Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

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...

Context

To date (April 2020), Android is still the most popular mobile operating system in the world. Taking into account billion of Android users worldwide, mining this data has the potential to reveal user behaviors and trends in the whole global scope.

Content

There are 2 CSV files: - app.csv with 53,732 rows and 18 columns. - comment.csv with 1,468,173 rows and 4 columns.

The scraping was done in April 2020.

Acknowledgements

This dataset is obtained from scraping Google Play Store. Without Google and Android, this dataset wouldn’t have existed.

The dataset is first published in this blog.

Inspiration

Business trends on mobile can be explored by examining this dataset.

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

Don't forget to hit the upvote🙏🙏

🔖 Overview

The Google Play App Reviews dataset contains valuable feedback from users who have reviewed apps on the Google Play Store. This dataset includes both user ratings and detailed comments, making it ideal for sentiment analysis, user experience evaluation, and app performance research.

📚 Columns Description

📊 Key Features

• ✅ Rich Feedback: Includes both ratings and textual feedback from users.
• 🌍 Global Reach: Reviews are collected from users worldwide, providing diverse insights.
• 🔒 Anonymized Data: No personally identifiable information is included.
• ⚙️ Ready for Analysis: Cleaned and pre-processed for immediate use in sentiment analysis and app performance evaluation.

🎯 Potential Use Cases

• Sentiment Analysis: Analyze user sentiment based on reviews and ratings.
• Customer Feedback: Measure user satisfaction and discover areas for improvement.
• App Version Comparison: Evaluate how different versions of the app perform based on user feedback.
• Geographic Insights: Analyze regional differences in app usage and reviews.
• Developer Interaction: Assess the effectiveness of developer responses to user reviews.

🚀 Get Started!

Ready to dive into the world of app feedback and sentiment analysis? Explore the dataset, build models to understand user sentiments, and enhance app experiences based on real feedback.

Happy coding! ✨

Google Gemini Statistics: In 2023, Google unveiled the most powerful AI model to date. Google Gemini is the world’s most advanced AI leaving the ChatGPT 4 behind in the line. Google has 3 different sizes of models, superior to each, and can perform tasks accordingly. According to Google Gemini Statistics, these can understand and solve complex problems related to absolutely anything. Google even said, they will develop AI in such as way that it will let you know how helpful AI is in our daily routine. Well, we hope our next generation won’t be fully dependent on such technologies, otherwise, we will lose all of our natural talent! Editor’s Choice Google Gemini can follow natural and engaging conversations. According to Google Gemini Statistics, Gemini Ultra has a 90.0% score on the MMLU benchmark for testing the knowledge of and problem-solving on subjects including history, physics, math, law, ethics, history, and medicine. If you ask Gemini what to do with your raw material, it can provide you with ideas in the form of text or images according to the given input. Gemini has outperformed ChatGPT -4 tests in the majority of the cases. According to the report this LLM is said to be unique because it can process multiple types of data at the same time along with video, images, computer code, and text. Google is considering its development as The Gemini Era, showing the importance of our AI is significant in improving our daily lives. Google Gemini can talk like a real person Gemini Ultra is the largest model and can solve extremely complex problems. Gemini models are trained on multilingual and multimodal datasets. Gemini’s Ultra performance on the MMMU benchmark has also outperformed the GPT-4V in the following results Art and Design (74.2), Business (62.7), Health and Medicine (71.3), Humanities and Social Science (78.3), and Technology and Engineering (53.00).

Software maintenance constitutes a large portion of the software development lifecycle. To carry out maintenance tasks, developers often need to understand and reproduce bug reports. As such, there has been increasing research activity coalescing around the notion of automating various activities related to bug reporting. A sizable portion of this research interest has focused on the domain of mobile apps. However, as research around mobile app bug reporting progresses, there is a clear need for a manually vetted and reproducible set of real-world bug reports that can serve as a benchmark for future work. This paper presents AndroR2: a dataset of 90 manually reproduced bug reports for Android apps listed on Google Play and hosted on GitHub, systematically collected via an in-depth analysis of 459 reports extracted from the GitHub issue tracker. For each reproduced report, AndroR2 includes the original bug report, an apk file for the buggy version of the app, an executable reproduction script, and metadata regarding the quality of the reproduction steps associated with the original report. We believe that the ANDROR2 dataset can be used to facilitate research in automatically analyzing, understanding, reproducing, localizing, and fixing bugs for mobile applications as well as other software maintenance activities more broadly.

