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1,532 results for “App”
Using the Socialise app to collect smartphone sensor data for mental health research: A feasibility study
<p>To investigate the feasibility of collecting smartphone sensor data for mental health research, we tested the Socialise app that was developed at the Black Dog Institute in a group of people with a lived experience of mental health challenges (n=32). Bluetooth, GPS and battery status data were collected at regular intervals (3, 4, 5 or 8 minutes) for 4 weeks. In addition, survey data was collected using the app to investigate the views of participants on user experience and the acceptability of passive data collection for mental health research. No mental health data was collected as part of the feasibility study.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 3. Screenshots of the new VC app
<p>The user will be able to stay logged in inside the app and to receive different types of notifications. The application will have the ability to be set up for working offline, while users have the possibility to change the application language, which can be useful for foreign or Erasmus students. Additionally, users will be able to customize the app based on some preferences available in a settings screen, check their grades, send direct messages to other students or professors and also save important documents under “My files” section. During the beta phase of development, a focus group with teachers will be organized in order to present the new apps and to understand what new features are needed to support the courses facilitation on the go.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 2. Screenshots of the Virtual Campus mobile application (d, e)
<p>The VC app was downloaded and used by 350 students, representing a percentage of 5% of the total number of the students enrolled on the platform courses. The application has features corresponding to the desktop version of the platform. In developing it, there were applied principles related to mobile learning usability and design, trying to provide enhanced possibilities for learning on-the-go and also specific notifications (Machun et al., 2012; Harrison et al., 2013; Mocofan, 2017). Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figire 1. Screenshots of the Virtual Campus mobile application (a, b, c)
<p>Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
NIH-funded smartphone intervention apps: 2014-2018
<p>This dataset includes our categorical analysis of 399 abstracts retrieved from NIH Reporter that included some form of smartphone app and that included an intervention strategy. </p>
AURORA Energy Tracker App Dashboard on 9 September 2024
<p>Snapshots of the AURORA Energy Tracker dashboard, showing data collected on 9 September 2024 regarding average user, energy labels and gender distribution.</p> <p>The dashboard is available at https://dashboard.aurora-h2020.eu/en-GB and shows data from 6 February 2024. Note that AURORA is an ongoing citizen science project and the data on this page is provided without guarantee.</p>
Fitness App Ethical Requirements Stack
<p>An image of the Fitness App Ethical Requirements Stack</p>
Ethical Requirements Stack for Fitness App
<p>An image of the Ethical Requirements Stack for the Fitness App</p>
Fitness App
<p>Ethical Requirements Stack diagram</p>
The relationship between drugs consumption and dating app use: results from an Italian survey
<p>Open-access dataset regarding the survey "The relationship between drugs consumption and dating app use: results from an Italian survey" by L.Flesia, V.Fietta, C.Foresta and M.Monaro</p>
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
Datasets from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"
<p>We publicly release the anonymized <em>song_embeddings.parquet user_embeddings.parquet user_features_test.parquet user_features_train.parquet user_features_validation.parquet</em> datasets, with each of the TT-SVD or UT-ALS versions of embeddings, from the music streaming platform Deezer, as described in the article "<em>A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"</em> published in the proceedings of the 27TH ACM SIGKDD conference on knowledge discovery and data mining (<em>KDD 2021</em>). The paper is available <a href="https://arxiv.org/abs/2106.03819">here</a>.</p> <p>These datasets are used in the GitHub repository <a href="https://github.com/deezer/semi_perso_user_cold_start">deezer/semi_perso_user_cold_start</a> to reproduce experiments from the article.</p> <p>Please cite our paper if you use our code or data in your work.</p>
Text-fig. 2. Fossiliferous localities in the Ashawq Formation north and north-east of Thaytiniti, Dhofar Governorate, Oman (for latitude, longitude and altitude, see App. 1). The large white area either side of the north-south road is the village of Aydim (map modified from Google Earth). in Large Mammals From The Rupelian Of Oman - Recent Finds
Text-fig. 2. Fossiliferous localities in the Ashawq Formation north and north-east of Thaytiniti, Dhofar Governorate, Oman (for latitude, longitude and altitude, see App. 1). The large white area either side of the north-south road is the village of Aydim (map modified from Google Earth).
