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1,532 results for “App”
Dataset for article "Prevalence of Depressive Symptoms Among the US Population During COVID-19 (2021-2022): A Study Using the Goodville App"
<p>This dataset contains PHQ-8 data obtained using Goodville app. The dataset was used for preparing research article "Prevalence of Depressive Symptoms Among the US Population During COVID-19 (2021-2022): A Study Using the Goodville App". The first page of excel table includes all US PHQ-8 data. The second page includes only valid PHQ-8 patterns after Rasch analysis. </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>
Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions
<p>This is the dataset for the paper: Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions</p> <p>This paper was accepted for publication at the 58th Hawaii International Conference on System Sciences (HICSS) - Software Technology Track</p> <p>Preprint: <a href="https://arxiv.org/abs/2409.07945">https://arxiv.org/abs/2409.07945</a></p>
Mobile App Security Trends and Topics: An Examination of Questions From Stack Overflow
<p>This is the dataset for the paper: Mobile App Security Trends and Topics: An Examination of Questions From Stack Overflow</p> <p>This paper was accepted for publication at the 58th Hawaii International Conference on System Sciences (HICSS) - Software Technology Track</p> <p>Preprint: <a href="https://arxiv.org/abs/2409.07926">https://arxiv.org/abs/2409.07926</a></p>
Dataset: Gold standard dataset for explainability need detection in app reviews.
<p>We crawled 90,000 app reviews from both Google Play Store and Apple App Store, including reviews from both free and paid apps. These reviews were filtered for explainability needs, and after this process, 4,495 reviews remained. Among them, 2,185 reviews indicated an explanation need, while 2,310 did not. This resulting gold standard dataset was used to train and evaluate several machine learning models and rule-based approaches for detecting explanation needs in app reviews.</p> <p>The dataset includes both balanced and unbalanced evaluation sets, as well as the original crawled data from October 2023. In addition to machine learning approaches, rule-based methods optimized for F1 score, precision, and recall are also included.</p> <p>We provide several pre-trained machine learning models (including BERT, SetFit, AdaBoost, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Random Forest, and SVM) along with training scripts and evaluation notebooks. These models can be applied directly or retrained using the included datasets.</p> <p>For further details on the structure and usage of the dataset, please refer to the README.md file within the provided ZIP archive.</p>
CORE APP - Line 1 winning entry
Open the record for dataset details and reuse information.
RARE((Repository for App review REfinement)
<p># This directory contains the Benchmark RARE Dataset and Code File as described in the paper:</p> <p><br>- RARE_Dataset: In this folder, we introduce RARE, a benchmark for App Review Refinement. This folder contains two subfolders named Gold_Corpus and Silver_Corpus.</p> <p>1. Gold_Corpus: In this folder, a corpus of 10,000 annotated reviews, collaboratively refined by software engineers and a large language model (LLM) sourced from 10 different application domains, is provided.</p> <p>2. Silver_Corpus: This folder includes a set of 10,000 automatically refined reviews using the best-performing model, Flan-T5, which was trained on 10,000 reviews from the gold corpus, forming the silver corpus.</p> <p> </p> <p><br>- Code_File: In this folder, all the code files used in the entire experiment and research are provided. This folder contains four subfolders named Data_Extraction, Refined_Review_Generation_through_Prompting, Model_Finetuning_and_Inferences, and Result_Evaluation.</p> <p>1. Data_Extraction: This folder contains 2 Python files named 'Google_Play_Store_Reviews_Extraction_from_10_different_App.py', which was used for extracting 10,000 raw reviews from the Google Play Store, and 'Apple_App_Store_Reviews_Extraction_from_10_different_App.py', which was used for extracting 10,000 raw reviews from the Apple App Store.</p> <p>2. Refined_Review_Generation_through_Prompting: This folder contain a Python file named 'Prompting_GPT_3.5_TURBO_For_Refined_Review_Generation.py', which was used to guide GPT-3.5-Turbo in generating refined versions of the raw reviews.</p> <p>3. Model_Finetuning_and_Inferences: This folder contains 16 Python files: one for fine-tuning and another for inference, each for eight models, including BART, Flan-T5, Pegasus, Llama-2, Falcon, Mistral, Orca-2, and Gemma.</p> <p>4. Result_Evaluation: This folder contains 2 Python files: 'Reference_free_Automatic_Metrics_Evaluation.py' for evaluating reference-free metrics such as FKGL, FKRE, LEN, and SS, and 'Reference_Based_Automatic_Metrics_Evaluation.py' for evaluating reference-based metrics such as SARI and BERTScore Precision.</p>
