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137 results for “Smartphone apps”
Biological soil covers: data on lichen, bryophyte and algae coverage in soils gathered by SoilSkin citizen science program using eBryoSoil app for smartphones
<p>Biological soil covers (BSC) are small-sized topsoil communities composed mainly by lichens, bryophytes and algae that cover the terrestrial surface and play an essential role in maintaining the quality of the soil. However, little is known about their distribution, conservation, and ecosystem functions. The SoilSkin citizen science project aims to expand the scientific knowledge about the distribution of biological soil covers as an important step to evaluate the vulnerability of soil ecosystems of the Iberian Peninsula in the face of global change.</p> <p>The project has a dedicated free of charge app for smartphones (eBryoSoil, available at Google Play <a href="https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US">https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US</a>) that is designed to obtain information about the coverage of the BSC communities. To use this app, users must select a sampling location and capture the three soil pictures required to complete a transect. These photographs are taken at a 27 cm distance from the soil, in a straight line with 15 meters of distance between each picture. After the acquisition of each image, users can quantify the coverage percentage of biological soil covers and select the type of habitat where the transect took place. The transect is complete when all three pictures and their respective information are uploaded.</p> <p>The data presented here contains the records from SoilSkin participants, which mainly include a characterization of the cover patterns of biological soil covers, the type of habitat and the coordinates where each record was taken. The data set is composed by 279 unique records taken by 37 unique users from 28/11/2019 to 12/12/2020, across the Iberian Peninsula. These records specifically detail the percentage of cover occupied by three types of lichen growth forms (crustose, foliose and fruticose); liverworts; two types of moss growth forms (acrocarpous and pleurocarpous); algae; and soil. Moreover, each record also contains a description of the main type of habitat where the transect took place, that was selected from a list contained in the app with the following habitats:</p> <ul> <li>Dense forest - Habitat characterized by trees of more than 2 meters tall and canopy over 60%.</li> <li>Open forest – Habitat characterized by trees with more than 2 meters tall and a canopy below 60%.</li> <li>Shrubland – Habitat characterized by woody vegetation with less than 2 meters tall.</li> <li>Grassland – Habitat characterized by herbaceous plants.</li> <li>Agricultural land – Habitat characterized by temporary or woody crops.</li> <li>Coastal habitat – Habitat characterized by a landscape where land is in contact with the sea, creating a visibly different landscape from inner terrestrial one’s.</li> <li>Urban green spaces – Habitat characterized by a landscape in which man-made structures are present.</li> </ul> <p>The database was revised to correct any possible mistakes (e.g., miscalculation of total percentages; habitat missing in some registers; removal of invalid registers).</p> <p>The data file contains the following columns:</p> <ul> <li>Date: numerical variable indicating the “day”/”month”/”year” when the register was generated.</li> <li>User_ID: categorical variable with the identification number of the user who gathered the record.</li> <li>Transect: categorical variable with the identification of the number of the transect.</li> <li>Photo_number: numeric variable that takes values of 1, 2 or 3 and corresponds with the identification of the photographs within each transect.</li> <li>Photo_label: character string with the identification of the photograph from each record.</li> <li>Register_localization: categorical variable with the identification of the geographic area where the record was done.</li> <li>Latitude: integer, variable indicating the latitude of the sampling location in decimal degrees.</li> <li>Longitude: integer, variable indicating the longitude of the sampling location in decimal degrees.</li> <li>Accuracy: integer, variable indicating the accuracy of the coordinates given by the GPS.</li> <li>Habitat_type: categorical variable with the description of the main type of habitat of the sampling location.</li> <li>Lichen_Crustose: integer, variable indicating the percentage of crustose lichen cover quantified in the record.</li> <li>Lichen_Foliose: integer, variable indicating the percentage of foliose lichen cover quantified in the record.