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488 results for “mobile applications”
Design and Validation of an Assessment Tool for Educational Mobile Applications Used with Autistic Learners
<p>Appendix A and B, supplementary material to the publication:</p> <p>Sanromà-Giménez, M., Lázaro Cantabrana, J. L., Usart Rodríguez, M., & Gisbert-Cervera, M. (2021). Design and Validation of an Assessment Tool for Educational Mobile Applications Used with Autistic Learners. <em>Journal of New Approaches in Educational Research,</em> 10(1), doi: 10.7821/naer.2021.1.574</p>
Factors used to Influence Mobile Health Application Rating
<p>Over the last five years, mobile health applications (mHealthapp) have evolved exponentially to assess and support our health and well-being. This paper presents an Artificial Intelligence (AI)-enabledmHealth app rating tool which takes multidimensional measures such as starrating, user's review and features declared by the developer to generate apprating. However, currently, there is very little conceptual understanding onhow users' reviews affect app rating from a multi-dimensional perspective. This study applies artificial intelligence (AI)-based text mining technique to develop more comprehensive understanding of users' feedback based on an array of factors, determining the mHealth app ratings. Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user's star rating, user's text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users' sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer's statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Our proposed model concentrates on heart related apps found in the play store and app gallery. The findings indicate the efficacy of the model as opposed to the current device scale. This study has implications for both app developers and consumers who are using mHealth apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has been also found that the fuzzy based rating has a high variance compared to the conventional app rating whereas the fuzzy based rating shows high relationship in contrast to scoring based on expert opinion.</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>
The dataset of the paper titled "Investigating End-Users' Values for Agriculture Mobile Applications Development: A Mixed-Methods Empirical Study on Bangladeshi Female Farmers"
<p>This package includes a survey questionnaire, demographic questions, focus groups questionnaire, interview questionnaire, and 10 main values with corresponding attributes and the referred names used in this paper submitted to IST.</p>
The dataset of the paper titled "Investigating End-Users' Values in Agriculture Mobile Applications Development: An Empirical Study on Bangladeshi Female Farmers"
<p>This package includes a survey questionnaire, demographic questions, and 10 main values with corresponding attributes and the referred names used in this paper submitted to JSS.</p>
Classification of Mobile Application Reviews using Deep Language Models
<p>supplementary material for ASE 2022 "Classification of Mobile Application Reviews using Deep Language Models"</p>
Data from: A mobile application–based citizen science product to compile bird observations
<p>This repository contains the data and codes to reproduce the analysis of manuscript: "A mobile application–based citizen science product to compile bird observations":</p> <p>Nokelainen, O, Lauha, P, Andrejeff, S, Hänninen, J, Inkinen, J, Kallio, A, Lehto, HJ, Mutanen, M, Paavola, R, Schiestl-Aalto, P, Somervuo, P, Sundell, J, Talaskivi, J, Vallinmäki, M, Vancraeyenest, A, Lehtiö, A and Ovaskainen, O. 2024. A Mobile Application– Based Citizen Science Product to Compile Bird Observations. <em>Citizen Science: Theory and Practice, </em>9(1): 24, pp. 1–14. DOI: <a href="https://doi.org/10.5334/cstp.710" target="_blank" rel="noopener noreferrer">https://doi.org/10.5334/cstp.710</a></p>
A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges
<p>This is the dataset for the paper: <strong>A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges</strong></p> <p>This paper was accepted for publication at the International Conference on Software Maintenance and Evolution (ICSME 2024) - Industry Track</p> <p>Preprint: <a href="https://arxiv.org/abs/2408.09032">https://arxiv.org/abs/2408.09032</a></p> <p> </p> <h2><strong>Artifacts</strong></h2> <p>The table below describes the artifacts associated with this study.</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Purpose</strong></td> </tr> <tr> <td>SurveyResponse.csv</td> <td>The results of the online survey. This file contains only responses from participants who completed all required survey questions.</td> </tr> <tr> <td>SurveyQuestions.pdf & SurveyQuestions.tex</td> <td>These files contain the survey questions in the form they were presented to participants. They are in PDF and LaTeX format.</td> </tr> <tr> <td>ParticipantLocation.xlsx </td> <td>This file shows the location (based on Qualtircs metadata) of participants who responded to the survey.</td> </tr> <tr> <td>IRB_Approval.pdf</td> <td>This is the IRB approval document.</td> </tr> <tr> <td>freetext-responses-categorized.xlsx</td> <td>This file is related to survey questions #20, #22, and #23. It is a manual categorization of free-text responses by the authors. </td> </tr> <tr> <td>likert-figures\*.pdf</td> <td>The files in this directory are the Likert figures used in the paper.</td> </tr> <tr> <td>multichoice-questions\*.csv</td> <td>The files in this directory show the frequency of occurrence for the answer options associated with single-choice and multi-choice questions.