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1,532 results for “App”
Exploring Transparency Concerns in Social Media App Reviews: A Human-LLM Perspective
<p>This is a dataset for the "Exploring Transparency Concerns in Social Media App Reviews: A Human-LLM Perspective." paper submitted to EASE2025.</p>
GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal
<p>GNOSIS Instructional videos.</p>
The Dataset of the PhD thesis titled "Operationalizing Values in Mobile Applications: A Mixed-Methods Empirical Study on Agriculture Apps for Bangladeshi Female Farmers"
<p>This package includes the survey (PVQ) questionnaire, demographic questions, focus groups questionnaire, interview questionnaire, and member checking summary used in this thesis.</p>
GLARE: Google Apps Arabic Reviews Dataset
<p>This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.</p>
Replication Package - How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis
<p>This is the replication package for the paper titled "How Do Requirements Evolve During</p> <p>Elicitation? An Empirical Study Combining Interviews and App Store Analysis", by Alessio Ferrari, Paola Spoletini and Sourav Debnath.</p> <p> </p> <p>The package contains the following folders and files. </p> <p> </p> <p>**<strong>/Experiment Material</strong>**</p> <p>This folder contains the material used for the experiment, and provided to the participants.</p> <p>In particular, it includes the following files:</p> <p> </p> <p>- Happy CampingTM_briefdescription.pdf/docx: brief description of the product for which requirements need to be elicited</p> <p>- Hw_description.pdf/docx: desciption of the tasks to be performed by the participants</p> <p>- Modeling_Intro_Slides.pdf: introductory slides to modelling for requirements engineering</p> <p>- Self-assessment Questionnaire.pdf: first questionnaire to self-assess the mistakes, from the SaPeer method (https://doi.org/10.1007/s00766-020-00334-0) </p> <p>- Self-assessment Questionnaire (Second Interview).pdf: second questionnare to self-assess the mistakes, from the Sapeer method</p> <p> </p> <p>**<strong>/R-analysis</strong>**</p> <p> </p> <p>This is a folder containing all the R implementations of the the statistical tests included in the paper, together with the source .csv file used to produce the results. Each R file has the same title as the associated .csv file. The titles of the files reflect the RQs as they appear in the paper. The association between R files and Tables in the paper is as follows:</p> <p> </p> <p>- RQ1-1-analyse-story-rates.R: Tabe 1, user story rates </p> <p>- RQ1-1-analyse-role-rates.R: Table 1, role rates</p> <p>- RQ1-2-analyse-story-category-phase-1.R: Table 3, user story category rates in phase 1 compared to original rates</p> <p>- RQ1-2-analyse-role-category-phase-1.R: Table 5, role category rates in phase 1 compared to original rates</p> <p>- RQ2.1-analysis-app-store-rates-phase-2.R: Table 8, user story and role rates in phase 2</p> <p>- RQ2.2-analysis-percent-three-CAT-groups-ph1-ph2.R: Table 9, comparison of the categories of user stories in phase 1 and 2</p> <p>- RQ2.2-analysis-percent-two-CAT-roles-ph1-ph2.R: Table 10, comparison of the categories of roles in phase 1 and 2. </p> <p> </p> <p>The .csv files used for statistical tests are also used to produce boxplots. The association betwee boxplot figures and files is as follows. </p> <p> </p> <p>- RQ1-1-story-rates.csv: Figure 4 </p> <p>- RQ1-1-role-rates.csv: Figure 5</p> <p>- RQ1-2-categories-phase-1.csv: Figure 8</p> <p>- RQ1-2-role-category-phase-1.csv: Figure 9</p> <p>- RQ2-1-user-story-and-roles-phase-2.csv: Figure 13</p> <p>- RQ2.2-percent-three-CAT-groups-ph1-ph2.csv: Figure 14</p> <p>- RQ2.2-percent-two-CAT-roles-ph1-ph2.csv: Figure 17</p> <p>- IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv: Figure 15</p> <p>- IMG-only-RQ2.2-frequent-roles.csv: Figure 18</p> <p> </p> <p>NOTE: The last two .csv files do not have an associated statistical tests, but are used solely to produce boxplots.</p> <p> </p> <p>**<strong>/Data-Analysis</strong>**</p> <p> </p> <p>This folder contains all the data used to answer the research questions. </p> <p> </p> <p>**<strong>RQ1.xlsx</strong>**: includes all the data associated to RQ1 subquestions, two tabs for each subquestion (one for user stories and one for roles). The names of the tabs are self-explanatory of their content.