App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

Google Play Store dataset to explore detailed information about apps, including ratings, descriptions, updates, and developer details. Popular use cases include app performance analysis, market research, and consumer behavior insights.

Use our Google Play Store dataset to explore detailed information about apps available on the platform, including app titles, developers, monetization features, user ratings, reviews, and more. This dataset also includes data on app descriptions, safety measures, download counts, recent updates, and compatibility, providing a complete overview of app performance and features.

Tailored for app developers, marketers, and researchers, this dataset offers valuable insights into user preferences, app trends, and market dynamics. Whether you're optimizing app development, conducting competitive analysis, or tracking app performance, the Google Play Store dataset is an essential resource for making data-driven decisions in the mobile app ecosystem.

Dataset Features

• url: The URL link to the app’s detail page on the Google Play Store.
• title: The name of the application.
• developer: The developer or company behind the app.
• monetization_features: Information regarding how the app generates revenue (e.g., in-app purchases, ads).
• images: Links or references to images associated with the app.
• about: Details or a summary description of the app.
• data_safety: Information regarding data safety and privacy practices.
• rating: The overall rating of the app provided by its users.
• number_of_reviews: The total count of user reviews received.
• star_reviews: A breakdown of reviews by star ratings.
• reviews: Reviews and user feedback about the app.
• what_new: Information on the latest updates or features added to the app.
• more_by_this_developer: Other apps by the same developer.
• content_rating: The content rating which guides suitability based on user age.
• downloads: The download count or range indicating the app’s popularity.
• country: The country associated with the app listing.
• app_category: The category or genre under which the app is classified.

Distribution

• Data Volume: 17 Columns and 65.54M Rows
• Format: CSV

Usage

This dataset is ideal for a variety of applications:

• App Market Analysis: Enables market researchers to extract insights on app popularity, engagement, and trends across different categories.
• Machine Learning: Can be used by data scientists to build recommendation engines or sentiment analysis models based on app review data.
• User Behavior Studies: Facilitates academic or industrial research into user preferences and behavior with respect to mobile applications.

Coverage

• Geographic Coverage: global.

License

CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: To train machine learning models for app popularity prediction, sentiment analysis, or recommendation systems.
• Researchers: For academic or scientific studies into market trends, consumer behavior, and app performance analysis.
• Businesses: For strategic analysis, developing market insights, or enhancing app development and user engagement strategies.

Suggested Dataset Name

1. Play store Insights
2. Android App Scope
3. Market Analytics
4. Play Store Metrics Vault

5. AppTrend360: Google Play Edition

Pricing

Based on Delivery frequency

~Up to $0.0025 per record. Min order $250

Approximately 10M new records are added each month. Approximately 13.8M records are updated each month. Get the complete dataset each delivery, including all records. Retrieve only the data you need with the flexibility to set Smart Updates.

• Monthly

New snapshot each month, 12 snapshots/year Paid monthly

• Quarterly

New snapshot each quarter, 4 snapshots/year Paid quarterly

• Bi-annual

New snapshot every 6 months, 2 snapshots/year Paid twice-a-year

• One-time purchase

New snapshot one-time delivery Paid once

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.

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: 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: 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: 10.1109/ACCESS.2023.3332644.

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.

Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical analyses aiming at assessing the actual quality of test cases manually developed by mobile developers: this perspective could provide evidence-based findings on the future research directions in the field as well as on the current status of testing in the wild. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps, possibly because of time to market requirements. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.

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.

Mobile App Analytics Software Market Outlook

The global mobile app analytics software market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 8.4 billion by 2032, growing at a CAGR of 14.3% during the forecast period. This robust growth is driven by increasing smartphone penetration and the growing importance of mobile applications in business strategies. The rising need for real-time data analysis and user insights to optimize app performance and enhance user experience further fuels market expansion.

One of the primary growth factors for the mobile app analytics software market is the rapid increase in smartphone usage worldwide. With the proliferation of mobile devices, users are spending more time on mobile applications, which has incentivized businesses to invest in mobile app analytics to understand user behavior and improve app functionalities. Moreover, the widespread adoption of mobile devices has provided businesses with rich data sets to analyze, thereby driving the demand for sophisticated analytics tools. This trend is expected to continue as more businesses recognize the value of mobile app analytics in driving customer engagement and retention.

Another significant growth driver is the increasing demand for personalized user experiences. In today’s competitive market landscape, businesses are striving to deliver personalized content and experiences to their users to gain a competitive edge. Mobile app analytics software enables companies to gather and analyze user data, providing valuable insights that can be used to tailor app experiences to individual users’ preferences and behaviors. This personalization not only enhances user satisfaction but also boosts user retention rates, leading to higher revenue generation for businesses.

