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

Unlock the power of user feedback with our iOS App Store Reviews Dataset, a comprehensive collection of reviews from thousands of apps across various categories. This robust App Store dataset includes essential details such as app names, ratings, user comments, timestamps, and more, offering valuable insights into user experiences and preferences.

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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.

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

The name and download numbers of government mobile apps.

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.

This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the International Conference on Mobile Software Engineering and Systems (MOBILESoft) in 2015.

Dataset Collection We built a dataset that consists of a random sample of Android app metadata and user reviews available on the Google Play Store on January and March 2014. Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice. The dataset D1 contains available apps in the Google Play Store in January 2014. Then, we created a new snapshot (D2) of the Google Play Store in March 2014.

The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.

For each app, we retrieved the following metadata: name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting, and the set of permission requests.

In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata: title, description, device, and version of the app. None of these fields were mandatory, thus several reviews lack some of these details. From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).

Dataset Stats Some stats about the datasets:

• D1 (Jan. 2014) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.

• D2 (Mar. 2014) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.

Additional stats about the datasets are available here.

Dataset Description To store the dataset, we created a graph database with Neo4j. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g., clusters of apps sharing similar sets of permission requests).

In particular, our dataset graph contains six types of nodes: - APP nodes containing metadata of each app, - PERMISSION nodes describing permission types, - CATEGORY nodes describing app categories, - SUBCATEGORY nodes describing app subcategories, - USER_REVIEW nodes storing user reviews. - TOPIC topics mined from user reviews (using LDA).

Furthermore, there are five types of relationships between APP nodes and each of the remaining nodes:

• USES_PERMISSION relationships between APP and PERMISSION nodes
• HAS_REVIEW between APP and USER_REVIEW nodes
• HAS_TOPIC between USER_REVIEW and TOPIC nodes
• BELONGS_TO_CATEGORY between APP and CATEGORY nodes
• BELONGS_TO_SUBCATEGORY between APP and SUBCATEGORY nodes

Dataset Files Info

Neo4j 2.0 Databases

googlePlayDB1-Jan2014_neo4j_2_0.rar

googlePlayDB2-Mar2014_neo4j_2_0.rar We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with Neo4j 2.0. In particular with the tool version 'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).

Neo4j 3.5 Databases

googlePlayDB1-Jan2014_neo4j_3_5_28.rar

googlePlayDB2-Mar2014_neo4j_3_5_28.rar Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official Neo4j Download Center. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28. The databases can be opened with the tool version: 'Neo4j Community Edition 3.5.28'. The tool can be downloaded from the official Neo4j Donwload page.

  In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the Neo4j Migration Guide.

  First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j

Ziel dieser Studie war es, den Einfluss verschiedener Anreizsysteme auf die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern experimentell zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen, nicht wahrscheinlichen Online-Panel rekrutiert wurden. Im Dezember 2017 beantworteten 1.214 Teilnehmer einen Fragebogen zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit, allgemeine Einstellungen gegenüber der Umfrageforschung und Forschungseinrichtungen. Darüber hinaus enthielt der Fragebogen ein Experiment zur Bereitschaft, an der mobilen Datenerhebung unter verschiedenen Anreizbedingungen teilzunehmen. Themen: Besitz von Smartphone, Handy, Desktop- oder Laptop-Computer, Tablet-Computer und/oder E-Book-Reader; Art des Smartphones; Bereitschaft zur Teilnahme an der mobilen Datenerfassung unter verschiedenen Anreizbedingungen; Wahrscheinlichkeit des Herunterladens der App zur Teilnahme an dieser Forschungsstudie; Befragter möchte lieber an der Studie teilnehmen, wenn er 100 Euro erhalten könnte; Gesamtbetrag, den der Befragte für die Teilnahme an der Studie verdienen müsste (offene Antwort); Grund, warum der Befragte nicht an der Forschungsstudie teilnehmen würde; Bereitschaft zur Teilnahme an der Studie für einen Anreiz von insgesamt 60 Euro; Bereitschaft zur Aktivierung verschiedener Funktionen beim Herunterladen der App (Interaktionshistorie, Smartphone-Nutzung, Merkmale des sozialen Netzwerks, Netzqualitäts- und Standortinformationen, Aktivitätsdaten); vorherige Einladung zum Herunterladen der Forschungs-App; Herunterladen der Forschungs-App; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Browsen, E-Mails, Fotografieren, Anzeigen/Post-Social-Media-Inhalte, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluethooth, Spielen, Streaming von Musik/Videos); Selbsteinschätzung der Kompetenz im Umgang mit dem Smartphone; Einstellung zu Umfragen und Teilnahme an Forschungsstudien (persönliches Interesse, Zeitverlust, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen zum Datenschutz (Marktforschungsunternehmen, Universitätsforscher, Regierungsbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und Social-Media-Plattformen); allgemeine Datenschutzbedenken; Gefühl der Datenschutzverletzung durch Banken und Kreditkartenunternehmen, Steuerbehörden, Regierungsbehörden, Marktforschung, soziale Netzwerke, Apps und Internetbrowser; Bedenken zur Datensicherheit bei Smartphone-Aktivitäten für Forschungszwecke (Online-Umfrage, Umfrage-Apps, Forschungs-Apps, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Ortung, Bluetooth). Demographie: Geschlecht, Alter; Bundesland; höchster Schulabschluss; höchstes berufliches Bildungsniveau. Zusätzlich verkodet wurden: laufende Nummer; Dauer (Reaktionszeit in Sekunden); Gerätetyp, mit dem der Fragebogen ausgefüllt wurde. The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions. Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth). Demography: sex, age; federal state; highest level of school education; highest level of vocational education. Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.

