5 datasets found
  1. Mobile Daily Smartphone

    • kaggle.com
    zip
    Updated Feb 9, 2025
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    willian oliveira (2025). Mobile Daily Smartphone [Dataset]. https://www.kaggle.com/datasets/willianoliveiragibin/mobile-daily-smartphone
    Explore at:
    zip(3783 bytes)Available download formats
    Dataset updated
    Feb 9, 2025
    Authors
    willian oliveira
    License

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

    Description

    this graph was created in R and Canva :

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F89427211b425b885997029de576bc555%2Fgraph1.gif?generation=1739130158617622&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F7963143f93af16d50bfa667550fbffbd%2Fgraph2.gif?generation=1739130165946944&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F07cb6ebb5596b36e8ede943ca9b7f9b8%2Fgraph3.gif?generation=1739130173200555&alt=media" alt="">

    Recent years have witnessed a rapid growth in the use of mobile devices, enabling people to access the Internet in various contexts. More than 77% of Americans now own a smartphone, with an increasing trend in terms of the time people spend on their phones. More recently, with the release of intelligent assistants such as Google Assistant, Apple Siri, and Microsoft Cortana, people are experiencing mobile search through a single voice-based interface. These systems introduce several research challenges. Given that people spend most of their times in apps and, as a consequence, most of their search interactions would be with apps (rather than a browser), one limitation is that users are unable to use a intelligent assistants to search within many apps.

  2. f

    Academic use of smart phones in Latin American medical students

    • datasetcatalog.nlm.nih.gov
    • figshare.com
    Updated Jun 11, 2024
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    Izquierdo, Juan (2024). Academic use of smart phones in Latin American medical students [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001400063
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    Dataset updated
    Jun 11, 2024
    Authors
    Izquierdo, Juan
    Area covered
    Latin America
    Description

    This data base shows the responses of medical students from 7 countries of Latin America, about academic use of smartphones.

  3. d

    us consumer cell phone records

    • datarade.ai
    Updated Aug 27, 2025
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    Response America (2025). us consumer cell phone records [Dataset]. https://datarade.ai/data-products/us-consumer-cell-phone-records-response-america
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    Dataset updated
    Aug 27, 2025
    Dataset provided by
    Response America, LLC
    Authors
    Response America
    Area covered
    United States of America
    Description

    This dataset, titled "U.S. Consumer Cell Phone Data," provides anonymized records of consumer purchases and ownership of cell phones across the United States. Each entry in the dataset includes vital information such as the name, address, and purchase-related details of individual buyers. The data offers insights into consumer behavior, trends in cell phone adoption, and geographic patterns of mobile technology use.

    Key features of the dataset include:

    1. Name: The names of cell phone buyers, enabling linkages with other consumer records for demographic and behavioral analysis (while respecting privacy guidelines).

    2. Address: Detailed geographic information, including street addresses, cities, states, and ZIP codes. This allows for regional analysis of purchasing trends and the potential for mapping market penetration of specific phone brands or models.

    3. Device Details: While not explicitly stated in the description, such datasets often include information about the types of cell phones purchased, brands, models, and potentially the purchase date.

    This dataset could be invaluable for businesses, marketers, and researchers aiming to understand consumer preferences, improve mobile marketing strategies, or tailor product offerings to specific regions.

    In summary, the U.S. Consumer Cell Phone Data is a robust resource for analyzing the intersection of consumer behavior and mobile technology, provided that its usage adheres to relevant privacy and ethical standards.

  4. PopSign ASL v1.0 - game (test set)

    • kaggle.com
    zip
    Updated Mar 3, 2025
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    Victor Geislinger (2025). PopSign ASL v1.0 - game (test set) [Dataset]. https://www.kaggle.com/datasets/mrgeislinger/popsign-asl-v1-0-game-test
    Explore at:
    zip(131680295057 bytes)Available download formats
    Dataset updated
    Mar 3, 2025
    Authors
    Victor Geislinger
    License

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

    Description

    Contains video data of ASL signs from PopSign v1.0 game category (test set only).

    Video data before landmark data as part of the 2023 Google - Isolated Sign Language Recognition Competition https://www.kaggle.com/competitions/asl-signs See related landmark data here: https://www.kaggle.com/competitions/asl-signs/data

    Information below mirrored from original source: https://signdata.cc.gatech.edu/view/datasets/popsign_v1_0

    PopSign ASL v1.0

    95% of deaf children are born to hearing parents. Since many hearing parents do not know sign, these deaf children are at risk for language acquisition delays resulting in cognitive issues. We are making an educational smartphone game PopSign that helps hearing parents practice their signing vocabulary.

    Our dataset is the largest collection of isolated sign videos collected using mobile phones. We are using the data to train recognition models for use in smartphone applications, including the PopSign game. PopSign and related educational technology teach hearing parents and deaf children to sign, reducing developmental problems.

    From original paper:

    PopSign ASL v1.0 collects examples of 250 isolated American Sign Language signs using the selfie camera on Pixel 4A smartphones in a variety of environments. It is the largest isolated sign language dataset publicly available, the first to focus on one-handed signing with smartphones, and one of the few of its size that has been manually reviewed.

    Papers

  5. Penetration rate of smartphones in the United States 2014-2029

    • statista.com
    Updated Dec 18, 2023
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    Statista Research Department (2023). Penetration rate of smartphones in the United States 2014-2029 [Dataset]. https://www.statista.com/topics/982/mobile-payments/
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    Dataset updated
    Dec 18, 2023
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    United States
    Description

    The smartphone penetration in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.3 percentage points. The penetration is estimated to amount to 97 percent in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.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 smartphone penetration in countries like Mexico and Canada.

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willian oliveira (2025). Mobile Daily Smartphone [Dataset]. https://www.kaggle.com/datasets/willianoliveiragibin/mobile-daily-smartphone
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Mobile Daily Smartphone

studies on mobile information retrieval have focused.

Explore at:
zip(3783 bytes)Available download formats
Dataset updated
Feb 9, 2025
Authors
willian oliveira
License

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

Description

this graph was created in R and Canva :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F89427211b425b885997029de576bc555%2Fgraph1.gif?generation=1739130158617622&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F7963143f93af16d50bfa667550fbffbd%2Fgraph2.gif?generation=1739130165946944&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F07cb6ebb5596b36e8ede943ca9b7f9b8%2Fgraph3.gif?generation=1739130173200555&alt=media" alt="">

Recent years have witnessed a rapid growth in the use of mobile devices, enabling people to access the Internet in various contexts. More than 77% of Americans now own a smartphone, with an increasing trend in terms of the time people spend on their phones. More recently, with the release of intelligent assistants such as Google Assistant, Apple Siri, and Microsoft Cortana, people are experiencing mobile search through a single voice-based interface. These systems introduce several research challenges. Given that people spend most of their times in apps and, as a consequence, most of their search interactions would be with apps (rather than a browser), one limitation is that users are unable to use a intelligent assistants to search within many apps.

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