The burgeoning e-commerce sector also plays a crucial role in the growth of the mobile app analytics software market. With the rise of online shopping, e-commerce businesses are increasingly relying on mobile applications to reach their customers. Mobile app analytics software helps e-commerce companies track and analyze user interactions, purchase patterns, and preferences, enabling them to optimize their app performance and marketing strategies. As the e-commerce industry continues to expand, the demand for mobile app analytics software is expected to grow in tandem.

Regionally, North America holds a dominant position in the mobile app analytics software market, attributed to the high penetration of smartphones and the presence of major technology companies in the region. Additionally, the early adoption of advanced technologies and the increasing focus on digital transformation initiatives further bolster market growth in North America. The Asia Pacific region is also witnessing significant growth, driven by the rapid digitalization of emerging economies and the increasing number of mobile app users. Europe, Latin America, and the Middle East & Africa are also expected to contribute to market growth, supported by the rising adoption of mobile applications and the growing emphasis on user experience optimization.

Component Analysis

The mobile app analytics software market is segmented into software and services components. The software segment holds a substantial share of the market, driven by the need for advanced analytical tools to process and interpret vast amounts of user data. Mobile app analytics software offers functionalities such as user behavior analysis, app performance tracking, and marketing campaign effectiveness measurement, which are crucial for businesses aiming to optimize their mobile strategies. As the demand for data-driven decision-making continues to rise, the software segment is expected to maintain its dominance in the market.

Services, as a component, also play a vital role in the mobile app analytics software market. These services include implementation, consulting, and maintenance, which are essential for ensuring the effective deployment and utilization of mobile app analytics tools. Consulting services, in particular, help businesses understand how to leverage analytics software to achieve their strategic objectives. Additionally, maintenance services ensure that the analytics tools remain up-to-date with the latest technological advancements and market trends, thereby enhancing their effectiveness and reliability.

Customization services are another critical aspect of the services component. Businesses often require tailored solutions that align with their specific needs and goals. Customization services enable compa

With academical purposes for the Master in Data Science at UOC, this data extraction project is carried out using Web Scraping techniques on the Exodus-Privacy website, which is dedicated to analyze security and privacy aspects in Android applications. The dataset about user privacy treatment by mobile applications, provides information on trackers that have been included in the application and the device permissions that the user must accept at the time of installation. In addition, it provides more interesting application features for analytical processing of mobile applications.

Dataframe files:
 · exodus.zip: Contains de icon attribute within the dataset file exodus.json (3G) in a [RGBA] 32x32 list format.
 · exodusNoIcon.zip: Contains de dataset file exodusNoIcon.json (100M) with 153.373 png files. Each file is named with the Id attribute within the dataset file.

Dataframe attributes:

{
  "id": {
    "Id": id,
    "Name": "name",
    "Tracker_count": trackersCount,
    "Permissions_count": permissionsCount,
    "Version": "version",
    "Downloads": "downloads",
    "Analysis_date": "analysisDate",
    "Trackers": [
      {
        "Tracker Name": [
          "trackerPurpose"
        ]
      }
    ],
    "Permissions": [
      "permission",
    ],
    "Permissions_warning_count": permissionWarningCount,
    "Developer": "developer",
    "Country": "country",
    "Icon": [
      [
        R,
        G,
        B,
        A
      ]
    ]
  }
}

Files in this dataset represent an investigation into use of the Library mobile app Minrva during the months of May 2015 through December 2015. During this time interval 45,975 API hits were recorded by the Minrva web server. The dataset included herein is an analysis of the following: 1) a delineation of API hits to mobile app modules use in the Minrva app by month, 2) a general analysis of Minrva app downloads to module use, and 3) the annotated data file providing associations from API hits to specific modules used, organized by month (May 2015 – December 2015).

A vast collection of data which includes the Top 100 Free Applications in the iOS App Store for each day since February 2024.

Features:

• Date of chart
• Rank
• App name
• App identifier
• Chart collection

Usage:

Market trend analysis, business strategy development.

Coverage:

This will cover the top free app chart in the UK iOS App store.

License:

CCO

Who can use it:

Product Owners or Project Managers can use this data set.

How to use it:

The data set could be used to track specific applications and their position within the App store chart over time.

This repository is part of the ITC-NetMingledApp dataset, which includes network traffic data from 36 Android applications, with each capture featuring concurrent traffic from multiple applications and smartphones. This repository contains part #1 of the data related to the Iran-Tehran scenario. Each capture is stored in a compressed file containing the relevant PCAP files of the associated applications. The PCAP files are named according to a convention: {TimeStamp}_{Application Name}{Download-Upload Speed}.pcap Part #2 of Iran-Tehran scenario is in the Tehran Dataset #2 (https://doi.org/10.17632/zsffy3j9y6.1) repository.

