This dataset provides comprehensive insights into mobile app usage patterns across different categories, including education, social media, productivity, entertainment, health, news, and shopping applications. It contains screen time data for 500 users with demographic information such as age and gender, making it valuable for analyzing digital behavior patterns and productivity correlations.

🚀 MobileViews: A Large-Scale Mobile GUI Dataset

MobileViews is a large-scale dataset designed to support research on mobile agents and mobile user interface (UI) analysis. The first release, MobileViews-600K, includes over 600,000 mobile UI screenshot-view hierarchy (VH) pairs collected from over 20,000 apps on the Google Play Store. This dataset is based on the DroidBot, which we have optimized for large-scale data collection, capturing more comprehensive interaction details while… See the full description on the dataset page: https://huggingface.co/datasets/mllmTeam/MobileViews.

User Feedback Dataset from the Top 15 Downloaded Mobile Applications

This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. The dataset is particularly suitable for Natural Language Processing (NLP) applications, such as text classification and topic modeling.

List of Included Applications:

• TikTok
• Instagram
• Facebook
• WhatsApp
• Telegram
• Zoom
• Snapchat
• Facebook Messenger
• Capcut
• Spotify
• YouTube
• HBO Max
• Cash App
• Subway Surfers
• Roblox

Data Columns and Descriptions:

• review_id: Unique identifiers for each user feedback/application review.
• content: User-generated feedback/review in text format.
• score: Rating or star given by the user.
• TU_count: Number of likes/thumbs up (TU) received for the review.
• app_id: Unique identifier for each application.
• app_name: Name of the application.
• RC_ver: Version of the app when the review was created (RC).

Terms of Use:

This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:

1.Paper

M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3332644

2.Dataset

Asnawi, M. H., Pravitasari, A. A., Herawan, T., & hendrawati, T. (2023). User Feedback Dataset from the Top 15 Downloaded Mobile Applications [Data set]. In The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling (1.0.0, Vol. 11, pp. 130272–130286). Zenodo. https://doi.org/10.5281/zenodo.10204232

Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.

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.

Explore Crawlora's mobile app dataset: 5,063,929 apps across both stores — 2,113,447 on Apple's App Store and 2,950,482 on Google Play. Categories, ratings, install scale, pricing and global availability — with REST API access.

Aggregate metrics over 5,063,929 apps across the Apple App Store (2,113,447) and Google Play (2,950,482): category mix, ratings, pricing, attention concentration, and global storefront availability — queryable via REST API.

The MASC dataset is the foundation for developing machine-learning models to detect and classify these screen types. Some of advantages of the MASC dataset, composed of mobile application screens that I have collected, can be summarized as follows: • Large and Diverse App Screen Sample: The MASC dataset includes over 7,000 unique mobile app screens from various apps and activity types, so it can support the development of robust ML models for mobile app screen classification. and it can also serve as a benchmark for developing and evaluating new ML models in this domain. • Realistic Data: Screens collected from actual Android apps via the Rico platform represent real-world designs, aiding models' generalization to real apps. • Improved App Accessibility: Identifying common screen patterns can offer insights to enhance accessibility features, benefiting users with disabilities. • Enhanced User Experience: Understanding mobile app screen types can lead to better user interface design, improving the overall user experience. • many potential applications can be created using the MASC Dataset. These applications include UI captioning and semantic tagging, user-friendly designs with explanations, intelligent tutorials, and enhanced design search features.

MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments

Mobile World is a substantially more challenging mobile-use benchmark designed to better reflect real-world mobile usage. It comprises 201 tasks across 20 applications, featuring long-horizon, cross-app tasks, and novel task categories including agent-user interaction and MCP-augmented tasks. The difficulty of Mobile World is twofold:

Long-horizon, cross-application tasks.… See the full description on the dataset page: https://huggingface.co/datasets/Tongyi-MAI/MobileWorld.

Real Mobile Apps Parsed from Apple AppStore for the Market in 2026.

As of July 2026, Microsoft Clarity led among Android apps that used mobile data intelligence software development kits (SDKs) with an integration reach of over 35 percent. StartApp - TrueNet Network and Speed Info was the second most integrated SDK, present in 16 percent of such apps, while Comscore Analytics ranked fifth with integration in over nine percent of apps using data intelligence SDKs.

This dataset provides information about 837 mobile applications (apps) published across the whole world to fight the COVID-19 crisis. This information includes: (a) information available in the mobile app stores (Apple App Store and Google Play) between 20/04/2020 and 02/08/2020; (b) complementary information obtained from manual analysis performed until mid-September 2020; and (c) status information about app availability on 28/02/2021, when we last visited the mobile app stores. The dataset is one of the outcomes of the JRC Unit B.6 multi-channel approach to the monitoring and analysis of COVID-19-related mobile apps.

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

As of May 2023, the mobile app version of popular ********************************* used ** of the data points they collected to track their iOS users, as well as collecting ** data points connected to the user's identity. Facebook, which was identified as the most data-hungry app among all the mobile social media, used ***** of its ** collected data points to track users. Dating app ****** collected ** data points collected to the users' identity, as well as **** data points to track users activity.

