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5 results for “google play”
Google Play Store Data
<p>Mobile App Stores such as Google, Apple have wide range of applications to suffice every need of customers in the digital platform. Customer feedback and ratings has always been one of the major metrics that can be used to review the performance and accordingly provide suitable recommendations to enhance the functionality. The Given dataset contain the feedback of the customer regarding the app used in app store.</p> <p>Data Set Column Details are as given below:</p> <p><strong>Column name: </strong></p> <p><strong>Description</strong>:</p> <p><strong>Column Name in Working Sheet </strong></p> <p><strong>Datatype</strong></p> <p><strong>Please read the Readme.docs file</strong></p> <p> </p>
A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store
<p>This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the <a href="https://github.com/mooselab/Sports-Apps-Analysis">replication package</a> to direct you to help replicate it for your dataset too. </p> <p>Note: The dataset given are protected with password, and the password is available in our published paper</p>
Benchmark results for Vellamo and AnTuTu from Google Play in Xiaomi Redmi Note 4
<p>The dataset contains the print screens of the execution of following benchmark: Vellamo Browser, Vellamo Metal, Vellamo Multicore, and AnTuTu General. The benchmark was repeated until the smartphone reaches a battery level below 20%. For each benchmark, we recorded the numerical results for each microbenchmark. Each benchmark was evaluated using different approaches for CPU frequency scaling: Ondemand, Performance, Interactive, HS, ZT, OUR-G-0.1, OUR-G-0.5 and OUR-G-0.9.</p> <p>Ondemand, Performance, and Interactive are native from Android OS.</p> <p>OUR-G-0.1, OUR-G-0.5 and OUR-G-0.9 referrers to our proposed method to save energy, executed in smartphone Xiaomi Redmi Note 4.</p> <p>The approaches HS is from:<br> @article{hshen2013,<br> author = "H. Shen and Y. Tan and J. Lu and Q. Wu and Q. Qiu",<br> title = "Achieving Autonomous Power Management Using Reinforcement Learning",<br> journal = "ACM Transactions on Design Automation of Electronic Systems (TODAES)",<br> volume = "18",<br> month = "",<br> number = "2",<br> year = "2013",<br> pages = "24-32"<br> }</p> <p>and ZT in from:<br> @inproceedings{ztian2018,<br> author = "Zhongyuan Tian and Zhe Wang and Haoran Li and Peng Yang and Rafael Kioji Vivas Maeda and Jiang Xu",<br> title = "Multi-device collaborative management through knowledge sharing",<br> booktitle = "Proc. 23rd Asia and South Pacific Design Automation Conference (ASP-DAC)",<br> month = "" ,<br> year = "2018",<br> pages = "22-27"<br> }</p> <p>The energy.txt file contains the acquisition of voltage and current from smartphone fuel gauge.</p> <p>The other files are self-explained.</p> <p> </p> <p> </p>
Google Play Store Permission Analysis
<p>A tool designed to evaluate the risk of Android apps based on permissions, size anomalies, and other factors. It fetches datasets from Kaggle, processes the data, calculates risk scores using a weighted system, and generates insightful visualizations to understand risk distribution across app categories.</p> <h3>Key Features:</h3> <ul> <li><strong>Data Handling</strong>: Downloads, cleans, and preprocesses datasets for analysis.</li> <li><strong>Risk Scoring</strong>: Analyzes permissions, app size anomalies, and other patterns to assign risk scores.</li> <li><strong>Visual Insights</strong>: Creates heatmaps, bar charts, and scatter plots to visualize correlations and risk distributions.</li> </ul> <h3>Project Outputs:</h3> <ul> <li>Cleaned data, analysis reports, and visualizations.</li> </ul> <p>Licensed under Creative Commons Attribution 3.0.</p>
AppSet: Most Popular Google Play Apps in December 2023
<p>APK files obtained from AndroZoo and metadata retrieved from Google Play for the most popular apps for all app categories from Google Play in December 2023. Used in our ACSAC'24 paper "Manifest Problems: Analyzing Code Transparency for Android Application Bundles".</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.