Mobile Phone Dataset | Smartphone & Feature Phone

Crowdsourced original images of a wide variety of mobile phones

About Dataset

This dataset is collected by* DataCluster Labs*, India. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

This dataset is an extremely challenging set of over 3000+ original Mobile Phone images captured and crowdsourced from over 1000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs.

Dataset Features 1. Dataset size : 3000+ 2. Captured by : Over 1000+ crowdsource contributors 3. Resolution : 99% images HD and above (1920x1080 and above) 4. Location : Captured with 600+ cities accross India 5. Diversity : Various lighting conditions like day, night, varied distances, view points etc. 6. Device used : Captured using mobile phones in 2020-2021 7. Applications : Mobile Phone detection, cracked screen detection, etc.

Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai

Visit www.datacluster.ai to know more.

The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/

Number, App name, App description, App download link for Android version, App download link for IOS version

Dataset Card for Screen2Words

Screen2Words is a dataset providing screen summaries (i.e., image captions for mobile screens). It uses the RICO image database.

  Dataset Details





  Dataset Sources

Repository: google-research-datasets/screen2words RICO raw downloads

Paper: Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning Rico: A Mobile App Dataset for Building Data-Driven Design Applications

  Uses

This dataset is for… See the full description on the dataset page: https://huggingface.co/datasets/rootsautomation/RICO-Screen2Words.

Open Jackson is the City of Jackson's open data portal to find facts, figures, and maps related to our lives within the city. We are working to make this the default technology platform to support the publication of the City's public information, in the form of data, and to make this information easy to find, access, and use by a broad audience. The release of Open Jackson marks the culminating point of our efforts to transition to a transparent government. We will continue our work to curate high-quality and up-to-date datasets and develop a platform that is widely accessible. If you have feedback, please contact [email protected]. In 2015, a new law created the online open data portal to increase transparency and accountability in Jackson by making key information easily accessible and usable to both city officials and citizens. Click here to view the Jackson Open Data Policy. You may use the search bar at the top of the page to find data. Once you find a dataset you would like to download, select the data and view the available download options. Datasets can also be filtered to display only the features of the dataset that you are interested in for download. Data is offered for download in several formats. Spatial and tabular data formats (CSV, KML, shapefile, and JSON) are available for use in GIS and other applications. Mobile users may require additional software to view downloaded data. To edit a shapefile on an iOS device, users will need to unzip the file with an app such as iZip and then open the file in a viewer/editor such as iGIS. By using data made available through this site, the user agrees to all the conditions stated in the following paragraphs as well as the terms and conditions described under the City of Jackson homepage. The data made available has been modified for use from its original source, which is the City of Jackson. The City of Jackson makes no claims as to the completeness, accuracy, timeliness, or content of any data contained in this application; makes no representation of any kind, including, but not limited to, warranty of the accuracy or fitness for a particular use; nor are any such warranties to be implied or inferred with respect to the information or data furnished herein. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the site is being used at one's own risk. The City of Jackson reserves the right to discontinue providing any or all of the data feeds at any time and to require the termination of any and all displaying, distributing or otherwise using any or all of the data for any reason including, without limitation, your violation of any provision of these Terms of Use. If you have questions, suggestions, requests or any other feedback, please contact or email at [email protected]

The number of Instagram users in Saudi Arabia was forecast to continuously increase between 2024 and 2028 by in total 1.6 million users (+10.64 percent). According to this forecast, in 2028, the Instagram user base will have increased for the fifth consecutive year to 16.64 million users. User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 Instagram users in countries like Bahrain and Oman.

You may use the search bar at the top of the page to find data. Once you find a dataset you would like to download, select the data and view the available download options. Datasets can also be filtered to display only the features of the dataset that you are interested in for download. Data is offered for download in several formats. Spatial and tabular data formats (CSV, KML, shapefile, and JSON) are available for use in GIS and other applications. Mobile users may require additional software to view downloaded data. To edit a shapefile on an iOS device, users will need to unzip the file with an app such as iZip and then open the file in a viewer/editor such as iGIS. If you need a quick primer on City of Denton Open Data platform, watch this intro video By using data made available through this site, the user agrees to all the conditions stated in the following paragraphs as well as the terms and conditions described under the City of Denton homepage. The data made available has been modified for use from its original source, which is the City of Denton. The City of Denton makes no claims as to the completeness, accuracy, timeliness, or content of any data contained in this application; makes no representation of any kind, including, but not limited to, warranty of the accuracy or fitness for a particular use; nor are any such warranties to be implied or inferred with respect to the information or data furnished herein. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the site is being used at one's own risk. The City of Denton reserves the right to discontinue providing any or all of the data feeds at any time and to require the termination of any and all displaying, distributing or otherwise using any or all of the data for any reason including, without limitation, your violation of any provision of these Terms of Use. If you have questions, suggestions, requests or any other feedback, please contact or email at [email protected]