This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8

This dataset contains user reviews of Nanovest, an investment application for AS stocks, gold, and cryptocurrency. The data was collected from user reviews of the Nanovest app on Google Play. The reviews, written in Indonesian, reflect users' experiences and opinions regarding the app’s features, security, and functionality. By analyzing this review data, this study aims to determine the proportion of positive and negative reviews and identify the key aspects frequently mentioned by users. The findings of this study can provide recommendations for improving service quality and application performance for cryptocurrency investment platforms in Indonesia.

This dataset was collected through web scraping using Python. A total of 2,000 reviews were gathered. After removing duplicate and irrelevant reviews through data cleaning, the final dataset consisted of 1,921 reviews. This study will classify the data into positive and negative sentiments using machine learning.

In the initial observation, the reviews showed a mix of feedback. Many users found the app beginner-friendly, while others raised concerns about its stability and compatibility.

This dataset can be useful for researchers conducting sentiment analysis in the cryptocurrency investment industry in Indonesia, helping them understand user experiences and identify areas for improvement. Additionally, it can serve as training data for machine learning models in sentiment classification. By analyzing user feedback, this dataset can serve as a foundation for investment apps to enhance application performance and preserve essential features while refining areas that need improvement in the development of cryptocurrency investment applications in Indonesia.

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

Mobile Application Coverage: The 30% Curse and Ways Forward## Purpose In this artifact, we provide the information about our benchmarks used for manual and tool exploration. We include coverage results achieved by tools and human analysts as well as plots of the coverage progression over time for analysts. We further provide manual analysis results for our case study, more specifically extracted reasons for unreachability for the case study apps and extracted code-level properties, which constitute a ground truth for future work in coverage explainability. Finally, we identify a list of beyond-GUI exploration tools and categorize them for future work to take inspiration from. We are claiming available and reusable badges; the artifact is fully aligned with the results described in our paper and comprehensively documented.## ProvenanceThe paper preprint is available here: https://people.ece.ubc.ca/mjulia/publications/Mobile_Application_Coverage_ICSE2025.pdf## Data The artifact submission is organized into five parts:- 'BenchInfo' excel sheet describing our experiment dataset- 'Coverage' folder containing coverage results for tools and analysts (RQ1) - 'Reasons' excel sheet describing our manually extracted reasons for unreachability (RQ2)- 'ActivationProperties' excel sheet describing our manually extracted code properties of unreached activities (RQ3)- 'ActivationProperties-Graph' pdf which presents combinations of the extracted code properties in a graph format.- 'BeyondGUI' folder containing information about identified techniques which go beyond GUI exploration.The artifact requires about 15MB of storage.### Dataset: 'BenchInfo.xlsx'This file list the full application dataset used for experiments into three tabs: 'BenchNotGP' (apps from AndroTest dataset which are not on Google Play), 'BenchGP' (apps from AndroTest which are also on Google Play) and 'TopGP' (top ranked free apps from Google Play). Each tab contains the following information:- Application Name- Package Name- Version Used (Latest)- Original Version- # Activities- Minimum SDK- Target SDK- # Permissions (in Manifest)- List of Permissions (in Manifest)- # Features (in Manifest)- List of Features (in Manifest)The 'TopGP' sheet also includes Google-Play-specific information, namely:- Category (one of 32 app categories)- Downloads- Popularity RankThe 'BenchGP' and 'BenchNotGP' sheets also include the original version (included in the AndroTest benchmark) and the source (one of F-Droid, Github or Google Code Archives).