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204 results for “mobile phone”
Mobile phone data for forests in Szklarska Poreba and Swieradow Forest District
<p><strong>Mobile phone data: </strong>Data were collected for forest in 395 base fields (750 m × 750 m). The scope of data collected covers the period from January 1, 2019 to December 31, 2019. Unique user visits were counted in the base fields. A unique visit to the base field was considered to be a visit that occurred on a specific day in a different time period. There are 5 time periods separated: 6:00 - 10:00, 10:00 - 14:00; 14:00 - 18:00, 18:00 - 22:00, 22:00 - 6:00. Mobile phone data were collected to determine the spatial distribution of social activities in forest areas.The fully anonymized data was acquired from Selectivv. It collects information about mobile phone users (over 20 million users in Poland). The scope of data collected by Selectivv includes: user locations; timestamps; data from applications (350,000 applications) and websites (about 17 million pages), where users consent to data collection for better content profiling.</p> <p> </p> <p><strong>Data description:</strong> type - vector layer, column N - number of visits, coordinate system - 2180</p>
Simulated Self-user Shadowing for Mobile Phone Antennas at 28 GHz and at 60 GHz
<p>The purpose of this dataset is to supplement the data presented in our conference publication "Self-user shadowing effects of millimeter-wave mobile phone antennas in a browsing mode" at EuCAP 2019 (see <a href="https://ieeexplore.ieee.org/document/8739947">https://ieeexplore.ieee.org/document/8739947</a>).</p> <p>This dataset contains the 3-D surface meshes of the two numeric human body models used in the above publication. One body model holds the mobile phone with one hand (vertically, "OneHand") and the other body model with both hands (horizontally, "TwoHand"). The body models were initially exported from MakeHuman (<a href="http://www.makehumancommunity.org">http://www.makehumancommunity.org</a>), the actual body postures were then created with Blender 3D Creation Suite (<a href="https://www.blender.org">https://www.blender.org</a>), and these final body models were exported in OBJ format (a generic geometry definition file format). Then these models were imported into CST Studio Suite (<a href="http://www.cst.com">http://www.cst.com</a>) in order to simulate the 3-D realised-gain patterns of the antenna. The material properties of the human body model used in the simlations are described in detail in the above publication. Also the dual-polarised mobile-phone antenna design with one vertical feed port and one horizontal feed port is described in detail within the above publication (see Fig. 3) and is not part of this dataset. (Note that "port #1" in Fig. 3 of the publication denotes the vertical antenna port for the <em>one-hand</em> case, while "port #1" denotes the horizontally antenna port in the <em>two-hand</em> case.)</p> <p>This dataset also contains the simulated 3-D polarimetric, directional, complex-valued (real, imaginary) realised-gain patterns, seperately for 28 GHz and for 60 GHz, in 1-degree resolution in both phi and theta directions. The patterns are seperately given for the vertical ("VPolPatch")and the horizontal feed port ("HPolPatch"). The 2-D pattern cuts presented in the above publication (in Figs. 5-11) are subsets of the 3-D patterns in this dataset.</p> <p>The format of the eight ascii files {xxGHzStandingyyHandzzPolPatch.txt} is a follows:<br> 1st column: Theta angle in degrees<br> 2nd column: Phi angle in degrees<br> 3rd column: real part of Gain, theta component, in dBi<br> 4th column: imaginary part of Gain, theta component, in dBi<br> 5th column: real part of Gain, phi component, in dBi<br> 6th column: imaginary part of Gain, phi component, in dBi<br> where xx is "28" or "60" (GHz), yy is "One" or "Two" (-hand grip), and zz is "H" or "V" (-pol. antenna port), as described above.</p> <p>The spherical coordinate system is used in accordance to the IEEE-standard spherical coordinate system. The underlying Cartesian coordinate system is shown in the two attached preview (PNG) image files for both human body models, where the z-axis (theta=0 degrees) points to the directions of the head of the human, the x-axis (phi=0 degrees) towards the left side of the human, and the y-axis toward the back of the human.<br> </p>
REFERENCES DATASET: A SYSTEMATIC REVIEW OF THE EDUCATIONAL USE OF MOBILE PHONES IN TIMES OF COVID-19
<p>The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.</p> <p>This data set is composed of the table with the references used for the review.</p> <p> </p>
Mobile phone created model.