Description:

The Bike Sensor Data Set for Vehicle Encounters is a comprehensive collection of open data aimed at studying and analyzing encounter between bicycles and vehicles in urban environments. This dataset combines data captured by a sensor platform integrated with a smartphone mounted on a bike. By including various smartphone sensors and timestamps for overtaking events, this dataset offers a rich source of information for investigating and understanding the dynamics of vehicle encounters from the perspective of cyclists.

https://data.uni-hannover.de/dataset/98d83b12-493d-40af-8c4e-58e8064795c5/resource/3d19a3a2-73fc-408b-9d1c-87800b7d4b79/download/mounted_platform.png" alt="Photo of the measurement setup of the prototype sensor platform on a bicycle. The logging unit is located on the luggage rack and the side sensor below it at the height of the rear wheel.">

The dataset contains sensor streams recorded during vehicle encounters, including:

• inertial measurement unit
• magnetic field sesnor
• GNSS
• illuminance sensor
• barometric pressure sensor
• side viewing range sensor

These sensors provide a multidimensional view of the cyclist's environment, capturing physical movements, orientation, environmental conditions, and the proximity of vehicles alongside the cyclist. This data enables researchers to analyze overtaking positions, distance statistics, and potential collision scenarios, enhancing our understanding of vehicle encounters and supporting interventions for cyclist safety.

Key Features of the Bike Sensor Data Set:

• Sensor Streams: The data set provides synchronized streams of data from the smartphone sensors, offering valuable insights into the cyclist's experiences during vehicle encounters.
• Timestamps: Each overtaking event is annotated with a timestamp, allowing for temporal analysis and correlation with the other provided data sources.
• Comprehensive Smartphone Sensor Data: The data set encompasses data from a wide array of privacy preserving smartphone sensors, enabling researchers to explore different dimensions of the vehicle encounter experience, such as speed, acceleration, heading, ambient conditions, and sound levels.
• Trajectory Metadata: Each trajectory is accompanied by a JSON file containing metadata such as date, time, location, duration, as well as metadata for each sensor stream. The inclusion of weather information from the Bright Sky API adds an environmental dimension to the dataset.

Potential Applications:

The Bike Sensor Data Set for Vehicle Encounters holds significant potential for a variety of applications, including but not limited to:

• Transportation and urban planning research
• Machine learning and data mining algorithms for cyclist safety prediction
• Human factors research in transportation and mobility
• Development and testing of cyclist-oriented mobile applications

By utilizing this data set, researchers and practitioners can gain valuable insights into the dynamics of vehicle encounters from a cyclist's perspective. This, in turn, can contribute to the development of safer and more cyclist-friendly urban environments, promoting sustainable and active transportation alternatives.

Protection against ransomware is particularly relevant in systems running the Android operating system, due to its huge users' base and, therefore, its potential for monetization from the attackers. In "Extinguishing Ransomware - A Hybrid Approach to Android Ransomware Detection" (see references for details), we describe a hybrid (static + dynamic) malware detection method that has extremely good accuracy (100% detection rate, with false positive below 4%).

We release a dataset related to the dynamic detection part of the aforementioned methods and containing execution traces of ransomware Android applications, in order to facilitate further research as well as to facilitate the adoption of dynamic detection in practice. The dataset contains execution traces from 666 ransomware applications taken from the Heldroid project [https://github.com/necst/heldroid] (the app repository is unavailable at the moment). Execution records were obtained by running the applications, one at a time, on the Android emulator. For each application, a maximum of 20,000 stimuli were applied with a maximum execution time of 15 minutes. For most of the applications, all the stimuli could be applied in this timeframe. In some of the traces none of the two limits is reached due to emulator hiccups. Collected features are related to the memory and CPU usage, network interaction and system calls and their monitoring is performed with a period of two seconds. The Android emulator of the Android Software Development Kit for Android 4.0 (release 20140702) was used. To guarantee that the system was always in a mint condition when a new sample is started, thus avoiding possible interference (e.g., changed settings, running processes, and modifications of the operating system files) from previously run samples, the Android operating system was each time re-initialized before running each application. The application execution process was automated by means of a shell script that made use of Android Debug Bridge (adb) and that was run on a Linux PC. The Monkey application exerciser was used in the script as a generator of the aforementioned stimuli. The Monkey is a command-line tool that can be run on any emulator instance or on a device; it sends a pseudo-random stream of user events (stimuli) into the system, which acts as a stress test on the application software.

In this dataset, we provide both per-app CSV files as well as unified files, in which CSV files of single applications have been concatenated. The CSV files contain the features extracted from the raw execution record. The provided files are listed below:

• ransom-per_app-csv.zip - features obtained by executing ransomware applications, one CSV per application

• ransom-unified-csv.zip - features obtained by executing ransomware applications, only one CSV file