### RQ1: 'Coverage'The 'Coverage' folder includes coverage results for tools and analysts, and is structured as follows:- 'CoverageResults.xlsx": An excel sheet containing the coverage results achieved by each human analysts and tool. - The first tab described the results over all apps for analysts combined, tools combined, and analysts + tools, which map to Table II in the paper. - Each of the following 42 tab, one per app in TopGP, marks the activities reached by Analyst 1, Analyst 2, Tool 1 (ape) and Tool 2 (fastbot), with an 'x' in the corresponding column to indicate that the activity was reached by the given agent.- 'Plots': A folder containing plots of the progressive coverage over time of analysts, split into one folder for 'Analyst1' and one for 'Analyst2'. - Each of the analysts' folder includes a subfolder per benchmark ('BenchNotGP', 'BenchGP' and 'TopGP'), containing as many png files as applications in the benchmark (respectively 47, 14 and 42 image files) named 'ANALYST_[X]_[APP_PACKAGE_NAME]'.png.### RQ2: 'Reasons.xslx'This file contains the extracted reasons for unreachability for the 11 apps manually analyzed. - The 'Summary' tab provides an overview of unreached activities per reasons over all apps and per app, which corresponds to Table III in the paper. - The following 11 tabs, each corresponding to and named after a single application, describe the reasons associated with each activity of that application. Each column corresponds to a single reason and 'x' indicates that the activity is unreached due to the reason in that column. The top row sums up the total number of activities unreached due to a given reason in each column.- The activities at the bottom which are greyed out correspond to activities that were reached during exploration, and are thus excluded from the reason extraction.### RQ3: 'ActivationProperties.xslx'This file contains the full list of activation properties extracted for each of the 185 activities analyzed for RQ2.The first half of the columns (columns C-M) correspond to the reasons (excluding Transitive, Inconclusive and No Caller) and the second half (columns N-AD) correspond to properties described in Figure 5 in the paper, namely:- Exported- Activation Location: - Code: GUI/lifecycle, Other Android or App-specific - Manifest- Activation Guards: - Enforcement: In Code or In Resources - Restriction: Mandatory or Discretionary- Data: - Type: Parameters, Execution Dependencies - Format: Primitive, Strings, ObjectsThe rows are grouped by applications, and each row correspond to an activity of that application. 'x' in a given column indicates the presence of the property in that column within the analyzed path to the activity. The third and fourth rows sums up the numbers and percentages for each property, as reported in Figure 5.### RQ3: 'ActivationProperties-Graph.pdf'This file shows combinations of the individual properties listed in 'ActivationProperties.xlsx' in a graph format, extending the combinations described in Table IV with data (types and format) and reasons for unreachability.### BeyondGUIThis folder includes:- 'ToolInfo.xlsx': an excel sheet listing the identified 22 beyond-GUI papers, the date of publication, availability, invasiveness (Source code, Bytecode, framework, OS) and their targeting strategy (None, Manual or Automated).- ToolClassification.pdf': a pdf file describing our paper selection methodology as well as a classication of the techniques in terms of Invocation Strategy, Navigation Strategy, Value Generation Strategy, and Value Generation Types. We fully introduced these categories in the pdf file.## Requirements & technology skills assumed by the reviewer evaluating the artifactThe artifact entirely consists of Excel sheets which can be opened with common Excel visualization software, i.e., Microsoft Excel, coverage plots as PNG files and PDF files. It requires about 15MB of storage in total.No other specific technology skills are required of the reviewer evaluating the artifact.

Summary

Earth Map (earthmap.org) is a web-based FAO-Google tool for quick multi-temporal analysis of environment and climate parameters for evidence-based policies integrating cloud technologies and freely available datasets. Earth Map can analyse and display data that are already present in Google Earth Engine (earthengine.google.com) as other freely available datasets that have been gathered, processed and uploaded to the platform.

Data domains range from temperature to precipitation, fires, population, vegetation, evapotranspiration, water, land use/cover, elevation, soil, satellite images, etc. Most of the data include multi-temporal series allowing to have a time machine for several environmental parameters.

