017.

  11. a

    Public Traffic Restrictons Mobile Application

    • egishub-phoenix.hub.arcgis.com
    Updated Nov 29, 2018
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    City of Phoenix (2018). Public Traffic Restrictons Mobile Application [Dataset]. https://egishub-phoenix.hub.arcgis.com/datasets/public-traffic-restrictons-mobile-application/about
    Explore at:
    Dataset updated
    Nov 29, 2018
    Dataset authored and provided by
    City of Phoenix
    Description

    Web Application (Mobile Use) for Public Traffic Restriction and Emergency Closure for public consumption

  12. Attitudes towards the internet in China 2025

    • statista.com
    Updated Apr 11, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Umair Bashir (2025). Attitudes towards the internet in China 2025 [Dataset]. https://www.statista.com/topics/1145/internet-usage-worldwide/
    Explore at:
    Dataset updated
    Apr 11, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Umair Bashir
    Description

    When asked about "Attitudes towards the internet", most Chinese respondents pick "It is important to me to have mobile internet access in any place" as an answer. 48 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

  13. O

    Parking — Occupancy forecasting

    • data.qld.gov.au
    • researchdata.edu.au
    html
    Updated Jul 8, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Brisbane City Council (2025). Parking — Occupancy forecasting [Dataset]. https://www.data.qld.gov.au/dataset/parking-occupancy-forecasting
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Jul 8, 2025
    Dataset authored and provided by
    Brisbane City Council
    License

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

    Description

    This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

    The Brisbane City Council parking occupancy forecasting data is provided to be accessed by third party web or app developers to develop tools to provide Brisbane residents and visitors with likely parking availability within a paid parking area.

    The parking occupancy forecasting data is compiled using advanced analytics and machine learning to estimate paid parking availability. The solution uses parking occupancy survey data, parking meter transaction data and other traffic and environmental data.

    This dataset is linked to the open data called Parking — Meter locations. The field called MOBILE_ZONE is used to link the datasets. MOBILE_ZONE is a seven-digit mobile payment zone number that may include one or many parking meter numbers.

    Additional information on parking meters can be found on the Brisbane City Council website.

    The Brisbane City Council parking occupancy forecasting data includes parking data for all of Council’s parking meters. The data attributes used in this resource and their descriptions can be found in the Parking — Occupancy forecasting — metadata — CSV resource in this dataset.

    The Data and resources section of this dataset contains further information for this dataset.

  14. Number of internet users worldwide 2014-2029

    • statista.com
    Updated Apr 11, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista Research Department (2025). Number of internet users worldwide 2014-2029 [Dataset]. https://www.statista.com/topics/1145/internet-usage-worldwide/
    Explore at:
    Dataset updated
    Apr 11, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    World
    Description

    The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.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 internet users in countries like the Americas and Asia.

  15. w

    Live traffic information from Highways England (previously Highways Agency)

    • data.wu.ac.at
    • cloud.csiss.gmu.edu
    html, pdf, xml
    Updated Dec 10, 2015
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Highways England (2015). Live traffic information from Highways England (previously Highways Agency) [Dataset]. https://data.wu.ac.at/odso/data_gov_uk/MDQxZTkzZjktYmY0ZS00OGVjLWI3NzktNmJkYTk1ODhlZjU1
    Explore at:
    pdf, xml, htmlAvailable download formats
    Dataset updated
    Dec 10, 2015
    Dataset provided by
    Highways England
    License

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

    Description

    Live traffic information data showing traffic information on the strategic road network in England, maintained by Highways England (previously called the Highways Agency).

    Update: 10th December 2015 Following the last announcement in 2013, the transformation to a new traffic information data service is complete and a new range of data products have replaced the legacy traffic information pull data services.

    The legacy Datex 1 datasets which provided live traffic event data are no longer being updated and have been removed from data.gov. The Datex publications have been replaced by lighter xml feeds showing current and planned roadworks and unplanned traffic events across our road network. The xml feeds listed below will be supplemented by a traffic data API and JSON mobile api which will be published on our data.gov page shortly.

    Please contact us if you have any immediate concerns about these changes and we will work with you to provide a replacement service where possible.

    Alternatively, please register as a data subscriber http://www.trafficengland.com/subscribers for more information about the new Datex service.

