Use the OpenWeb Ninja Google Play App Store Data API to access comprehensive data on Google Play Store, including Android Apps / Games, reviews, top charts, search, and more. Our extensive dataset provides over 40 app store data points, enabling you to gain deep insights into the market.

The App Store Data dataset includes all key app details:

App Name, Description, Rating, Photos, Downloads, Version Information, App Size, Permissions, Developer and Contact Information, Consumer Review Data.

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/

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

Analysis of ‘Playstore Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/madhav000/playstore-analysis on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Google Play Store team had launched a new feature wherein, certain apps that are promising, are boosted in visibility. The boost will manifest in multiple ways including higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in search results visibility. This feature will help bring more attention to newer apps that have the potential.

Analysis to be done:

The problem is to identify the apps that are going to be good for Google to promote. App ratings, which are provided by the customers, is always a great indicator of the goodness of the app. The problem reduces to: predict which apps will have high ratings.

Problem Statement:

Google Play Store team is about to launch a new feature wherein, certain apps that are promising, are boosted in visibility. The boost will manifest in multiple ways including higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in search results visibility. This feature will help bring more attention to newer apps that have the potential.

Content:

Dataset: Google Play Store data (“googleplaystore.csv”)

Fields in the data: App: Application name Category: Category to which the app belongs Rating: Overall user rating of the app Reviews: Number of user reviews for the app Size: Size of the app Installs: Number of user downloads/installs for the app Type: Paid or Free Price: Price of the app Content Rating: Age group the app is targeted at - Children / Mature 21+ / Adult Genres: An app can belong to multiple genres (apart from its main category). For example, a musical family game will belong to Music, Game, Family genres. Last Updated: Date when the app was last updated on Play Store Current Ver: Current version of the app available on Play Store Android Ver: Minimum required Android version

--- Original source retains full ownership of the source dataset ---

As COVID-19 continues to spread across the world, a growing number of malicious campaigns are exploiting the pandemic. It is reported that COVID-19 is being used in a variety of online malicious activities, including Email scam, ransomware and malicious domains. As the number of the afflicted cases continue to surge, malicious campaigns that use coronavirus as a lure are increasing. Malicious developers take advantage of this opportunity to lure mobile users to download and install malicious apps.

However, besides a few media reports, the coronavirus-themed mobile malware has not been well studied. Our community lacks of the comprehensive understanding of the landscape of the coronavirus-themed mobile malware, and no accessible dataset could be used by our researchers to boost COVID-19 related cybersecurity studies.

We make efforts to create a daily growing COVID-19 related mobile app dataset. By the time of mid-November, we have curated a dataset of 4,322 COVID-19 themed apps, and 611 of them are considered to be malicious. The number is growing daily and our dataset will update weekly. For more details, please visit https://covid19apps.github.io

This dataset includes the following files:

(1) covid19apps.xlsx

In this file, we list all the COVID-19 themed apps information, including apk file hashes, released date, package name, AV-Rank, etc.

(2)covid19apps.zip

We put the COVID-19 themed apps Apk samples in zip files . In order to reduce the size of a single file, we divide the sample into multiple zip files for storage. And the APK file name after the file SHA256.

If your papers or articles use our dataset, please use the following bibtex reference to cite our paper: https://arxiv.org/abs/2005.14619

(Accepted to Empirical Software Engineering)

 @misc{wang2021virus,
   title={Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware}, 
   author={Liu Wang and Ren He and Haoyu Wang and Pengcheng Xia and Yuanchun Li and Lei Wu and Yajin Zhou and Xiapu Luo and Yulei Sui and Yao Guo and Guoai Xu},
   year={2021},
   eprint={2005.14619},
   archivePrefix={arXiv},
   primaryClass={cs.CR}
}

Context

Andriod apps meta-data in JSON format

Content

Meta-data of around 50k android apps. Containing information about the downloads, category, likes, dislikes, ratings, description, and much more.

AOS All Apps

## Overview

AOS All Apps is a dataset for object detection tasks - it contains Android Apps annotations for 250 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains 54,987 UI screenshots and the metadata from 7,748 Android applications belonging to 25 application categories

Download link: https://www.dropbox.com/sh/kfkhevxykzwputb/AAAhL6ipmOg4zZn4jUL_myF0a?dl=0