Earth Map aims to lower the access to some feature of Earth Engine through a simple graphical interface with drop-down menus. Any user can run environmental and climatic analysis on their area of interest and in a matter of few seconds.

https://data.apps.fao.org/catalog/dataset/a7116f30-254f-43c3-85ce-6756b4dd5259/resource/2d9c30c0-b593-4879-9096-1b3e87cc248a/download/earth-map-screenshot.png" alt="EarthMap Screenshot">

Application

Users without prior experience in GIS or remote sensing, but with knowledge of the land to be analysed, can use Earth Map to produce images, tables and statistics describing the environmental and climatic context and history of an area. Therefore, Earth Map can play a strategic role in providing guidance in project design but also in project monitoring and final evaluation.

Even in countries where data appear to be scarce, the remote-sensing data in Earth Engine are integrated with additional freely available datasets to provide timely analysis, customized for the objectives of the projects. The tool allows to gather an in-depth multi-temporal perspective of the environmental and climatic conditions with a focus on the study of the anomalies and their frequency.

Background

Earth Map has been developed in the framework of the FAO-Google partnership, in synergy with the FAO Hand-in-Hand Geospatial Platform and thanks to the support of the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU). The team behind Earth Map is the same team that developed Collect Earth (www.openforis.org/tools/collect-earth.html) and it is still maintaining it; Collect Earth is another FAO-Google application to produce detailed statistics of land use, land use change and forest through a point sampling approach and freely available remote sensing data.

The dataset available through the Sightings Map of Invasive Plants in Portugal results from the Citizen Science platform INVASORAS.PT, which records sightings of invasive plants in Portugal (mainland and Archipelagos of Madeira and Azores). This platform was originally created in 2013, in the context of the project “Plantas Invasoras: uma ameaça vinda de fora” (Media Ciência nº 16905), developed by researchers from Centre for Functional Ecology of University of Coimbra and of Coimbra College of Agriculture of the Polytechnic Institute of Coimbra. Currently this project is over, but the platform is maintained by the same team. Sightings are reported by users who register at the platform and submit them, either directly on the website (https://invasoras.pt/pt/mapeamento) or using an app for Android (https://play.google.com/store/apps/details?id=pt.uc.invasoras2) and iOS (https://apps.apple.com/pt/app/plantas-invasoras-em-portugal/id1501776731) devices. Only validated sightings are available on the dataset. Validation is made based on photographs submitted along with the sightings by experts from the platform INVASORAS.PT team. As with all citizen science projects there is some risk of erroneous records and duplication of sightings.

This dataset contains some stats about google play store app.

There's a story behind every dataset and here's your opportunity to share yours. Based on installs, reviews you can sort out the apps. A clear picture can be drawn of apps, you can find out apps of what category are the most expensive, most popular, have most installs. Also various comparison can be done based on the data given in the dataset.

Here are a few use cases for this project:

1. Automated Driving: The "Russian signs" model can be used in automated driving systems to help identify traffic situations and ensure compliance with traffic rules. The model could interpret signs such as speed limits, parking regulations, and various other road signs, ensuring safe navigation.

2. Traffic Rule Enforcement: Law enforcement agencies could use this model to automatically detect and record violations of traffic rules by identifying no parking zones, speed limits, and other prohibitive signs, which may go unnoticed by human officers.

3. Mapping Services: Companies like Google or Yandex can utilize this computer vision model to enhance the information provided on their mapping services. The model can help to identify and categorize different streets, areas, and conditions according to the signs present, thus giving users more detailed or specialized map information.

4. Driver-Assistance Apps: The model can be integrated into driver-assistance applications to alert drivers about upcoming signs or road conditions. For instance, if the model detects a "30Limit" sign, the app might warn the driver to slow down.

5. Traffic Management: City traffic management can use this model to analyze the needs for specific signs in different areas. By identifying the existing signs, they can assess if the current signs are enough or need new ones to maintain smooth traffic.

