Dataset

This dataset was created by Michael Lomuscio

Contents

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

This dataset offers a comprehensive overview of the iPhone's journey in the global smartphone market from 2010 to 2024 . It includes:

📊 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.

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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Explore the Indian Smartphone Market Dataset, featuring demographics, brand preferences (iPhone vs Android), pricing, purchase behavior, and usage trends.

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 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

User ID: A unique integer identifier for each synthetic user. Session ID: Randomly generated session identifiers (e.g., S-123456), capturing the concept of user sessions. IP Address: Fake IP addresses generated via Faker to simulate different network origins. Timestamp: Randomized timestamps (within the last 30 days) indicating when each interaction occurred. Session Duration: An approximate measure (in seconds) of how long a user remained active. Device & Technical Details

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

Event Type: The action taken by the user (e.g., “click,” “view,” “scroll,” “like,” “share,” etc.). Event Target: The UI element or screen component interacted with (e.g., “home_page_banner,” “search_bar,” “notification_popup”). Event Value: A numeric field indicating additional context for the event (e.g., intensity, count, rating). App Version: Simulated version identifier for the mobile application (e.g., “4.2.8”). Data Quality & “Noise” To better approximate real-world data, 1% of all fields have been intentionally “corrupted” or altered:

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.

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

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

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.

This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web. It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website. It has detailed information such as when an individual is walking, driving, and flying. Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website. Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download. The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user. The dataset comprises a pair of (latitude, and longitude), and a timestamp. All the data was delivered in a single CSV file. As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

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This dataset, titled "Phone Listings from GSMArena.com," consists of two primary files: data.json and processed_data.csv, each containing detailed information about various phone models available on the market.

data.json File This file holds the raw, unprocessed data scraped from GSMArena.com. The columns and attributes include:

phone_brand: The brand or manufacturer of the phone (e.g., Apple, Samsung, Xiaomi). phone_model: The specific model or number of the phone. price: The price point of the phone, which can either be an exact figure or a rough estimate. This column might require data cleaning due to inconsistencies. specs: A nested dictionary that details the phone’s technical specifications. This includes features such as screen size, camera resolution, processor type, battery life, and other relevant hardware components. pricing: A nested dictionary containing price listings for the phone across various e-commerce platforms. processed_data.csv File This file contains cleaned and processed phone data, aggregated from various e-commerce sources. The columns are more refined, and each phone entry provides comprehensive details:

phone_brand: The manufacturer or brand of the phone. phone_model: The specific model or name of the phone. store: The particular store or e-commerce platform where the phone is listed. price: The price of the phone as a floating-point number, set in the native currency. currency: The currency in which the phone is priced (e.g., USD, EUR). price_USD: The phone price converted into USD. storage: The storage capacity of the phone, measured in gigabytes (GB). ram: The amount of RAM available in the phone, also measured in gigabytes (GB). Launch: The official launch date of the phone, represented in a datetime format. Dimensions: The physical dimensions of the phone, typically provided in millimeters (e.g., 163.8 x 76.8 x 8.9 mm). Weight: The weight of the phone, measured in grams. Display_Type: The type of display technology used, for example, "LTPO Super Retina XDR OLED, 120Hz, HDR10." Display_Size: The size of the phone's display in inches. Display_Resolution: The resolution of the phone's display (e.g., 1280 x 2856 pixels). OS: The phone's operating system, such as iOS 18 or Android 14. NFC: A flag indicating the presence of Near Field Communication (NFC), with values of 1 for phones that have NFC and 0 for phones that do not. USB: The type of USB port (e.g., USB Type-C 3.2 Gen 2). BATTERY: The battery capacity of the phone, measured in milliampere hours (mAh). Features_Sensors: Various features and sensors included with the phone (e.g., fingerprint scanner, accelerometer). Colors: Available color options for the phone model (e.g., Black Titanium, White Titanium). Video: Camera specifications for video recording, including supported resolutions and frame rates (e.g., 4K@30fps). Chipset: The chipset model in the phone, such as "Apple A18 Pro (3 nm)." CPU: Specifications of the central processing unit (CPU) (e.g., Hexa-core, 2x4.05 GHz). GPU: Specifications of the graphical processing unit (GPU). Year: The year in which the phone model was released. Foldable: A flag indicating whether the phone is foldable (1 = foldable, 0 = not foldable). PPI_Density: The pixel density of the display in pixels per inch (ppi). quantile_10, quantile_50, quantile_90: These columns represent the 10th, 50th (median), and 90th quantiles of phone prices in a given year. price_range: This column classifies phones into different price ranges (low, medium, or high), based on their position in the price distribution (quantiles). Overall, this dataset provides extensive information on phone models, offering both raw and processed views of phone listings, along with important price and technical details.

Infant Crying smartphone speech dataset, collected by Android smartphone and iPhone, covering infant crying. Our dataset was collected from extensive and diversify speakers(201 people in total, with balanced gender distribution), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Turkish Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics. Transcribed with text content, timestamp, speaker's ID, gender and other attributes. Our dataset was collected from extensive and diversify speakers(around 400 native speakers), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Format

16kHz, 16bit, uncompressed wav, mono channel;

Content category

Dialogue based on given topics

Recording condition

Low background noise (indoor)

Recording device

Android smartphone, iPhone

Country

Republic of Türkiye(TUR)

Language(Region) Code

tr-TR

Language

Turkish

Speaker

410 native speakers in total, 51% male and 49% female

Features of annotation

Transcription text, timestamp, speaker ID, gender, noise

Accuracy rate

Word accuracy rate(WAR) 97%

Lao Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Format

16kHz, 16bit, uncompressed wav, mono channel.

Recording condition

quiet indoor environment, low background noise, without echo;

Recording device

Android smartphone, iPhone;

Speaker

418 speakers totally, with 52% female and 48% male

Language

Laotian

Features of annotation

Transcription text;

Accuracy Rate

Word Accuracy Rate (WAR) 95%;

This dataset encompasses mobile app usage, web clickstream and location visitation behavior, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). The only omnichannel meter at scale representing iOS and Android platforms.

English(India) Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics. Transcribed with text content, timestamp, speaker's ID, gender and other attributes. Our dataset was collected from extensive and diversify speakers(390 native speakers), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Format

16 kHz, 16 bit, uncompressed wav, mono channel;

Content category

Dialogue based on given topics

Recording condition

Low background noise (indoor)

Recording device

Android smartphone, iPhone

Country

India(IN)

Language(Region) Code

en-IN

Language

English

Speaker

734 native speakers in total

Features of annotation

Transcription text, timestamp, speaker ID, gender, noise

Accuracy rate

Word Correct rate(WCR) 98%

global cloud mobile backend as a service (BaaS) market size is expected to grow from USD 6.52 billion in 2024 to USD 27.08 billion by 2034, at a CAGR of 15.3%.