ObjectiveTo analyse the relationship between health app quality with user ratings and the number of downloads of corresponding health apps.Materials and methodsUtilising a dataset of 881 Android-based health apps, assessed via the 300-point objective Organisation for the Review of Care and Health Applications (ORCHA) assessment tool, we explored whether subjective user-level indicators of quality (user ratings and downloads) correlate with objective quality scores in the domains of user experience, data privacy and professional/clinical assurance. For this purpose, we applied spearman correlation and multiple linear regression models.ResultsFor user experience, professional/clinical assurance and data privacy scores, all models had very low adjusted R squared values (< .02). Suggesting that there is no meaningful link between subjective user ratings or the number of health app downloads and objective quality measures. Spearman correlations suggested that prior downloads only had a very weak positive correlation with user experience scores (Spearman = .084, p = .012) and data privacy scores (Spearman = .088, p = .009). There was a very weak negative correlation between downloads and professional/clinical assurance score (Spearman = -.081, p = .016). Additionally, user ratings demonstrated a very weak correlation with no statistically significant correlations observed between user ratings and the scores (all p > 0.05). For ORCHA scores multiple linear regression had adjusted R-squared = -.002.ConclusionThis study highlights that widely available proxies which users may perceive to signify the quality of health apps, namely user ratings and downloads, are inaccurate predictors for estimating quality. This indicates the need for wider use of quality assurance methodologies which can accurately determine the quality, safety, and compliance of health apps. Findings suggest more should be done to enable users to recognise high-quality health apps, including digital health literacy training and the provision of nationally endorsed “libraries”.

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
      ]
    ]
  }
}

Human_Detection_Android_app

## Overview

Human_Detection_Android_app is a dataset for object detection tasks - it contains Human annotations for 918 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Android UI Objects

## Overview

Android UI Objects is a dataset for object detection tasks - it contains App UI Elements annotations for 1,412 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Introducing the new and improved Main Roads GPS-SLK app, putting network location accuracy at your fingertips. The GPS-SLK app is backed by a dedicated support team and offers a number of functional benefits, which will continue to grow and evolve to meet future demands.Its features include: •Compatibility with iOS and Android (download anytime via the App Store or Google Play) •Location data for State and Local roads •Location data for cycle paths •Offline usage when GPS is enabled (no data, no worries) •Improved location sharing functionality with photo capture •Improved data update notifications Make sure to contact our team with any feedback, so we can keep improving the app! See Frequently Asked Questions for more information.

This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web.
It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website.
It has detailed information such as when an individual is walking, driving, and flying.
Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website.
Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download.
The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user.
The dataset comprises a pair of (latitude, and longitude), and a timestamp.
All the data was delivered in a single CSV file.
As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020.
10.1007/s12243-020-00807-x (DOI)
and
T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.
10.1111/exsy.12627 (DOI)

Eyedrop Bottle Project

## Overview

Eyedrop Bottle Project is a dataset for object detection tasks - it contains Vigamox Bottle annotations for 10,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Please sign-in (top-right) to Launch SCIMS Online

• your existing SIX login credentials will not work
• if you used SCIMS Online in the last 6 months you should have received a activation email from Okta. Please access our Information Sheet for further information
• DCS Spatial Services is aware of an ongoing issue requiring users to click the login button multiple times to launch SCIMS. We are hoping to have this resolved shortly.

The Survey Control Information Management System (SCIMS) is a database that contains the coordinates, heights and related attributes for Permanent Survey Marks (PSMs) constituting the State Control Survey. SCIMS online is a tool which enables users to discover and download data related to each survey mark contained within SCIMS. This includes position, accuracy, source and all other technical information, required by surveyors, to fulfil their obligations under NSW legislation when undertaking surveys and creating survey plans.

Content Title	SCIMS Online
Content Type	Web Application
Description	SCIMS online is a toll which enables users to discover and download data related to each survey mark contained within the Survey Control Information Management System (SCIMS).
Initial Publication Date	15/11/2023
Data Currency	15/11/2023
Data Update Frequency	Other
Content Source	Website URL
File Type	Document
Attribution
Data Theme, Classification or Relationship to other Datasets
Accuracy
Spatial Reference System (dataset)	GDA94
Spatial Reference System (web service)	EPSG:4326
WGS84 Equivalent To	GDA94
Spatial Extent
Content Lineage
Data Classification	Unclassified
Data Access Policy	Open
Data Quality
Terms and Conditions	Creative Commons
Standard and Specification
Data Custodian	DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795
Point of Contact	Please contact us via the Spatial Services Customer Hub
Data Aggregator
Data Distributor
Additional Supporting Information
TRIM Number

IIn deze tabel vindt u informatie over CoronaMelder. Dit betreft twee variabalen: 1. Het aantal mensen die CoronaMelder gedownload hebben 2. Het aantal mensen die anderen waarschuwden via CoronaMelder 1. Het aantal downloads is gebaseerd op basis van gegevens vanuit: - App Store (iOS) - Play Store (Android) - Huawei App Gallery (Android) 2. Als je positief getest bent op corona, dan kun je dit vrijwillig aangeven in de app, samen met een medewerker van de GGD. De cijfers tonen hoeveel mensen dit hebben gedaan.