</p> <p> </p> <p>**<strong>RQ2.1.xlsx</strong>**: includes all the data for the RQ1.1 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: for each category of user story, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that category for phase 1, and the second one reports the</p> <p>number of user stories in that category for phase 2, considering the specific analyst. </p> <p> </p> <p>* Data Source-role: for each category of role, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that role for phase 1, and the second one reports the</p> <p>number of user stories in that role for phase 2, considering the specific analyst. </p> <p> </p> <p>* RQ2.1 rates: reports the final rates for RQ2.1. </p> <p>NOTE: The other tabs are used to support the computation of the final rates.</p> <p> </p> <p>**<strong>RQ2.2.xlsx</strong>**: includes all the data for the RQ2.2 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.2-category-group: comparison between groups of categories in the different phases, used to produce Figure 14</p> <p> </p> <p>* RQ2.2-role-group: comparison between role groups in the different phases, used to produce Figure 17</p> <p> </p> <p>* RQ2.2-specific-roles-diff: difference between specific roles, used to produce Figure 18</p> <p> </p> <p>**<strong>NOTE:</strong>** the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.2-single-US-category.xlsx</strong>**: includes the data for the RQ2.2 subquestion associated to single categories of user stories.</p> <p>A separate tab is used given the complexity of the computations. </p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Totals: total number of user stories for each analyst in phase 1 and phase 2</p> <p> </p> <p>* Results-Rate-Comparison: difference between rates of user stories in phase 1 and phase 2, used to produce the file</p> <p>"img/IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv", which is in turn used to produce Figure 15</p> <p> </p> <p>* Results-Analysts: number of analysts using each novel category produced in phase 2, used to produce Figure 16.</p> <p>NOTE: the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.3.xlsx</strong>**: includes the data for the RQ2.3 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.3-categories: novel categories produced in phase 2, used to produce Figure 19</p> <p> </p> <p>* RQ2-3-most-frequent-categories: most frequent novel categories</p> <p> </p> <p>**<strong>/Raw-Data-Phase-I</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx, plus the file of the original user stories with annotations (original-us.xlsx). Each file contains two tabs:</p> <p> </p> <p>- Evaluation: includes the annotation of the user stories as existing user story in the original categories (annotated with "E"), novel user story in a certain category (refinement, annotated with "N"), and novel user story in novel category (Name of the category in column "New Feature"). **<strong>NOTE 1:</strong>** It should be noticed that in the paper the case "refinement" is said to be annotated with "R" (instead of "N", as in the files) to make the paper clearer and easy to read. </p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Raw-Data-Phaes-II</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx. Each file contains two tabs:</p> <p> </p> <p>- Analysis: includes the annotation of the user stories as belonging to existing original </p> <p>category (X), or to categories introduced after interviews, or to categories introduced </p> <p>after app store inspired elicitation (name of category in "Cat. Created in PH1"), or to </p> <p>entirely novel categories (name of category in "New Category").</p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Figures</strong>**</p> <p> </p> <p>This folder includes the figures reported in the paper. The boxplots are generated from the </p> <p>data using the tool http://shiny.chemgrid.org/boxplotr/. The histograms and other plots are </p> <p>produced with Excel, and are also reported in the excel files listed above. </p>
BirdNET App data: 2020 case studies
<p>Datasets for four case studies used to test whether BirdNET App data could be used to replicate known patterns in avian ecology and thus serve as a reliable research resource. The case studies were selected based on the availability of results we could attempt to reproduce (e.g., song dialect mapping has been somewhat limited at broad scales because of data limitations) and to encompass a range of phenomena (e.g., simple vs complex songs and the migration of small vs large populations). Researchers interested in collaborations using app data are also encouraged to email <<a href="mailto:ccb-birdnet@cornell.edu">ccb-birdnet@cornell.edu</a>>.</p> <p>Please note that BirdNET confidence scores are <em><strong>not </strong></em>equivalent to probabilities and are unique to each species. That is, a confidence score of 0.85 may have a very different probability of being correct for species A and species B.</p>
