This dataset simulates anonymized mobile screen time and app usage data collected from Android/iOS users over a 3-month period (Jan–April 2024). It captures daily usage trends across various app categories including:

Productivity: Google Docs, Notion, Slack

Entertainment: YouTube, Netflix, TikTok

Social Media: Instagram, WhatsApp, Facebook

Utilities: Chrome, Gmail, Maps

For YouTube, additional engagement statistics such as views, likes, and comments are included to analyze video popularity and content consumption behavior.

The dataset enables exploration of:

Productivity vs. entertainment screen time patterns

Daily usage fluctuations

App-specific user engagement

Correlation between time spent and user interactions

YouTube content virality metrics

This is a great resource for:

EDA projects

Behavioral clustering

Dashboard development

Time series and anomaly detection

Building recommendation or focus-assistive apps

Dataset

This dataset was created by Michael Lomuscio

Contents

Android maintained its position as the leading mobile operating system worldwide in the third quarter of 2025 with a market share of about ***** percent. Android's closest rival, Apple's iOS, had a market share of approximately ***** percent during the same period. The leading mobile operating systems Both unveiled in 2007, Google’s Android and Apple’s iOS have evolved through incremental updates introducing new features and capabilities. The latest version of iOS, iOS 18, was released in September 2024, while the most recent Android iteration, Android 15, was made available in September 2023. A key difference between the two systems concerns hardware - iOS is only available on Apple devices, whereas Android ships with devices from a range of manufacturers such as Samsung, Google and OnePlus. In addition, Apple has had far greater success in bringing its users up to date. As of February 2024, ** percent of iOS users had iOS 17 installed, while in the same month only ** percent of Android users ran the latest version. The rise of the smartphone From around 2010, the touchscreen smartphone revolution had a major impact on sales of basic feature phones, as the sales of smartphones increased from *** million units in 2008 to **** billion units in 2023. In 2020, smartphone sales decreased to **** billion units due to the coronavirus (COVID-19) pandemic. Apple, Samsung, and lately also Xiaomi, were the big winners in this shift towards smartphones, with BlackBerry and Nokia among those unable to capitalize.

Compilation Dataset: Smartphone Processors Ranking & Scores

Context

Benchmarks allow for easy comparison between multiple devices by scoring their performance on a standardized series of tests, and they are useful in many instances: When buying a new phone or tablet

Content

Newest data as of May 3rd, 2022. This dataset contains benchmarks of Android and iOS devices

1. Total Score

Benchmark apps gives your device an overall numerical score as well as individual scores for each test it performs. The overall score is created by adding the results of those individual scores. These score numbers don't mean much on their own, they're just helpful for comparing different devices. For example, if your device's score is 300000, a device with a score of 600000 is about twice as fast. You can use individual test scores to compare the relative performance of specific parts of different devices. For example, you could compare how fast your phone's storage performs compared to another phone's storage.

2. CPU Score

The first part of the overall score is your CPU score. The CPU score in turn includes the output of CPU Mathematical Operations, CPU Common Algorithms, and CPU Multi-Core. In simpler words, the CPU score means how fast your phone processes commands. Your device's central processing unit (CPU) does most of the number-crunching. A faster CPU can run apps faster, so everything on your device will seem faster. Of course, once you get to a certain point, CPU speed won't affect performance much. However, a faster CPU may still help when running more demanding applications, such as high-end games.

3. GPU Score

The second part of the overall score is your GPU score. This score is comprised of the output of graphical components like Metal, OpenGL or Vulkan, depending on your device. The GPU score means how well your phone displays 2D and 3D graphics. Your device's graphics processing unit (GPU) handles accelerated graphics. When you play a game, your GPU kicks into gear and renders the 3D graphics or accelerates the shiny 2D graphics. Many interface animations and other transitions also use the GPU. The GPU is optimized for these sorts of graphics operations. The CPU could perform them, but it's more general-purpose and would take more time and battery power. You can say that your GPU does the graphics number-crunching, so a higher score here is better.