Text-fig. 6. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in endocranial view. a – shaded drawing; b – line drawing with labels. Abbreviations: app – apex partis petrosae, cc – cochlear canaliculus, crp – crista petrosa, cs – cerebral surface, iam – internal acoustic meatus, lji – lateral jugular incisure, me – mastoid exposure, mji – medial jugular incisure, saf – subarcuate fossa, sips – sulcus for inferior petrosal sinus, soev – sulcus for occipital emissary vein, sss – sulcus for sigmoid sinus, tc – transverse crest, to ptc – to posttemporal canal, va – vestibular aqueduct. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 6. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in endocranial view. a – shaded drawing; b – line drawing with labels. Abbreviations: app – apex partis petrosae, cc – cochlear canaliculus, crp – crista petrosa, cs – cerebral surface, iam – internal acoustic meatus, lji – lateral jugular incisure, me – mastoid exposure, mji – medial jugular incisure, saf – subarcuate fossa, sips – sulcus for inferior petrosal sinus, soev – sulcus for occipital emissary vein, sss – sulcus for sigmoid sinus, tc – transverse crest, to ptc – to posttemporal canal, va – vestibular aqueduct.
Analysis of websites, social media pages and apps for the development of new strategies for increasing participation of women in clinical trials
<p>For T2.5 of the i-CONSENT project, an analysis was undertaken of the strategies used to effectively communicate with women on the topic of women’s health or women’s health research, considering aspects such as tone, format and audience interaction. A total of 42 websites, social media pages and apps were included in the analysis. The attached document presents the findings from the data generation stage of this analysis.</p>
A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App
<p>Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).</p>
Bases da Revisão Sistemática da Literatura DO trabalho "Adequação para a Engenharia de Requisitos dos requisitos extraídos a partir dos feedbacks de multidão de usuários de APPS"
<p>Base Revisão Sistemática da Literatura da dissertação de mestrado <strong>"Adequação para a Engenharia de Requisitos dos requisitos extraídos a partir dos <em>feedbacks</em> de multidão de usuários de APPS".</strong></p> <p> </p> <ul> <li>BaseArtigos_RSL.xlsx - contém os estudos que participaram da revisão, os estudos selecionados, dados extraídos e sintetizados</li> <li>AvaliacaoAmeacasAvalidade.xlsx - contém o formulario de avaliação das ameaças à validade</li> </ul>
Port Places App - Zip folders of experience data
<p>These zip folders are exports from the Safarnama authoring tool on the 3rd of July, 2023. The tool was hosted at <a href="https://safarnama.lancs.ac.uk">https://safarnama.lancs.ac.uk</a> at the time of deposit. The folders contain all media items, icons, and a JSON representation of the experiences created for the Port Places app by the Ports, Past and Present project. English and Welsh Language experiences are deposited as separate items.</p> <p>For more information about Safarnama, see <a href="https://github.com/jwoodmansey/">https://github.com/jwoodmansey/</a>.</p>
Building an Ecological Momentary Assessment Smartphone App for 4- to 10-Year-Old Children: A Pilot Study
<p>The file "KIDsRawData_0111.RData" is a R data file, in which the "KIDs_data" dataframe contains EMA survey responses of all participants during the study period. </p> <p>The file "iEMA Post-study Caregiver Survey.xlsx" contains caregivers' responses to post-study survey on app usability and other.</p> <p>The file "Call Log and Reasons for not Participating.xlsx" is a list of call (to caregivers) logs. </p>
Artifact for MobiCom'23: Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility
<p>This dataset contains the anonymized failure data collected from our physical and device farms over a three-month period. The failure data involves 5,918 physical devices as well as 5,918 virtualized devices running on ARM commodity servers.</p> <p>For more details, please visit our website (<a href="https://android-emulation-testing.github.io/">Android-Emulation-Testing.github.io</a>) or read our paper:</p> <ul> <li>[MobiCom'23] Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility</li> </ul> <p>If you use our dataset in your work, please reference it using:</p> <pre><code>@inproceedings {lin2022virtual, author = {Lin, Hao and Qiu, Jiaxing and Wang, Hongyi and Li, Zhenhua and Gong, Liangyi and Gao, Di and Liu, Yunhao and Qian, Feng and Zhang, Zhao and Yang, Ping and Xu, Tianyin}, title = {{Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility}}, booktitle = {The 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom'23)}, year = {2023}, publisher = {ACM} }</code></pre> <p> </p> <p> </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.