VLC Data: A Multi-Class Network Traffic Dataset Covering Diverse Applications and Platforms Valencia Data (VLC Data) is a network traffic dataset collected from various applications and platforms. It includes both encrypted and, when applicable, unencrypted protocols, capturing realistic usage scenarios and application-specific behavior. The dataset covers 18.5 hours, 58 pcapng files, and 24.26 GB, with traffic from: Video streaming: Netflix and Prime Video (10–50 min) via Firefox. Gaming: Roblox sessions on Windows (20–35 min), recorded outside of virtual machines, despite VM support. Video conferencing: Microsoft Teams (20 min) via Firefox. Web browsing: Wikipedia, BBC, Google, LinkedIn, Amazon, and OWIN6G (2–5 min) via Firefox or Chrome. Audio streaming: Spotify (30–33 min) on multiple OS. Web streaming: YouTube in 4K and Full HD (20–30 min). This dataset is publicly available for traffic analysis across different apps, protocols, and systems. Table Description: Type Applications Platform Time [min] Comments Filename Size (MB) Video Streaming Netflix Linux 10 Running Netflix on Firefox Browser netflix_linux_10m_01 95.1 Video Streaming Netflix Linux 20 Running Netflix on Firefox Browser netflix_linux_20m_01 167.7 Video Streaming Netflix Linux 20 Running Netflix on Firefox Browser netflix_linux_20m_02 237.9 Video Streaming Netflix Linux 20 Running Netflix on Firefox Browser netflix_linux_20m_03 212.6 Video Streaming Netflix Linux 25 Running Netflix on Firefox, but 2 min in Menu netflix_linux_25m_01 610.7 Video Streaming Netflix Linux 35 Running Netflix on Firefox, but 1 min in Menu netflix_linux_35m_01 534.8 Video Streaming Netflix Linux 50 Running Netflix on Firefox Browser netflix_linux_50m_01 660.9 Video Streaming Netflix Windows 10 Running Netflix on Firefox Browser netflix_windows_10m_01 132.1 Video Streaming Netflix Windows 20 Running Netflix on Firefox Browser netflix_windows_20m_01 506.4 Video Streaming Prime Video Linux 20 Running Prime Video on Firefox Browser prime_linux_20m_01 767.3 Video Streaming Prime Video Linux 20 Running Prime Video on Firefox Browser prime_linux_20m_02 569.3 Video Streaming Prime Video Windows 20 Running Prime Video on Firefox Browser prime_windows_20m_01 512.3 Video Streaming Prime Video Windows 20 Running Prime Video on Firefox Browser prime_windows_20m_02 364.2 Gaming Roblox Windows 20 Doesn't run in VM roblox_windows_20m_01 127.5 Gaming Roblox Windows 20 Doesn't run in VM roblox_windows_20m_02 378.5 Gaming Roblox Windows 20 Doesn't run in VM roblox_windows_20m_03 458.9 Gaming Roblox Windows 30 Doesn't run in VM roblox_windows_30m_01 519.8 Gaming Roblox Windows 30 Doesn't run in VM roblox_windows_30m_02 357.3 Gaming Roblox Windows 35 Doesn't run in VM roblox_windows_35m_01 880.4 Audio Streaming Spotify Linux 30 Running Spotify app on Ubuntu-Linux spotify_linux_30m_01 98.2 Audio Streaming Spotify Linux 30 Running Spotify app on Ubuntu-Linux spotify_linux_30m_02 112.2 Audio Streaming Spotify Linux 30 Running Spotify app on Ubuntu-Linux spotify_linux_30m_03 175.5 Audio Streaming Spotify Windows 30 Running Spotify app on Windows spotify_windows_30m_01 50.7 Audio Streaming Spotify Windows 30 Doesn't run in VM spotify_windows_30m_02 63.2 Audio Streaming Spotify Windows 33 Running Spotify app on Windows spotify_windows_33m_01 70.9 Video Conferencing Teams Linux 20 Running Teams on Firefox Browser teams_linux_20m_01 134.6 Video Conferencing Teams Linux 20 Running Teams on Firefox Browser teams_linux_20m_02 343.3 Video Conferencing Teams Linux 20 Running Teams on Firefox Browser teams_linux_20m_03 376.6 Video Conferencing Teams Windows 20 Running Teams on Firefox Browser teams_windows_20m_01 634.1 Video Conferencing Teams Windows 20 Running Teams on Firefox Browser teams_windows_20m_02 517.8 Video Conferencing Teams Windows 20 Running Teams on Firefox Browser teams_windows_20m_03 629.9 Web Browsing Web Linux 2 OWIN6G website on Firefox Browser web_linux_2m_owin6g 1.2 Web Browsing Web Linux 2 Wikipedia website on Firefox Browser web_linux_2m_wikipedia 19.7 Web Browsing Web Linux 3 OWIN6G website on Firefox Browser web_linux_3m_owin6g 4.5 Web Browsing Web Linux 3 Wikipedia website on Firefox Browser web_linux_3m_wikipedia 23.5 Web Browsing Web Linux 5 Amazon website on Chrome Browser web_linux_5m_amazon 262.9 Web Browsing Web Linux 5 BBC website on Firefox Browser web_linux_5m_bbc 55.7 Web Browsing Web Linux 5 Google website on Firefox Browser web_linux_5m_google 22.6 Web Browsing Web Linux 5 Linkedin website on Firefox Browser web_linux_5m_linkedin 39.8 Web Browsing Web Windows 3 OWIN6G website on Firefox Browser web_windows_3m_owin6g 32.6 Web Browsing Web