    Update: 12th August 2013 Following a change of supplier, the NTIS system is being re-developed and will eventually replace the legacy system. Please consult the updated document which describes the NTIS Legacy DATEX II v1.0 Publisher.

    New services will be delivered in DATEX II v2 format using web services to push data to subscribers. Full details of the new services can be found in document NIS P TIH 008 available from the TIH website. Potential subscribers’, Project Managers and engineers seeking to develop a new interface are encouraged to consider using the new services.

  16. r

    The Regional Skåne SOM Survey 2015

    • researchdata.se
    • demo.researchdata.se
    Updated Feb 5, 2019
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    University of Gothenburg (2019). The Regional Skåne SOM Survey 2015 [Dataset]. http://doi.org/10.5878/002911
    Explore at:
    Dataset updated
    Feb 5, 2019
    Dataset authored and provided by
    University of Gothenburg
    Time period covered
    2015
    Area covered
    Skåne County
    Description

    This is the sixth SOM survey conducted in the region of Skåne. The questionnaire was divided into ten subject fields: Local news; Politics and democracy; To live in Skåne; Traffic and communications; Internet and mobile phones; Activities, interests and values; Denmark; Housing; Working life; Background.

    Purpose:

    Study attitudes and behaviour linked to local and regional issues

  17. Attitudes towards the internet in Japan 2025

    • statista.com
    Updated Apr 11, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Umair Bashir (2025). Attitudes towards the internet in Japan 2025 [Dataset]. https://www.statista.com/topics/1145/internet-usage-worldwide/
    Explore at:
    Dataset updated
    Apr 11, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Umair Bashir
    Description

    When asked about "Attitudes towards the internet", most Japanese respondents pick "I'm concerned that my data is being misused on the internet" as an answer. 35 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

  18. w

    Road Traffic Crashes 2012-2023 - Kenya

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    Updated May 23, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Sveta Milusheva (2024). Road Traffic Crashes 2012-2023 - Kenya [Dataset]. https://microdata.worldbank.org/index.php/catalog/6249
    Explore at:
    Dataset updated
    May 23, 2024
    Dataset authored and provided by
    Sveta Milusheva
    Time period covered
    2012 - 2023
    Area covered
    Kenya
    Description

    Abstract

    This project geolocated the location of road traffic crashes based on crowdsourced reports of crashes from Ma3Route, a mobile/web/SMS platform that crowdsources transport data

    Geographic coverage

    Primarily Nairobi, Kenya

    Analysis unit

    Road traffic crashes

    Kind of data

    Observation data/ratings [obs]

    Sampling procedure

    All tweets from @Ma3Route from August 2012 to July 2023

    Mode of data collection

    Internet [int]

  19. Attitudes towards the internet in Australia 2025

    • statista.com
    Updated Apr 11, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Umair Bashir (2025). Attitudes towards the internet in Australia 2025 [Dataset]. https://www.statista.com/topics/1145/internet-usage-worldwide/
    Explore at:
    Dataset updated
    Apr 11, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Umair Bashir
    Description

    When asked about "Attitudes towards the internet", most Australian respondents pick "It is important to me to have mobile internet access in any place" as an answer. 54 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

  20. Countries with the most Facebook users 2024

    • statista.com
    • ai-chatbox.pro
    Updated Jun 17, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stacy Jo Dixon (2025). Countries with the most Facebook users 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset updated
    Jun 17, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    Which county has the most Facebook users?

                  There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.
    
                  Facebook – the most used social media
    
                  Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.
    
                  Facebook usage by device
                  As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.
    
Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Frank Loh; Florian Wamser; Fabian Poignée; Stefan Geißler; Tobias Hoßfeld (2022). YouTube Dataset on Mobile Streaming for Internet Traffic Modeling, Network Management, and Streaming Analysis [Dataset]. http://doi.org/10.6084/m9.figshare.19096823.v2

YouTube Dataset on Mobile Streaming for Internet Traffic Modeling, Network Management, and Streaming Analysis

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
txtAvailable download formats
Dataset updated
Apr 14, 2022
Dataset provided by
figshare
Authors
Frank Loh; Florian Wamser; Fabian Poignée; Stefan Geißler; Tobias Hoßfeld
License

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

Area covered
YouTube
Description

Streaming is by far the predominant type of traffic in communication networks. With thispublic dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3G/4G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

Search
Clear search
Close search
Google apps
Main menu