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ShareScore release 0.9.0
Dataset results
842 results for “Smartphone”
Evaluating Household Food Behavior With a Smartphone App
ClinicalTrials.gov study NCT03309306. IPD Sharing: NO. Countries: 1. Publications: 1.
Smartphone-based Utility of the Vestibulo-ocular Reflex
ClinicalTrials.gov study NCT06856746. IPD Sharing: YES. Countries: 1. Publications: 8.
A Smartphone-based Application Post-myocardial Infarction to Manage Cardiovascular Disease Risk
ClinicalTrials.gov study NCT03416920. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Step Away: Comparing a Chatbot-delivered Alcohol Intervention With a Smartphone App
ClinicalTrials.gov study NCT04447794. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The Influence of a Medication Adherence Smartphone Application on Medication Adherence in Chronic Illness
ClinicalTrials.gov study NCT05098743. IPD Sharing: NO. Countries: 1. Publications: 2.
A Smartphone Intervention for WIC Mothers to Improve Nutrition and Weight Gain During Pregnancy
ClinicalTrials.gov study NCT04028843. IPD Sharing: NO. Countries: 1. Publications: 4.
Impact of Preanesthetic Information and Behavioral Intervention Using Smartphone on Anxiety of Children
ClinicalTrials.gov study NCT02246062. IPD Sharing: Not stated. Countries: 1. Publications: 23.
Evaluation of a New 6 Minute Walk Test Smartphone App in Patients With Pulmonary Hypertension
ClinicalTrials.gov study NCT04633538. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Smartphone and 3D Printing Based Home Rehabilitation System for Chronic Stroke
ClinicalTrials.gov study NCT04363944. IPD Sharing: YES. Countries: 1. Publications: 2.
CareConekta: A Smartphone App to Improve Engagement in HIV Care
ClinicalTrials.gov study NCT03836625. IPD Sharing: YES. Countries: 1. Publications: 4.
Can a Smartphone App That Includes a Chatbot-based Coaching and Incentives Increase Physical Activity in Healthy Adults?
ClinicalTrials.gov study NCT03384550. IPD Sharing: YES. Countries: 1. Publications: 2.
Data from: Advancing mold identification in the routine laboratory: Performance of smartphone-based imaging and a newly developed Convolutional Neural Network
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Emergent smartphone users' dataset
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Risky decision and happiness task: The Great Brain Experiment smartphone app
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Effect of smartphone location on pharmacy students’ attention and working memory
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Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips
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Supplementary evaluation files for the paper: Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones
<p>This package contains evaluation supplementary files for the paper: <em>Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones</em> by Miroslav Opiela and František Galčík.</p> <p><strong>Contents: </strong></p> <ul> <li>ground_truth - real positions of checkpoints for given input files</li> <li>input - sensor measurements recordings with initial positions (also after floor transitions) and checkpoint labels </li> <li>maps - processed map models containing positions of points and connections (e.g., walls) in custom coordinate system. Reference to GNSS and map rotation is inducted in maps-meta.xml</li> <li>output - data processed by the localization system. JSON containing the applied method, its configuration, and all estimated positions. Errors for every folder are summarized in the csv file</li> <li>visualization - trajectories visualized for selected output files</li> <li>readme.txt - describes data formats used for particular files in this dataset and summarizes output files</li> </ul> <p><strong>Venues</strong></p> <p>Data are recorded in three buildings:</p> <ul> <li>codename: SA1, SA1_rotated - recorded by the author in the faculty building (Park Angelinum 9, 04001, Košice, Slovakia) using Lenovo tablet</li> <li>codename: AtlantisR0, AtlantisR-1, AtlantisR+1, AtlantisR+2 - the shopping mall Atlantis Le Centre (Boulevard Salvador Allende, 44800 Saint-Herblain, France). Dataset is from IPIN 2018 competition and loc_20180922_160206 is recorded by the author using Xiaomi Mi 5.</li> <li>codename: CNR_0, CNR_1, CNR_2 - the research institute building CNR (Via Giuseppe Moruzzi, 56127 Pisa, Italy). Dataset is from IPIN 2019 competition. </li> </ul> <p><strong>Used datasets</strong></p> <p>A subset of input data is derivated from available logfiles provided by organizers of IPIN 2018 and IPIN 2019 competitions:</p> <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a> </li> </ul> <p><strong>Funding</strong></p> <p>The work was partially supported by the Slovak Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grant no. 1/0056/18 and by the Slovak Research and Development Agency under the contract no. APVV-15-0091.</p> <p><strong>Contact</strong></p> <p>For any further questions, please contact:</p> <p>Miroslav Opiela, miroslav.opiela@upjs.sk Institute of Computer Science, Faculty of Science, P. J. Šafárik University (UPJS), Košice, Slovakia</p>
Período de um pêndulo utilizando o sensor de luminosidade de um Smartphone e o aplicativo Phyphox
<p>Neste Tutorial vamos utilizar o aplicativo de celular phyphox como instrumento para coleta de dados acerca do período de um pêndulo. Os valores obtidos conduzem ao valor da aceleração da gravidade local com bom grau de aproximação e erro estatístico ou precisão na medida da ordem de 3% e Exatidão da ordem de 1,1%. Outras medidas foram realizadas conduzindo a valores sempre desta ordem de grandeza.</p> <p>Propostas de experimentação para aulas remotas. Espero que apreciem esta proposta e que possa contrinuir com suas aulas presenciais ou remotas</p>
Are Smartphones and Fitness Apps Fit For Purpose? An experimental study.
<p>Data set for treadmill and fitness apps study</p>
Smartphones
<p>Dataset de smartphones</p> <p>jplazaf@uoc.edu</p> <p>dvilloria@uoc.edu</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.