</td> </tr> <tr> <td>code\</td> <td>Python code to generate the Likert charts and the CSV files that show the frequency of occurrence for the answer options.</td> </tr> <tr> <td>LICENSE.txt</td> <td>Open-source license compliant with OSI.</td> </tr> </tbody> </table> <h2>Code</h2> <p>The code is written in Python. Below is a description of the files:</p> <ol> <li>requirements.txt - Required packages</li> <li>MultiChoiceAnalysis.py - This file contains the logic to analyze the multichoice questions to generate the frequency of occurrence of the answer options. The CSV files are created in the parent directory</li> <li>main-mobileSecurity.py - This file reads SurveyResponse.csv (in the parent directory) and has the code to generate the likert charts and calls MultiChoiceAnalysis.py. The Likert charts are created in the parent directory.</li> </ol> <p>Perform the below steps to execute the code:</p> <ol> <li> Create a Python virtual environment: <blockquote> python -m venv venv</blockquote> </li> <li>Activate the virtual environment: <blockquote> venv\Scripts\activate</blockquote> </li> <li>Install packages <blockquote> pip install -r requirements.txt</blockquote> </li> <li>Run the Python code: <blockquote> python main-mobileSecurity.py</blockquote> </li> </ol> <p>Below are details about the code execution output:</p> <ul> <li>The CSV and PDF files will be created in the parent directory.</li> <li>The Likert charts (i.e., PDF files) are created in a directory called "likert-figures"</li> <li>The CSV files are created in a directory called "multichoice-questions"</li> <li>Elements from the CSV files are part of the write-up in the Results section of the manuscript. The numeric digits that are part of the file name correspond to the survey question.</li> <li>The data present in Tables 2 to 5 in the manuscript is obtained from the CSV files generated by the Python code. Specifically: <ul> <li>Table 2 - \multichoice-questions\survey-11-frequency_single_choice.csv</li> <li>Table 3 - \multichoice-questions\survey-12-frequency_single_choice.csv</li> <li>Table 4 - \multichoice-questions\survey-13-frequency_single_choice.csv</li> <li>Table 5 - \multichoice-questions\survey-14-frequency_single_choice.csv and \multichoice-questions\survey-15-frequency_single_choice.csv</li> </ul> </li> </ul> <p><em>Note: We include these output files in the artifacts package by default. The user can delete these directories and run the code to regenerate the files.</em></p>
Compiling Chronology of Massive and Increasing Personal Information Collection by Mobile Applications-DataSet
<p>These data are required or generated by our study. Android SDK files contain android platforms and build-tools. All Info files contain all the PIC assessment results of different versions of apks by our study. API results contain APIs extracted from 125 popular code libraries with their documentations and 1,750 app snapshots. App index files contain results of our mobile app ranking algorithm which is designed to select representative (coverage-bias-avoiding) apps in each specific year. SDKs files contain all the libraries used in our study with their history versions. The other files represent the results of our manual verification of 50 randomly picked records for each type of PI and the results of our survey on PI studied by related researches.</p>
A prototyping software engineering approach for designing and implementing model-based cloud mobile application for rationalized energy consumption
<p>The dataset used in this study comprises four files containing household consumers' energy consumption records. These records are collected at hourly intervals, providing detailed information on the energy usage patterns of the households. The dataset serves as a valuable resource for analyzing energy consumption trends, developing energy management strategies, and exploring the potential for energy efficiency improvements in residential settings.</p> <p> </p> <p> </p> <p> </p> <p> </p>
The Effect of Using Mobile Applications for Improving Oral Hygiene
ClinicalTrials.gov study NCT03109769. IPD Sharing: NO. Countries: 0. Publications: 1.
Evaluation of a Novel Mobile Phone Application ( OptiBP) for Blood Pressure Monitoring
ClinicalTrials.gov study NCT04121624. IPD Sharing: NO. Countries: 1. Publications: 1.
Feasibility and Usability of Mobile Applications for Monitoring Cutaneous Leishmaniasis Treatment in Colombia
ClinicalTrials.gov study NCT05533736. IPD Sharing: NO. Countries: 1. Publications: 7.
HappyMums Mobile Application Study
ClinicalTrials.gov study NCT06578845. IPD Sharing: NO. Countries: 6. Publications: 1.
Effectiveness of a Self-guided Mobile Application in Improving Obsessive Compulsive Disorder Symptoms
ClinicalTrials.gov study NCT06202677. IPD Sharing: NO. Countries: 1. Publications: 4.
Development of 'My Breastfeeding Guide' Mobile Nursing Application
ClinicalTrials.gov study NCT05901298. IPD Sharing: NO. Countries: 1. Publications: 3.
Mobile-Application Based Respiratory Rehabilitation For COPD
ClinicalTrials.gov study NCT05890443. IPD Sharing: NO. Countries: 1. Publications: 8.
Effectiveness of a Mobile Application in Improving Employees' Social Well-being.
ClinicalTrials.gov study NCT06495801. IPD Sharing: YES. Countries: 1. Publications: 1.
Evaluation of a 5-a-day Fruit and Vegetable Mobile Phone Application
ClinicalTrials.gov study NCT02779491. IPD Sharing: YES. Countries: 0. Publications: 1.
Mobile Application Effectiveness in Pressure Injury Care
ClinicalTrials.gov study NCT05255627. IPD Sharing: NO. Countries: 1. Publications: 2.
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)
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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.