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 data related to the Iran-Qom 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

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/

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
      ]
    ]
  }
}

Over three billion people use messaging apps, making them one of the most popular app types. For most people, mobile messaging consists of two platforms: Facebook Messenger and WhatsApp....

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.

Dataset Card for RICO Widget Captioning

Widget Captioning is a dataset for providing captions for UI elements on mobile screens. It uses the RICO image database.

  Dataset Details







  Dataset Sources

Repository: google-research-datasets/widget-caption RICO raw downloads

Paper: Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements Rico: A Mobile App Dataset for Building Data-Driven Design Applications… See the full description on the dataset page: https://huggingface.co/datasets/rootsautomation/RICO-WidgetCaptioning.

Mobile malware detection has attracted massive research effort in our community. A reliable and up-to-date malware dataset is critical to evaluate the effectiveness of malware detection approaches. Essentially, the malware ground truth should be manually verified by security experts, and their malicious behaviors should be carefully labelled. Although there are several widely-used malware benchmarks in our community (e.g., MalGenome, Drebin, Piggybacking and AMD, etc.), these benchmarks face several limitations including out-of-date, size, coverage, and reliability issues, etc.

We make effort to create MalRadar, a growing and up-to-date Android malware dataset using the most reliable way, i.e., by collecting malware based on the analysis reports of security experts. We have crawled all the mobile security related reports released by ten leading security companies, and used an automated approach to extract and label the useful ones describing new Android malware and containing Indicators of Compromise (IoC) information. We have successfully compiled MalRadar, a dataset that contains 4,534 unique Android malware samples (including both apks and metadata) released from 2014 to April 2021 by the time of this paper, all of which were manually verified by security experts with detailed behavior analysis. For more details, please visit https://malradar.github.io/

The dataset includes the following files:

(1) sample-info.csv

In this file, we list all the detailed information about each sample, including apk file hash, app name, package name, report family, etc.

(2) malradar.zip

We have packaged the malware samples in chunks of 1000 applications: malradar-0, malradar-1, malradar-2, malradar-3. All the apk files name after the file SHA256.

If your papers or articles used our dataset, please include a citation to our paper:

@article{wang2022malradar,
 title={MalRadar: Demystifying Android Malware in the New Era},
 author={Wang, Liu and Wang, Haoyu and He, Ren and Tao, Ran and Meng, Guozhu and Luo, Xiapu and Liu, Xuanzhe},
 journal={Proceedings of the ACM on Measurement and Analysis of Computing Systems},
 volume={6},
 number={2},
 pages={1--27},
 year={2022},
 publisher={ACM New York, NY, USA}
}

In 2021, WhatsApp's user base in the United Kingdom amounts to approximately 40.23 million users. The number of WhatsApp users in the United Kingdom is projected to reach 38.35 million users by 2025. User figures 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.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).

This database is provided by Grab Grab Holdings Inc. is a multinational technology company headquartered in One-North, Singapore. It is the developer of a super-app for ride-hailing, food delivery and digital payments services on mobile devices that operates in Singapore, Malaysia, Cambodia, Indonesia, Myanmar, the Philippines, Thailand and Vietnam.

The company also claimed to have two million driving partners, 68 million mobile app downloads, and 3.5 million daily rides

Publicly accessible data services, apps, maps, downloads and KMLs for all of the Alaska Department of Natural Resources datasets. This is the community's public platform for exploring and downloading open data, discovering and building apps, and engaging to solve important local issues. Analyze and combine Open Datasets using maps, as well as develop new web and mobile applications. Let's make our great community even better, together!DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the Open Data application. To make changes to this site, please visit https://opendata.arcgis.com/admin/

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.

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.

The NZ Aerial Imagery Set combines the latest aerial imagery for New Zealand available on the LINZ Data Service. How to use this dataset Use this dataset to view the coverage of recent orthophotos available from LDS, then visit our Aerial Photos category to find and download the individual datasets you’re interested in. Details about the capture date and extent of our individual imagery datasets can be found in the NZ Imagery Survey Index. A list of attribution requirements for each layer is available at Attributing Aerial Imagery data. NEW LINZ Aerial Imagery Basemap Try the new LINZ Basemap service for a free to use Aerial Imagery basemap of the New Zealand mainland and offshore islands. Powered by data from the LINZ Data Service and other authoritative open data sources, the LINZ Aerial Imagery Basemap delivers the latest imagery data direct from the Cloud for quality and performance. Access WMTS or XYZ tile APIs for use in GIS, web and mobile apps. LINZ Aerial Imagery Basemap

Issues from the Improve Detroit application. Detroiters use Improve Detroit to submit non-emergency service requests to the City and to check the status of non-emergency requests such as potholes, graffiti and damaged street signs. Issues are submitted through the Improve Detroit mobile app or the website. Improve Detroit uses the SeeClickFix CRM system from CivicPlus.Each row in the dataset represents a recorded issue, and includes information such as the type of issue reported, a brief description, issue status, and when an issue was opened, acknowledged, and closed. Links to a SeeClickFix webpage with an issue summary are also included. City of Detroit departments that use Improve Detroit are: DWSD, DPD, GSD, BSEED, DPW, PLA, PDD, Demolition, CRIO and DDOT. DoIT acts as system administrators.Click here for the Analytics Hub visualization of Improve Detroit Issues.NOTE: This dataset may not be downloadable through the Open Data Portal. The complete dataset, updated daily, is available to download in csv format.

This open data site is for exploring, accessing and downloading Kentucky-specific GIS data and discovering mapping apps. It provides simple access to information and tools that allow users to understand geospatial data. You can analyze and combine datasets using maps, as well as develop new web and mobile applications. Explore data by category, interact with web mapping applications, use Story Maps, or access our services directly. All data on the site is fed from a variety of authoritative sources.DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this site, please visit https://hub.arcgis.com/admin/

This layer shows workers' place of residence by commute length. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of commuters whose commute is 90 minutes or more. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B08303Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

According to the data published by AppMagic, the Shopee shopping app was downloaded about 9.75 million times in the Philippines in 2024. This was a significant increase from 0.57 million in 2017. Shopee was the third most downloaded shopping app in the country.

The global number of KakaoTalk users in was forecast to decrease between 2024 and 2028 by in total 0.7 million users. This overall decrease does not happen continuously, notably not in 2026 and 2027. The KakaoTalk user base is estimated to amount to 48.7 million users in 2028. Notably, the number of KakaoTalk users of was continuously increasing over the past years.User figures, here concerning the platform kakaoTalk, 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).

The number of WhatsApp users in North America was forecast to continuously increase between 2024 and 2029 by in total 7.6 million users (+4.97 percent). After the ninth consecutive increasing year, the WhatsApp user base is estimated to reach 160.51 million users and therefore a new peak in 2029. Notably, the number of WhatsApp users of was continuously increasing over the past years.User figures, shown here regarding the platform whatsapp, 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.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 WhatsApp users in countries like Worldwide and Africa.

The number of WhatsApp users in Europe was forecast to continuously increase between 2024 and 2029 by in total 2.6 million users (+2.17 percent). After the ninth consecutive increasing year, the WhatsApp user base is estimated to reach 122.38 million users and therefore a new peak in 2029. Notably, the number of WhatsApp users of was continuously increasing over the past years.User figures, shown here regarding the platform whatsapp, 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.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 WhatsApp users in countries like Africa and South America.