4. MEM score

The third part of the overall score is your MEM score. The MEM score includes the results of the output of RAM Access, ROM APP IO, ROM Sequential Read and Write, and ROM Random Access. In simpler words, the MEM score means how fast and how much memory your phone possesses. RAM stands for random-access memory; while ROM stands for read-only memory. Your device uses RAM as working memory, while flash storage or an internal SD card is used for long-term storage. The faster it can write to and read data from its RAM, the faster your device will perform. Your RAM is constantly being used on your device, whatever you're doing. While RAM is volatile in nature, ROM is its opposite. RAM mostly stores temporary data, while ROM is used to store permanent data like the firmware of your phone. Both the RAM and ROM make up the memory of your phone, helping it to perform tasks efficiently.

5. UX Score

The fourth and final part of the overall score is your UX score. The UX score is made up of the results of the output of the Data Security, Data Processing, Image Processing, User Experience, and Video CTS and Decode tests. The UX score means an overall score that represents how the device's "user experience" will be in the real world. It's a number you can look at to get a feel for a device's overall performance without digging into the above benchmarks or relying too much on the overall score.

Acknowledgements

Data scrapped from AnTuTu, cross-platform adjusted using 3DMark and Geekbench

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📊 Number of iPhone Users: Total users worldwide and within the USA. 📈 Sales Figures: Yearly iPhone sales data. 🏆 Market Share: Comparison of iOS and Android market shares across years. This dataset is perfect for:

Market forecasting and trend analysis. Competitive landscape studies between iOS and Android. Consumer behavior research in the tech industry. Whether you're a data scientist, market analyst, or tech enthusiast, this dataset provides valuable insights to support your research and projects.

This dataset encompasses mobile smartphone application (app) usage, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or surveying to understand the why. iOS and Android operating system coverage.

This dataset encompasses mobile web clickstream behavior on any browser, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or path to purchase and consumer journey understanding. Full URL deliverable available including searches.

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This dataset has been artificially generated to mimic real-world user interactions within a mobile application. It contains 100,000 rows of data, each row of which represents a single event or action performed by a synthetic user. The dataset was designed to capture many of the attributes commonly tracked by app analytics platforms, such as device details, network information, user demographics, session data, and event-level interactions.

Key Features Included

User & Session Metadata

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Device OS & OS Version: Simulated operating systems (Android/iOS) with plausible version numbers. Device Model: Common phone models (e.g., “Samsung Galaxy S22,” “iPhone 14 Pro,” etc.). Screen Resolution: Typical screen resolutions found in smartphones (e.g., “1080x1920”). Network Type: Indicates whether the user was on Wi-Fi, 5G, 4G, or 3G. Location & Locale

Location Country & City: Random global locations generated using Faker. App Language: Represents the user’s app language setting (e.g., “en,” “es,” “fr,” etc.). User Properties

Battery Level: The phone’s battery level as a percentage (0–100). Memory Usage (MB): Approximate memory consumption at the time of the event. Subscription Status: Boolean flag indicating if the user is subscribed to a premium service. User Age: Random integer ranging from teenagers to seniors (13–80). Phone Number: Fake phone numbers generated via Faker. Push Enabled: Boolean flag indicating if the user has push notifications turned on. Event-Level Interactions

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Typos and Misspellings: Random single-character edits, e.g., “Andro1d” instead of “Android.” Missing Values: Some cells might be blank (None) to reflect dropped or unrecorded data. Random String Injections: Occasional random alphanumeric strings inserted where they don’t belong. These intentional discrepancies can help data scientists practice data cleaning, outlier detection, and data wrangling techniques.

Usage & Applications

Data Cleaning & Preprocessing: Ideal for practicing how to handle missing values, inconsistent data, and noise in a realistic scenario. Analytics & Visualization: Demonstrate user interaction funnels, session durations, usage by device/OS, etc. Machine Learning & Modeling: Suitable for building classification or clustering models (e.g., user segmentation, event classification). Simulation for Feature Engineering: Experiment with deriving new features (e.g., session frequency, average battery drain, etc.).

Important Notes & Disclaimer

Synthetic Data: All entries (users, device info, IPs, phone numbers, etc.) are artificially generated and do not correspond to real individuals. Privacy & Compliance: Since no real personal data is present, there are no direct privacy concerns. However, always handle synthetic data ethically.

Differences between operating systems (Android, iOS, Mac OS, and Windows; Study 2).

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

Install App dataset provides comprehensive, first-party app install intelligence across the APAC region, sourced from AI-driven OS-level keyboard and utility applications. It captures highly granular insights into mobile app installations, updates, and user behavior, enabling precise market analytics, attribution tracking, and growth optimization.