</li> <li>Lichen_Fruticose: integer, variable indicating the percentage of fruticose lichen cover quantified in the record.</li> <li>Total_lichen: integer, variable indicating the sum of all lichen coverage quantified in the record.</li> <li>Liverwort: integer, variable indicating the percentage of liverwort cover quantified in the record.</li> <li>Moss_Acrocarpous: integer, variable indicating the percentage of acrocarpous moss cover quantified in the record.</li> <li>Moss_Pleurocarpous: integer, variable indicating the percentage of pleurocarpous moss cover quantified in the record.</li> <li>Total_ moss: integer, variable indicating the sum of all moss coverage quantified in the record.</li> <li>Algae: integer, variable indicating the percentage of algae cover quantified in the record.</li> <li>Soil: integer, variable indicating the percentage of soil visible in the record.</li> </ul> <p> </p>
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>
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>
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>
Risky decision and happiness task: The Great Brain Experiment smartphone app
<p>This resource consists of data from a risky decision and happiness task that was part of The Great Brain Experiment (GBE) smartphone app. Data were collected from 47,067 participants aged 18+ between March 8, 2013 and October 5, 2015. These anonymous unpaid participants completed the task a total of 91,058 times making approximately 2.7 million choices and 1.1 million happiness ratings in total. This resource represents at least 6,000 hours of task data. A subset of 1,858 participants also completed a depression questionnaire and answered five questions about their depression history.</p>
Energy Consumption Estimation of API-usage in Smartphone Apps via Static Analysis
<p>OPEN CALL FOR COLLECTING ENERGY PROFILES @ <a href="https://github.com/AbdulAli/replication-kit-msr-2023">https://github.com/AbdulAli/replication-kit-msr-2023</a></p> <p>Cite this work as:</p> <p>@inproceedings{bangash2023msr,<br> title={Energy Consumption Estimation of API-usage in Mobile Apps via Static Analysis},<br> author={Bangash, Abdul Ali and Jamal, Qasim and Eng, Kalvin and Ali, Karim and Hindle, Abram},<br> booktitle={2023 20th International Conference on Mining Software Repositories (MSR)},<br> pages={5721--5730},<br> year={2023},<br> organization={IEEE}<br> }</p> <p>This is the replication-kit of the paper published at MSR 2023.</p> <p>It includes:</p> <ul> <li>SQLite operations' benchmarks</li> <li>SQLite benchmarks' energy profiles</li> <li>The E-Factor Calculation program</li> </ul>
Pilot Trial of a Game Embedded in a Smartphone App for Smoking Cessation
ClinicalTrials.gov study NCT05227027. IPD Sharing: NO. Countries: 1. Publications: 2.
Smartphone-Administered App Treatment for Adults With Body Dysmorphic Disorder
ClinicalTrials.gov study NCT03221738. IPD Sharing: NO. Countries: 1. Publications: 1.
Feasibility Testing of the "MyGlucoCare" Smartphone App for Women With Gestational Diabetes
ClinicalTrials.gov study NCT07372872. IPD Sharing: NO. Countries: 0. Publications: 2.
Caminamos: A Smartphone App to Connect With Walking Partners
ClinicalTrials.gov study NCT03059901. IPD Sharing: NO. Countries: 1. Publications: 11.
Smart Linkage-to-HIV Care Via a Smartphone App
ClinicalTrials.gov study NCT02756949. IPD Sharing: NO. Countries: 1. Publications: 3.
Virtual Hope Box - Effectiveness of a Smartphone App for Coping With Suicidal Ideation
ClinicalTrials.gov study NCT01982773. IPD Sharing: NO. Countries: 1. Publications: 20.
Piloting a Smartphone App to Improve Treatment Adherence Among South African Adolescents Living With HIV
ClinicalTrials.gov study NCT04661878. IPD Sharing: NO. Countries: 1. Publications: 1.
A Proof-of-concept RCT of Version 3.0 of the Smoking Cessation Smartphone App "Smiling Instead of Smoking" (SiS)
ClinicalTrials.gov study NCT04672239. IPD Sharing: NO. Countries: 1. Publications: 5.
Version 2 of the Smoking Cessation Smartphone App "Smiling Instead of Smoking" (SiS)
ClinicalTrials.gov study NCT03951766. IPD Sharing: NO. Countries: 1. Publications: 10.
Promoting Radon Testing Via Smartphone App: A Clinical Trial in a High Radon State
ClinicalTrials.gov study NCT04980521. IPD Sharing: NO. Countries: 1. Publications: 1.
The PortionSize Smartphone App Pilot (PS Pilot)
ClinicalTrials.gov study NCT04494971. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluating Household Food Behavior With a Smartphone App
ClinicalTrials.gov study NCT03309306. IPD Sharing: NO. 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.
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.
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.