This dataset contains cleaned data from the Google Play Store, which includes information on various mobile applications available on the platform. The original dataset was sourced from [mention the source if available, e.g., Kaggle, official API, etc.], and it has been preprocessed to remove inconsistencies, handle missing values, and standardize formats for easier analysis.

Features: The dataset includes the following columns: - App Name: The name of the mobile application. - Category: The category under which the app is listed on the Play Store. - Rating: The user rating of the app (0-5 scale). - Reviews: The number of user reviews. - Size: The size of the app (e.g., in MB). - Installs: The number of times the app has been installed. - Type: Whether the app is free or paid. - Price: The price of the app, if paid. - Content Rating: The age group suitable for the app. - Genres: The genres associated with the app. - Last Updated: The last date the app was updated. - Current Version: The current version of the app. - Android Version: The minimum Android OS version required to run the app.

🍎 Overview

This dataset contains detailed information on 11,500 simulated Apple App Store applications as they would appear in 2026. It has been meticulously generated to reflect the real-world distributions and characteristics of the iOS App Store ecosystem.

This dataset is ideal for Exploratory Data Analysis (EDA), predictive modeling, and understanding the trends in the mobile app industry on the Apple platform.

Features (Columns)

The dataset contains the following 16 columns:

1. App_ID: Unique numeric identifier for the app (similar to real App Store IDs).
2. App_Name: The name of the application.
3. Developer: The company or developer who published the app.
4. Primary_Genre: The main category of the app (e.g., Games, Photo & Video, Utilities).
5. Genres: A comma-separated list of all genres the app belongs to.
6. User_Rating: The average user rating of the app on a scale from 1.0 to 5.0 (NaN for apps with very few ratings).
7. Rating_Count: The total number of user ratings the app has received. Note: The App Store displays exact rating counts, unlike the Play Store's install bins.
8. Price_USD: The price of the application in USD (0.0 for free apps).
9. Type: Whether the app is "Free" or "Paid".
10. Size_Bytes: The size of the application in bytes.
11. Content_Rating: Age restriction rating (4+, 9+, 12+, 17+).
12. Last_Updated: The date the app was last updated (YYYY-MM-DD).
13. Current_Version: The current version number of the application.
14. Min_iOS_Version: The minimum iOS version required to run the app.
15. In_App_Purchases: Indicates if the app offers in-app purchases ("Yes" or "No").
16. Supported_Devices: The Apple devices supported by the app (e.g., "iPhone, iPad, iPod touch", "iPhone only").

Potential Use Cases

• Rating Prediction: Can you predict an app's user rating based on its genre, size, price, and update frequency?
• Pricing Strategy Analysis: Analyze the relationship between app price, in-app purchases, and user ratings across different genres.
• Trend Analysis: Understand which categories have the highest user engagement (rating counts) and explore the dominance of Free vs. Paid models in 2026.
• NLP on App Names: Extract trends from app names (e.g., use of terms like "AI", "Crypto", "Pro") and see how they correlate with popularity.

This is a dataset of the paper titled "Strategies to Embed Human Values in Mobile Apps: What do End-Users and Practitioners Think?". In this study, we conducted a mixed-methods empirical study, which collected data through 13 semi-structured interviews with Bangladeshi agriculture mobile app practitioners and 4 focus groups with 20 Bangladeshi female farmers. Our aim is to identify the extent to which the existing agriculture mobile apps reflect Bangladeshi female farmers' values and to propose potential strategies to address their values in agriculture apps. There are four documents in this dataset.

1. Questionnaire for focus groups
2. Questionnaire for interviews
3. Examples of open coding process
4. Summary of member checking outcomes

Quantify mobile app popularity and user acquisition by tracking downloads across major app stores, offering insights into market penetration and growth.

As of May 2023, the mobile app of shopping and marketplace platform Etsy used approximately half of its collected data points to track users. In comparison, health app Noom used only *** of its collected user data point for tracking purposes.

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.

Context

This dataset provides insights into the daily mobile usage patterns of 1,000 users, covering aspects such as screen time, app usage, and user engagement across different app categories.

It includes a diverse range of users based on age, gender, and location.

The data focuses on total app usage, time spent on social media, productivity, and gaming apps, along with overall screen time.

This information is valuable for understanding behavioral trends and app usage preferences, making it useful for app developers, marketers, and UX researchers.

This dataset is useful for analyzing mobile engagement, app usage habits, and the impact of demographic factors on mobile behavior. It can help identify trends for marketing, app development, and user experience optimization.

Outcome

This dataset enables a deeper understanding of mobile user behavior and app engagement across different demographics.

Key outcomes include insights into app usage preferences, daily screen time habits, and the impact of age, gender, and location on mobile behavior.

This analysis can help identify patterns for improving user experience, tailoring marketing strategies, and optimizing app development for different user segments.