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Alongside app-level insights, the dataset provides detailed device intelligence — including manufacturer, model, OS type/version, language, and user agent — combined with IP-based location data (country, region, city) and daily server timestamps for freshness tracking.

All data is hashed, privacy-compliant, and refreshed daily, making it ideal for organizations seeking high-quality, real-world app install signals across Android and iOS ecosystems.

📊 Key Features • First-party, consented data from OS-level applications • Hashed identifiers (device_id, advertising_id) for privacy-safe integration • Install and update timestamps for temporal and behavioral analysis • is_new_install flag to separate new installs from reinstalls or app updates • Comprehensive app, device, and location attributes • Daily refreshed dataset ensuring data accuracy and timeliness

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User Interface (Navigation) Corpus

  Overview

This dataset serves as a collection of various images of, videos and metadata of mobile (both iOS and Android) and web user interfaces as well as tags and text extractions associated to them. Dataset also includes user interface navigation annotations and videos related to them. One of the possible use cases of this dataset is training a UI navigation agent.

  Dataset Structure

The resources of this dataset are packed… See the full description on the dataset page: https://huggingface.co/datasets/teleren/ui-navigation-corpus.

This dataset provides a comprehensive analysis of mobile device usage patterns and user behavior classification. It contains 700 samples of user data, including metrics such as app usage time, screen-on time, battery drain, and data consumption. Each entry is categorized into one of five user behavior classes, ranging from light to extreme usage, allowing for insightful analysis and modeling.

Key Features: - User ID: Unique identifier for each user. - Device Model: Model of the user's smartphone. - Operating System: The OS of the device (iOS or Android). - App Usage Time: Daily time spent on mobile applications, measured in minutes. - Screen On Time: Average hours per day the screen is active. - Battery Drain: Daily battery consumption in mAh. - Number of Apps Installed: Total apps available on the device. - Data Usage: Daily mobile data consumption in megabytes. - Age: Age of the user. - Gender: Gender of the user (Male or Female). - User Behavior Class: Classification of user behavior based on usage patterns (1 to 5).

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SmartParking is a trial designed to help ease traffic congestion and lower travel times by using real-time bay sensor data and the ParkCBR app to show drivers where they are more likely to find available car parking in the Manuka shopping precinct. Android users can download the ParkCBR from GooglePlay Store and iOS users from the AppStore. The Lots dataset shows the locations and describes each lot.

Mobile App Statistics (Apple iOS app store)

The ever-changing mobile landscape is a challenging space to navigate. . The percentage of mobile over desktop is only increasing. Android holds about 53.2% of the smartphone market, while iOS is 43%. To get more people to download your app, you need to make sure they can easily find your app. Mobile app analytics is a great way to understand the existing strategy to drive growth and retention of future user.

With million of apps around nowadays, the following data set has become very key to getting top trending apps in iOS app store. This data set contains more than 7000 Apple iOS mobile application details. The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Interactive full Shiny app can be seen here( https://multiscal.shinyapps.io/appStore/)

Data collection date (from API); July 2017

Dimension of the data set; 7197 rows and 16 columns

Content:

appleStore.csv

1. "id" : App ID

2. "track_name": App Name

3. "size_bytes": Size (in Bytes)

4. "currency": Currency Type

5. "price": Price amount

6. "rating_count_tot": User Rating counts (for all version)

7. "rating_count_ver": User Rating counts (for current version)

8. "user_rating" : Average User Rating value (for all version)

9. "user_rating_ver": Average User Rating value (for current version)

10. "ver" : Latest version code

11. "cont_rating": Content Rating

12. "prime_genre": Primary Genre

13. "sup_devices.num": Number of supporting devices

14. "ipadSc_urls.num": Number of screenshots showed for display

15. "lang.num": Number of supported languages

16. "vpp_lic": Vpp Device Based Licensing Enabled

appleStore_description.csv

1. id : App ID
2. track_name: Application name
3. size_bytes: Memory size (in Bytes)
4. app_desc: Application description

Acknowledgements

The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Inspiration

1. How does the App details contribute the user ratings?
2. Try to compare app statistics for different groups?

Reference: R package From github, with devtools::install_github("ramamet/applestoreR")

Licence

Copyright (c) 2018 Ramanathan Perumal