Explore our dataset: 117K doctors' mobile app usage data in TSV format for AI healthcare insights. Ideal for analytics and AI development.

During the study period

😊If You downloaded this dataset or it is useful to You, please upvote it!

Description:

This dataset contains records of events and errors related to the operation of mobile applications on various mobile devices. Each entry includes information about the timestamp, device characteristics, session identifiers, and textual descriptions of events or errors.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F72a315b39866c02162b229d5a209f4b4%2F5.png?generation=1695227457850330&alt=media" alt=""> Data Fields: - Status: A numerical indicator of the event status (e.g., 0 for success, 1 for error). - Event: A textual description of the action or event, including error text if an error occurred. - Device Identification: Information about the mobile device, including model and Android version. - App Version: The version of the mobile application experiencing the event. - App Language: The language in which the application is running. - Android Version: The version of the Android operating system on the device. - Session Identifiers: Unique session or device identifiers associated with the event. - Additional Data: Additional event details, such as the country and other characteristics. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2Fbca8f9b9fb8288e258a59fad5e53ac15%2F4.png?generation=1695227273200372&alt=media" alt="">

This dataset consists of apps needed permissions during installation and run-time. We collect apps from three different sources google play, third-party apps and malware dataset. This file contains more than 5,00,000 Android apps. features extracted at the time of installation and execution. One file contains the name of the features and others contain .apk file corresponding to it extracted permissions and API calls. Benign apps are collected from Google's play store, hiapk, app china, Android, mumayi , gfan slideme, and pandaapp. These .apk files collected from the last three years continuously and contain 81 distinct malware families.

As of May 2024, 44 percent of the total revenues generated by the global app market came from subscriptions. Other monetization methods such as paid downloads and in-app purchases represented the most popular types of revenue streams for global app publishers. Overall, 56 percent of total app revenues came from other monetization methods.

In 2024, the United States was the leading app market, with the Apple App Store and the Google App Store generating approximately 31 billion U.S. dollars of in-app revenues. China was the second-largest app market, as in-app revenues in the region generated approximately 17.34 billion U.S. dollars. Japan ranked third, as the region generated around 11.25 billion U.S. dollars in app revenues for the examined period.

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

This dataset provides daily, aggregated mobile app usage statistics, including launch counts, session lengths, and retention rates, segmented by platform, country, and device type. It enables detailed analysis of user engagement, retention, and growth trends across different mobile applications and markets, supporting strategic decisions for app development and marketing.

We built a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019.

Context

This data set contains some basic statistics about user count and user growth as well as crash count for a real mobile app. The dataset contains a basic timeseries of 1 hour resolution for a period of one week.

Content

The data set contains columns for total concurrent user count, new users acquired in that period of time, number of sessions and crash count.

Acknowledgements

This data set would not be available without the Real User Monitoring capabilities of Dynatrace and its flexibility to export and expose this data for scientific experiments.

Inspiration

The data set was intended to play around with seasonality, trend and prediction of timeseries.

This dataset provides detailed, event-level records of mobile app feature usage, including user interactions, device context, session information, and user segmentation. It enables product teams and UX researchers to analyze feature adoption rates, engagement patterns, and user cohorts, supporting data-driven decisions for app improvement and user experience optimization.

🚀 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.

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.

Tie app usage to web and location events using anonymized PanelistID for omnichannel consumer journey understanding.

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.

App download rankings, usage metrics, and user engagement data (iOS/Android)

During the first quarter of 2024, YouTube shorts recorded the highest engagement rate across all short video platforms and in-app features analyzed. Content hosted on YouTube in form of shorts had an engagement rate of 5.91 percent, while TikTok reported an engagement rate of approximately 5.75 percent. Facebook Reels had an engagement rate of around two percent, making the platform rank last for short-format user engagement.

This dataset comprises 10,000 user reviews of the BCA Mobile app collected from the Google Play Store between December 24, 2023, and June 12, 2024. Each review includes the user's name, the rating they provided (ranging from 1 to 5 stars), the timestamp of when the review was created, and the text content of the review. The dataset is in Indonesian and focuses on feedback from users in Indonesia. This data can be used to perform sentiment analysis, understand user experiences, identify common issues, and assess the overall performance of the BCA Mobile app during the specified timeframe. The reviews are sorted based on the newest first, providing the latest feedback at the top.

Context

This dataset contains reviews for one of the most popular mobile app - tiktok. All the publicly posted reviews are scraped from the google play store.

Inspiration

• The dataset can be used to identify key insights related to the app, key problems/issues people have raised.
• Perform sentiment analysis of the reviews and find what people are talking about.
• Perform topic modeling to identify key topics mentioned in the review over time
• Generate visualizations of different worlds / n-grams / topics extracted from the reviews.

CYC Mobile App - Grand Total *This indicator has been discontinued

This dataset includes network traffic data from more than 50 Android applications across 5 different scenarios. The applications are consistent in all scenarios, but other factors like location, device, and user vary (see Table 2 in the paper). The current repository pertains to Scenario B. Within the repository, for each application, there is a compressed file containing the relevant PCAP files. The PCAP files follow the naming convention: {Application Name}{Scenario ID}{#Trace}_Final.pcap.