Substrate induced currents through the monoamine transporters and single cell uptake of the fluorescent substrate APP+
<p>The concentrative power of the transporters for dopamine (DAT), norepinephrine (NET) and serotonin (SERT) is thought to be fueled by the transmembrane Na<sup>+</sup> gradient, but it is conceivable that they can also tap other energy sources, e.g. membrane voltage and/or the transmembrane K<sup>+</sup> gradient. We address this by recording uptake of endogenous substrates or the fluorescent substrate APP<sup>+</sup> ((4-(4-dimethylamino)phenyl-1-methylpyridinium) under voltage control in cells expressing DAT, NET or SERT. We show that DAT and NET differ from SERT in intracellular handling of K<sup>+</sup>. In DAT and NET, substrate uptake was voltage-dependent due to the transient nature of intracellular K<sup>+</sup> binding, which precluded K<sup>+</sup> antiport. SERT, however, antiports K<sup>+</sup> and achieves voltage-independent transport. Thus, there is a trade-off between maintaining constant uptake and harvesting membrane potential for concentrative power, which we conclude to occur due to subtle differences in the kinetics of co-substrate ion binding in closely related transporters.</p>
Data and code for the shiny app of the paper Economic disparity among generations under Paris Agreement
<p>This datasets contains the code and data used to generate the website <a href="https://climate-change.shinyapps.io/generation_disparity/?_ga=2.163158204.1233661101.1626298966-1055362809.1623199649">https://climate-change.shinyapps.io/generation_disparity/</a> in the paper Economic disparity among generations under Paris Agreement.</p>
NeuCA Shiny App sc-RNA Training Data
<p>Data to train classifiers for NeuCA shiny app.</p>
MySQL dump file for the mechismows server-side app
<p>This mysql dump file provides the data for the restful api implemented in the mechismows app: <a href="https://github.com/reactome-fi/mechismows">https://github.com/reactome-fi/mechismows</a>. The app is archived here: <a href="https://doi.org/10.5281/zenodo.5590949">https://doi.org/10.5281/zenodo.5590949</a>. </p>
Detecting Code Smells in React-based Web Apps
<p>DBR</p>
App Store Reviews
<p><strong>App Store Reviews</strong></p> <ul> <li>Apple App Store</li> <li>Google Play Store</li> </ul>
Refactoring React-based Web Apps
<p>Refactoring React-based Web Apps - Dataset</p>
Towards Fine-granularity Malicious Component Detection for Android Apps
<p>These files are the experiment data of AMCDroid.</p> <p> (1) is the dataset used in the experiment (we do not use the VirusShare dataset in 2019 since we failed to download it);</p> <p> (2) is the malicious component detection result of the running example;</p> <p> (3) to (7) are the experiment data in RQ1 to RQ5;</p> <p> (8) is the malicious component detection result of Roaming Mantis; and</p> <p> (9) is the malicious component detection result in internal validity.</p> <p>We will open source the code of AMCDroid after our paper is accepted.</p>
Replication package for PHP code smells in web apps: evolution, survival and anomalies
<p>Replication package (dataset and programs/scripts) and extra documents for article:</p> <p><strong>PHP code smells in web apps: evolution, survival and anomalies</strong></p> <p>Folder zips RQ1-5 - Extra graphics for all applications studied. In the article, due to lack of space, we only present graphics for two applications.</p> <p>Data folder zip - data used in the study and suitable for replication. The folder is divided in subfolders and there is an "explanation.txt".</p> <p>scripts.zip - PHP scripts used to pre-process data</p> <p>R scripts.zip - R scripts used to analyze and graphics</p>
Explanation Needs in App Reviews: Taxonomy and Automated Detection
<p><strong>Replication package for our paper submission to RE 2023</strong></p> <p>It contains the following files:</p> <ol> <li>Our dataset of 5,564 app reviews that we manually labeled with respect to the tags "explanation need present" and "explanation need not present".</li> <li>Code/notebooks that we used for the training and evaluation of our explanation need detection approaches</li> <li>.bin file of our best performing model</li> </ol>
Self-Presentation Differences in a Dating App: Results
<p>Results from the 'Self-Presentation Variations in a Dating Application' project. It includes the shareable processed data, which has been anonymized and simplified, as produced by the published analysis code. It also includes the necessary code to generate all the graphs and tables.</p>
Dataset for validation of app myjump2
<p>The dataset shows the results obtained during the data collection</p>
Datasets and Trained Models for "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"
<p>Datasets and Trained models for ICSE 2020 "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"</p>
ScienceDex guides
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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.