Benchmark (Android apps) used for evaluating PermDroid
<p>Real-world Android Apps used in the evaluation part of an ISSTA paper "PermDroid: Automatically Testing Permission-Related Behaviour of Android Applications". </p>
Twindroid - System calls traces - Android apps - Extention
<p>TwinDroid - a dataset of system calls traces, from both benign and infected Android apps.</p> <p>For other traces you can visit https://zenodo.org/record/6464808#.Yls2yVyZNH4</p> <p>To trace apps, you can use our script in: https://github.com/RaphaelKhoury/automated-apk-tracing</p> <p> </p> <p> </p>
GUI Screenshots EV reservation app
<p>The PDF document contains the screenshots of the developed prototype app to demonstrate the applicability of the reservation system for distributing the EV station utilization. It belongs to the article "How to distribute charging requests of electronic vehicles? A reservation-based approach".</p>
Supporting Developers in Addressing Human-centric Issues in Mobile Apps
<p>A replication package including a manually analysed dataset of a random sample of 1,200 app reviews and 1,200 issue comments from 12 diverse projects that exist on both Google App Store and GitHub, the results of our machine learning and deep learning approaches, our survey questions and raw responses from app developers to the survey.</p>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Data From: An App for Tree Trunk Diameter Estimation from Coarse Optical Depth Maps
<p><strong>Overview</strong></p> <p>This dataset contains RGB and depth images (raw images and text files) of tree trunks collected using Google ARCore. It has 378 samples from four countries, with DBH ranging from 4.8-155.3cm. We annotated aspects like trunk shapes, leaning, lighting conditions when captured, etc. The data was originally intended for evaluating our designed tree diameter measurement app (https://github.com/MingyueX/GreenLens).</p> <p><em>Note: treedata.zip is deprecated, please download treedata_version_2.zip instead.</em></p> <p><strong>Data introduction</strong></p> <ul> <li>treedata_version_2.zip contains two folders - <em>rgb </em>and <em>depthtxt </em>- representing raw RGB images and depth text files respectively.</li> <li>data_label.xlsx includes the labels and corresponding descriptions.</li> </ul> <p><strong>Usage Notes</strong></p> <p>The <em>rgb </em>folder contains jpg images with 480*360 resolution.</p> <p>The files with no extension in the <em>depthtxt</em> folder are stored in the format as x-coordinate of pixel, y-coordinate of pixel, depth measurement of pixel and confidence value.</p>
Cook app dataset
Open the record for dataset details and reuse information.
Augmented Reality App Attributes on Consumer Perceived Values
Open the record for dataset details and reuse information.
Data Leveraging Digital Innovation: Enhancing Tourist Intentions through Social Media, Websites, and Mobile App Experiences
Open the record for dataset details and reuse information.
Intention to use Mobile Learning APPs in university students
Open the record for dataset details and reuse information.
An Empirical Catalog of Code Smells for the Presentation Layer of Android Apps: Appendix
<p>The appendix of our "An Empirical Catalog of Code Smells for the Presentation Layer of Android Apps" paper.</p>
FIGURE 3 in Taxonomic description of a new species of Leucoagaricus and utilization of the Pantone Connect app in classification research
FIGURE 3. Microscopic structures of Leucoagaricus brunneorube. A: Basidia. B: Pileipellis. C: Cheilocystidia. D: Basidiospores. Bars=10 μm.
FIGURE 1 in Taxonomic description of a new species of Leucoagaricus and utilization of the Pantone Connect app in classification research
FIGURE 1. ML analysis of Leucoagaricus based on combined ITS and LSU sequence dataset, with Agaricus bisporus as outgroup taxon. Bootstrap values (BP) ≥ 50% from ML analysis and Bayesian posterior probabilities (BPP) ≥ 0.95 are shown at the supported branches. The newly generated sequences are shown in bold.
Food Safety and Online Food Delivery Apps Post Covid-19
<p>The data collection method that will be used will be questionnaires that will be made available online and written in Indonesian. Questionnaires were distributed during the post outbreak of COVID-19</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.