This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m2 for 2017; 13 m2 for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.

All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in Table 1. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.

Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey

For an interactive map demo of these datasets via Google Earth Engine Apps, visit: https://ekyzivat.users.earthengine.app/view/super-resolution-demo

Table 1: File naming scheme based on region, with some regions requiring two-scene mosaics.

Region

Landsat ID

Mosaic name

Yukon Flats Basin

LC08_L2SP_068014_20170708_20200903_02_T1

LC08_20170708_yflats_cog.tif

“

LC08_L2SP_068013_20170708_20201015_02_T1

“

Old Crow Flats

LC08_L2SP_067012_20170903_20200903_02_T1

-

Mackenzie River Delta

LC08_L2SP_064011_20170728_20200903_02_T1

LC08_20170728_inuvik_cog.tif

“

LC08_L2SP_064012_20170728_20200903_02_T1

“

Canadian Shield Margin

LC08_L2SP_050015_20170811_20200903_02_T1

LC08_20170811_cshield-margin_cog.tif

“

LC08_L2SP_048016_20170829_20200903_02_T1

“

Canadian Shield near Baker Creek

LC08_L2SP_046016_20170831_20200903_02_T1

-

Canadian Shield near Daring Lake

LC08_L2SP_045015_20170723_20201015_02_T1

-

Peace-Athabasca Delta

LC08_L2SP_043019_20170810_20200903_02_T1

-

Prairie Potholes North 1

LC08_L2SP_041021_20170812_20200903_02_T1

LC08_20170812_potholes-north1_cog.tif

“

LC08_L2SP_041022_20170812_20200903_02_T1

“

Prairie Potholes North 2

LC08_L2SP_038023_20170823_20200903_02_T1

-

Prairie Potholes South

LC08_L2SP_031027_20170907_20200903_02_T1

-

Fairbanks

LT05_L2SP_070014_19850831_20200918_02_T1

-

References:

Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. Remote Sensing, 11(18), 2163. https://doi.org/10.3390/rs11182163

Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1707

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

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Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

Read about this volunteer-driven effort, access data and apps, and contribute your own testing site data:https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-dataItem details page:https://giscorps.maps.arcgis.com/home/item.html?id=d7d10caf1cec43e0985cc90fbbcf91cbThis view is the originalCOVID-19 Testing Locations in the United States - public dataset. A backup copy also exists:https://giscorps.maps.arcgis.com/home/item.html?id=11fe8f374c344549815a716c8472832f. The parent hosted feature service is the same.This version is symbolized by type of test (molecular, antibody, antigen, or combinations thereof).This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and onfindcovidtesting.com. States and counties are encouraged to include this feature service in their own testing site locator apps as well.Please submit new testing sites or updated testing site information via this form:https://arcg.is/10S1ib. Including this link on your organization's testing site finder web app will allow testing providers to add their own sites directly to the map, improving the accuracy and completeness of the dataset.GISCorps volunteers verify each submission prior to including it in this public view. You can also add your sites in bulk by completing a copy ofthis templateand emailing it to admin@giscorps.org.This dataset is updated daily. All information is sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated byGISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Data dictionary document:https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharingArcade code for popups:https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing