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.

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.

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

If you enjoyed this dataset, here's some similar datasets you may like 😎

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fa96454d549040ca5bc6239b291b6a478%2Fgraph1.gif?generation=1729451150005529&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fddbecf3f014dc6d0c842ba2f1e0f7e11%2Fgraph2.gif?generation=1729451155866362&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fc142b757bbfe6a74e828354ae6beb9be%2Fgraph3.gif?generation=1729451160812914&alt=media" alt="">

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.

Dutch Scripted Monologue Smartphone Speech Dataset, collected from monologue based on given scripts. Transcribed with text content. Our dataset was collected from extensive and diversify speakers(681people in total, from Netherlands), 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

681 native speakers in total, 41% male and 59% female;

Country

the Netherlands(NLD);

Language(Region) Code

nl-NL;

Language

Dutch;

Features of annotation

Transcription text;

Accuracy Rate

Word Accuracy Rate (WAR) 95%

Gujarati(India) Scripted Monologue Smartphone speech dataset, covers several domains, including chat, interactions, in-home, in-car, numbers and more, mirrors real-world interactions. 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，16 bit， wav，mono channel

Recording condition

in-door

Content category

Chat comment, interactive, car-related, home-related, numbers

Country

India(IND)

Language

Gujarati

Accuracy

Word Accuracy Rate (WAR) at least 95% (Punctuation, tags, speaker ID, gender and other non-speech labeling are excluding statistic)

Device

Android phone, iPhone

Italian(Italy) Scripted Monologue Smartphone speech dataset, collected from monologue based on given prompts, covering oral; human-machine interaction; smart home command and in-car command; numbers; news domains. Transcribed with text content. Our dataset was collected from extensive and diversify speakers(3,109 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

oral category; human-machine interaction category; smart home command and in-car command category; numbers; news category

Recording condition

Low background noise (indoor)

Recording device

Android smartphone, iPhone

Country

Italy(ITA)

Language(Region) Code

it-IT

Language

Italian

Speaker

3,109 people from Italy, 48% male and 52% female

Features of annotation

Transcription text

Device

Android mobile phone, iPhone

Accuracy rate

Word Accuracy Rate(WAR) 95%

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

English(Malaysia) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and control, in-car command and control, numbers and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(423 people in total), 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

Low background noise(indoor), without echo;

Content category

Generic domain; human-machine interaction; smart home command and in-car command; numbers;

Recording device

Android Smartphone, iPhone

Speaker

423 speakers in total, with 53% females(225 speakers) and 47% males(198 speakers)

Country

Malaysia(MYS)

Language

English

Accuracy Rate

Sentence Accuracy Rate(SAR) 95%

German(Germany) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and in-car command, numbers and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(3,442 German native speakers in total), 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

Low background noise(indoor), without echo;

Content category

Generic domain; human-machine interaction; smart home command and control; in-car command and control; numbers

Recording device

Android Smartphone, iPhone;

Speaker

3,442 speakers totally, with 44% male and 56% female ; and 60% speakers of all are in the age group of 18-25,35% speakers of all are in the age group of 26-45, 5% speakers of all are in the age group of 46-60;

Country

Germany(DEU);

Language(Region) Code

de-DE;

Language

German;

Features of annotation

Transcription text;

Accuracy Rate

Sentence Accuracy Rate (SAR) 95%

Portuguese(Europe) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and in-car command, numbers, news and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(2,109 people in total), 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

Low background noise(indoor), without echo;

Content category

Generic domain; news; human-machine interaction; smart home command and control; in-car command and control; numbers

Recording device

Android Smartphone, iPhone;

Speaker

2,109 speakers totally, with 49% male and 51% female;

Country

Portugal

Language

Portuguese;

Features of annotation

Transcription text;

Accuracy Rate

Word Accuracy Rate (CAR) 97%

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users 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).

English(Japan) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and control, in-car command and control, numbers and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(464 people in total), 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

Low background noise(indoor), without echo;

Content category

Generic domain; human-machine interaction; smart home command and in-car command; numbers;

Recording device

Android Smartphone, iPhone

Speaker

464 speakers in total, with 53% females (244 spekers) and 47% males(220 speakers)

Country

Japan(JPN)

Language

English

Accuracy Rate

Sentence Accuracy Rate(SAR) 95%

Social Media Engagement (2025)

This dataset contains 20,000 synthetic social media posts crafted to mimic realistic user activity on a fictional platform. It simulates various user demographics, post content, hashtags, topics, and detailed engagement metrics such as likes, comments, and shares.

Overview

Each record represents a unique social media post made by a user, enriched with features that allow for analysis of trends, behavior, and engagement. The dataset includes:

• User-level information: age, gender, followers, verified status, etc.
• Post-level information: topic, hashtags, media, engagement
• Platform and device data
• Calculated engagement rate

Column Descriptions

About This Dataset

This dataset provides a comprehensive collection of Antutu benchmark scores scraped directly from the official Antutu website (https://www.antutu.com/en/ranking/index.htm). It covers a wide range of mobile devices, including smartphones and tablets, across both Android and iOS platforms. The data is organized into distinct categories, allowing for easy analysis and comparison of device performance, System-on-Chip (SoC) capabilities, and Artificial Intelligence (AI) processing power.

Key Features:

• Multiple Ranking Categories: The dataset includes scores from several Antutu ranking lists:

  • Android Performance (Smartphone): Overall performance scores for Android smartphones.
  • Android Performance (Pad): Overall performance scores for Android tablets.
  • Android SoC: Scores focusing on the System-on-Chip performance, including CPU and GPU.
  • Android AI (General Model): AI benchmark scores for general-purpose AI tasks (Image Classification, Object Detection, etc.).
  • Android AI (Large Language Model): AI benchmark scores specifically for Large Language Models (LLMs), measuring tasks like text summarization and question answering.
  • iOS Performance: Overall performance scores for iOS devices (iPhones and iPads).

• Detailed Score Breakdowns: Where available, the dataset provides individual component scores in addition to the total score. This allows for granular analysis of specific aspects of device performance. Examples include:

  • Performance: CPU Score, GPU Score, MEM Score, UX Score, Total Score.
  • SoC: CPU Score, GPU Score, Total Score.
  • AI (General Model): Image Classification, Object Detection, Super Resolution, Style Transfer, Total Score.
  • AI (Large Language Model): Read Speed, Text Summary, Objective Questions, Total Score.

• Clear Data Structure: The data is presented in a well-structured CSV format, with each row representing a single device's ranking in a specific category. Column names are self-explanatory and consistent across the dataset.

Data Columns:

The dataset contains the following columns, with variations depending on the specific ranking category:

• Platform: (String) The operating system platform (Android or iOS).
• Category: (String) The Antutu ranking category (e.g., Performance, SoC, AI).
• Device Type: (String, where applicable) Specifies the device type (Smartphone or Pad).
• AI Type: (String, where applicable) Specifies the type of AI benchmark (General Model or Large Language Model).
• Device: (String) The name of the device (e.g., "Samsung Galaxy S24 Ultra").
• CPU Score: (String) The CPU benchmark score.
• GPU Score: (String) The GPU benchmark score.
• MEM Score: (String) The memory benchmark score.
• UX Score: (String) The user experience benchmark score.
• Other AI-related, and category-related scores, described previously.
• Total Score: (String) The overall Antutu benchmark score.

Use Cases:

This dataset is valuable for a variety of applications, including:

• Mobile Device Comparison: Compare the performance of different smartphones and tablets across various metrics.
• Hardware Analysis: Analyze the performance of different SoCs and their individual components.
• AI Performance Evaluation: Track the progress of AI capabilities in mobile devices.
• Market Research: Identify trends in the mobile device market.
• Machine Learning Projects: Use the data as input for machine learning models, for tasks such as performance prediction or device classification.
• Data Visualization: Create visualizations to represent the data in an easy to understand way.

Data Source and Disclaimer:

All data is sourced from the official Antutu website. This dataset is provided for informational and research purposes only. While every effort has been made to ensure accuracy, there is no guarantee of completeness or correctness. The Antutu benchmark scores are subject to change. This dataset is not affiliated with or endorsed by Antutu.

Acknowledgements:

This dataset was created by scraping publicly available data from the Antutu website. The scraping process was performed using Python with the requests and BeautifulSoup libraries.

Spanish(Spain) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and in-car command, numbers, news and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(989 people in total), 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

Low background noise(indoor), without echo;

Content category

Generic domain; news; human-machine interaction; smart home command and control; in-car command and control; numbers

Recording device

Android Smartphone, iPhone;

Speaker

989 speakers totally, with 49% male and 51% female ; and 57% speakers of all are in the age group of 17-25,39% speakers of all are in the age group of 26-45, 4% speakers of all are in the age group of 46-60;

Country

Spain(ESP);

Language(Region) Code

es-ES;

Language

Spanish;

Features of annotation

Transcription text;

Accuracy Rate

Sentence Accuracy Rate (SAR) 95%

The number of mobile broadband connections in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 18.3 million connections (+20.46 percent). After the ninth consecutive increasing year, the number of connections is estimated to reach 107.69 million connections and therefore a new peak in 2029. Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. 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 mobile broadband connections in countries like Vietnam and Laos.