Phone Call Usage

## Overview

Phone Call Usage is a dataset for object detection tasks - it contains Cell Phone annotations for 3,115 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

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

Classroom Cell Phone Detection

## Overview

Classroom Cell Phone Detection is a dataset for object detection tasks - it contains Cell Phones annotations for 253 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Content

This data set includes 7 csv data files, which have a total of 1.4 million user ratings and reviews for different brands of cell phones.

Records of one cellphone user in the mobile phone data.

"Mobile phone usage is a global phenomenon, with billions of people worldwide using smartphones for communication, entertainment, and information. Average daily screen time varies across countries, with some nations spending over 5 hours per day on their devices."

Demographics and the total frequencies of a cell phone use.

Mobile phone log data used to mine contextual behavioral rules of individual mobile phone users

Percentage of smartphone users by selected smartphone use habits in a typical day.

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

'cellphone Detection'

## Overview

'cellphone Detection' is a dataset for object detection tasks - it contains Cell Phone Person annotations for 253 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Sector: 01. Ending all forms of poverty in the world

Algorithm: Males aged 6 years and over who use their mobile phones every day out of the total number of males aged 6 years and over * 100

Phenomenon: Stock

📄 Dataset Description: Smartprix Mobile Dataset Title: Smartprix Mobile Dataset Source: https://www.smartprix.com/mobiles License: Apache 2.0

📝 Overview This dataset contains detailed specifications of mobile phones scraped from Smartprix — a popular product comparison site. The data includes technical specifications, pricing, and ratings for a wide range of mobile devices available in the Indian market.

📦 Columns Included Column Name Description Name Full name of the smartphone Price Price in Indian Rupees (INR) Rating User rating (if available) SIM SIM type (Dual/Single SIM, 4G/5G supported) Processor Chipset used in the phone (e.g., Snapdragon 8 Gen 1) RAM Amount of RAM Battery Battery capacity (e.g., 5000 mAh) Display Display size and resolution Camera Primary camera specification Card Expandable storage support (e.g., microSD) OS Operating system (e.g., Android 13)

📊 Potential Use Cases Market trend analysis for mobile phones

Feature comparison across brands

Recommendation systems for e-commerce

NLP applications (e.g., generating product summaries)

⚠️ Disclaimer This dataset was collected using publicly available data from Smartprix.com and is intended for educational and research purposes only. Ensure compliance with the source’s terms of service before commercial use.

Information Technology Usage and Penetration - Table 720-90006 : Persons aged 10 and over who had a mobile phone (including smartphone and non-smartphone) by sex and age group

Mobile Phone Detection

## Overview

Mobile Phone Detection is a dataset for object detection tasks - it contains Objects annotations for 1,217 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.

This data set is composed of the table with the references used for the review.

Discover the ultimate resource for your B2B needs with our meticulously curated dataset, featuring 148MM+ highly relevant US B2B Contact Data records and associated company information.

Very high fill rates for Phone Number, including for Mobile Phone!

This encompasses a diverse range of fields, including Contact Name (First & Last), Work Address, Work Email, Personal Email, Mobile Phone, Direct-Dial Work Phone, Job Title, Job Function, Job Level, LinkedIn URL, Company Name, Domain, Email Domain, HQ Address, Employee Size, Revenue Size, Industry, NAICS and SIC Codes + Descriptions, ensuring you have the most detailed insights for your business endeavors.

Key Features:

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Reach out to us today to explore flexible pricing options and discover how Salutary Data Company Data, B2B Contact Data, B2B Marketing Data, B2B Email Data, Phone Number Data, Phone Number Datasets, and Phone Number Databases can transform your business strategies. Elevate your decision-making with top-notch B2B intelligence.

This dataset contains two examples of processed mobile phone signaling data, derived from anonymized raw records. The files Example_Data_01 and Example_Data_02 correspond to February 5, 2023, and February 24, 2024, respectively, in Nanjing, Jiangsu Province, China. They include full-day mobility records of 88,554 and 87,575 users. The dataset can be used for research on human mobility, travel behavior, urban dynamics, and spatiotemporal data analysis.

We surveyed 10,208 people from more than 15 countries on their mobile app usage behavior. The countries include USA, China, Japan, Germany, France, Brazil, UK, Italy, Russia, India, Canada, Spain, Australia, Mexico, and South Korea. We asked respondents about: (1) their mobile app user behavior in terms of mobile app usage, including the app stores they use, what triggers them to look for apps, why they download apps, why they abandon apps, and the types of apps they download. (2) their demographics including gender, age, marital status, nationality, country of residence, first language, ethnicity, education level, occupation, and household income (3) their personality using the Big-Five personality traits This dataset contains the results of the survey.