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/

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2F186cf4f6172ca2c696819b7b09931bd3%2Fimage3.jpg?generation=1584955857130173&alt=media" alt="">

The presence of business in the digital space is a must now. Indeed, there’s hardly any company, be it a small startup or an international corporation, that wouldn’t be available online. For this, the company may use one of two options — to develop an app or a website, or both.

In the case of a limited budget, business owners often have to make a choice. Thus, considering that mobile traffic bypassed the desktop’s in 2016 and continues to grow, it becomes obvious that the business should become accessible and convenient for smartphone users. But what is better a responsive website or a mobile application?

Entrepreneurs often turn to development companies to ask this question. Lacking sufficient knowledge, they hope to get answers to their questions from people with experience in this field. So, we decided to compile a guide that will give you clear and understandable information.

Mobile app

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Let's look at the stats. It will help you understand why a mobile app may be the obvious choice for your client.

In 2019, smartphone users installed about 204 billion(!) applications on their devices. On average, this is more than 26 applications per inhabitant of the planet Earth. And if this is not enough evidence, here’s one more point. The expected revenue of mobile applications will be $189 billion in 2020.

It sounds impressive, but this does not mean that a mobile application is something indispensable for every business. Not at all. Let's go through the pros and cons of a mobile application and try to understand when it is needed.

Pros

• A new level of interaction. Mobile applications are a more convenient method of interaction. They load and process content faster. One more useful feature is notifications. Perhaps, applications are the best way to inform users about new updates, promotions, and other news (who will read long letters in the mail?).
• Personalized targeting. Mobile applications are ideal for products or services that need to be used on an ongoing basis. The options like creating accounts, entering profile information, etc., make applications more personalized than websites. All this allows the business to target their audience more accurately without wasting money.
• Offline usage. That’s another major advantage. Applications can provide users with access to content without an internet connection.

Cons

• Development costs. In order to reach the maximum audience with a mobile app, it is necessary to cover two main operating systems — iOS and Android. Development for each OS can be too expensive for small business owners and they will have to make difficult choices. The way out of this situation is cross-platform development. Why? Because there’s no need to guess which platform targets prefer using — iOS or Android. Instead, you create just one app that runs seamlessly on both platforms.

• Maintenance. The application is a technical product that needs constant support. Upgrades should be carried out in a timely manner. Often, users need to personally update applications by downloading a new version, which is annoying. Regular bug-fixing for various devices (smartphones, tablets) and different operating systems might be a real problem. Plus, any update should be confirmed by the store where the application is placed.

• Suitable for businesses that provide interactive and personalized content (refers to all lifestyle and healthcare solutions), require regular app usage (for instance, to-do lists), rely on visual interaction and so on. For games, like Angry Birds, creating an app is also a wise choice.

Website

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In order to be convenient for users of mobile devices, a website should be responsive. We want to make an emphasis on this since it is critically important. Most of the traffic on the Internet comes from mobile devices, so your website should be adaptable, or in other words, mobile-friendly. If a mobile user needs to zoom in all the necessary elements and text to see something, they will immediately quit your website.

On the other hand, a responsive website has the following benefits.

Pros

• Maintenance. Maintaining a website is less costly. When compared to applications where the user mu...

In March 2024, Meta-powered apps Facebook and Instagram were the most downloaded mobile apps worldwide, with 59 million and 58 million downloads, respectively. Social video app TikTok followed with 46 million downloads. Meta-owned microblogging platform Threads generated 24 million downloads during the last month of the year.

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
      ]
    ]
  }
}

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.

Dataset consisting of feature vectors of 215 attributes extracted from 15,036 applications (5,560 malware apps from Drebin project and 9,476 benign apps). The dataset has been used to develop and evaluate multilevel classifier fusion approach for Android malware detection, published in the IEEE Transactions on Cybernetics paper 'DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection'. The supporting file contains further description of the feature vectors/attributes obtained via static code analysis of the Android apps.

Transit is a mobile app packed with features that helps you plan a trip on Loudoun County Transit buses. Real time bus tracking and information, service alerts and trip planners are some of the many useful features that make this app the favorite for transportation services.Download Transit app to your device for free and set your favorite routes to begin receiving notifications and real-time bus information.Transit Support

This dataset is a sample from the TalkingData AdTracking competition. I kept all the positive examples (where is_attributed == 1), while discarding 99% of the negative samples. The sample has roughly 20% positive examples.

For this competition, your objective was to predict whether a user will download an app after clicking a mobile app advertisement.

File descriptions

train_sample.csv - Sampled data

Data fields

Each row of the training data contains a click record, with the following features.

• ip: ip address of click.
• app: app id for marketing.
• device: device type id of user mobile phone (e.g., iphone 6 plus, iphone 7, huawei mate 7, etc.)
• os: os version id of user mobile phone
• channel: channel id of mobile ad publisher
• click_time: timestamp of click (UTC)
• attributed_time: if user download the app for after clicking an ad, this is the time of the app download
• is_attributed: the target that is to be predicted, indicating the app was downloaded

Note that ip, app, device, os, and channel are encoded.

I'm also including Parquet files with various features for use within the course.

Open Data Companion (ODC) is a productivity tool which provides a unified access point to over 120 open data portals and thousands of datasets from around the world; right from your mobile device. Crafted with mobile-optimised features and design, this is an easy and convenient way to find, access and share open data. Open Data Companion provides a framework for all State, Regional, National and Worldwide CKAN open data portals to deliver open data to all mobile users.

To access datasets from openAfrica Data Portal, type "Africa" during portal set up.

Download it: https://t.co/PtzKmfPlHJ

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]

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).

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.