This memorial is within the burial ground at Bridgefoot and was captured using an LG G3 mobile phone then processed with Autodesk Memento Beta. I did this to show that a fairly decent 3D model can be produced at the moment using low cost equipment. Source: Objaverse 1.0 / Sketchfab
Mobile phone data
<p><strong>Phone communication data</strong></p> <p>Phone communication data from multiple phone extractions. The file NodeList.csv contains the nodes of the multivariate graph and contains the following columns: </p> <p>_nodeID : unique identifier for each entity of the graph<br> _nodeType : specification of node type (phone, person, contact or identifier)<br> _viewIcon: tulip-specific attribute which defines the icon used for each node<br> _viewShape: tulip-specific attribute which defines the shape for each node</p> <p>The file EdgeList.csv contains all edges between the above specified nodes and contains the following columns:<br> _sourceID : unique identifier of source node<br> _targetID : unique identifier of target node<br> _relType : specification of the type of relation (communication, contact association, user account association)<br> _nbCommunications : number of communications exchanged between two given nodes. Attribute present only for edges of type ‘communication’.</p> <p>Nodes and relations can be imported into Tulip using the ‘import csv’ option. In order to create metanodes, only edges describing associations (contacts, user accounts) are used in a first step in order to use the algorithm ‘connected componants’. After importing the communication edges, metanodes can then be created based on the metric value calculated beforehand.</p> <p> </p> <p> </p> <p> </p>
Mobile phone video of individual ancient oak trees stems from the SCATTER project
<p>This repository contains video data recorded of tree stems as part of the <a href="https://doi.org/10.5281/zenodo.11658042">SCATTER project</a>.</p> <p>See <a href="https://zenodo.org/records/11658042">https://zenodo.org/records/11658042</a> for a list of trees and other available datasets. </p> <p>Individual video files are in .mp4 format and were recorded with a Google Pixel 7 phone. </p> <h2>Funding, licence and usage</h2> <p>This research was funded by the Woodland Trust Conservation Research Programme. By accessing or using this dataset, you agree to the terms of the relevant licence agreement(s). You will ensure that this dataset is cited in any publication that describes research in which the data have been used.</p>
A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland
<p><strong>Related article:</strong> Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. <a href="https://www.nature.com/articles/s41597-021-01113-4"><em>Scientific Data</em> 9, 39</a>.<br> </p> <p><strong>In this dataset:</strong></p> <p>We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning. </p> <p><strong>Please cite this dataset as:</strong><br> <br> Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4<br> </p> <p><strong>Organization of data</strong></p> <p>The dataset is packaged into a single Zipfile <em>Helsinki_dynpop_matrix.zip</em> which contains following files:</p> <ol> <li> <em>HMA_Dynamic_population_24H_workdays.csv</em> represents the dynamic population for average workday in the study area.</li> <li> <em>HMA_Dynamic_population_24H_sat.csv</em> represents the dynamic population for average saturday in the study area.</li> <li> <em>HMA_Dynamic_population_24H_sun.csv</em> represents the dynamic population for average sunday in the study area.</li> <li><em>target_zones_grid250m_EPSG3067.geojson</em> represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.</li> </ol> <p><strong>Column names</strong></p> <ol> <li><em>YKR_ID </em>: a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.</li> <li><em>H0, H1 ... H23 </em>: Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. <br> The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)</li> </ol> <p>In order to visualize the data on a map, the result tables can be joined with the <em>target_zones_grid250m_EPSG3067.geojson</em> data. The data can be joined by using the field <em>YKR_ID</em> as a common key between the datasets.</p> <p><strong>License</strong><br> Creative Commons Attribution 4.0 International.</p> <p><strong>Related datasets</strong></p> <ul> <li>Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612</li> <li>Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564</li> </ul> <p><br> </p>
Data from: Robust single-image tree diameter estimation with mobile phones
<p>Ground-based forest inventories are a key element of forest carbon monitoring, reporting, and verification schemes and a cornerstone of forest ecology research. Recent work using LiDAR-equipped mobile phones to automate parts of the forest inventory process assumes that tree trunks are well-spaced and visually unoccluded, or else requires manual intervention or offline processing to identify and measure tree trunks.</p> <p>In this paper, we design an algorithm that exploits a low-cost smartphone LiDAR sensor to estimate trunk diameter automatically from a single image in complex and realistic field conditions. We implement our design and build it into an app on a Huawei P30 Pro smartphone, demonstrating that the algorithm has low enough computational cost to run on this commodity platform in near real-time.</p> <p>We evaluate our app in three different forests across three seasons and find that in a corpus of 97 sample tree images, our app estimates trunk diameter with RMSE of 3.7 cm (R<sup>2</sup> = .97; 8.0% mean error) compared to manual DBH measurement. It achieves a 100% tree detection rate while reducing surveyor time by up to a factor of 4.6.</p> <p>Our work contributes to the search for a low-cost, low-expertise alternative to Terrestrial Laser Scanning that is nonetheless robust and efficient enough to compete with manual methods. We highlight the challenges that low-end mobile depth scanners face in occluded conditions and offer a lightweight, fully automatic approach for segmenting depth images and estimating trunk diameter despite these challenges. Our approach lowers the barriers to in situ forest measurement outside of an urban or plantation context, maintaining a tree detection and accuracy rate comparable to previous mobile phone methods even in complex forest conditions.</p>
First responders' mobile phone traces during a search-and-rescue exercise scenario
<p>This dataset is collected during a search-and-rescue exercise scenario in the framework of the ARTION project. </p> <p>The operation took place on the 22nd of May 2022 in Paphos district (near the beach at Mandria village). The exercise was organized and conducted by the Cyprus Civil Defence and data collection was performed by the KIOS Research and Innovation Center of Excellence of the University of Cyprus. </p> <p>The data is saved in an .xlsx file. It consists of 9 first responders' traces captured during a search-and-rescue operation. The responders were moving on foot holding their mobile phones, which were used for capturing their traces. By means of the ARTION mobile app, the locations of the mobile phones were captured by the build-in GPS receiver of the phone approximately every 5 seconds. </p> <p> </p> <p> </p>
supplementary data Chemical and Microbial Leaching of Valuable Metals from PCBs and Tantalum Capacitors of Spent Mobile Phones
<p>Table S1: Chemical composition of waste PCBs and tantalum capacitors (VICs)without HF precious metals; Table S2: Leaching with organic acids; Table S3: Leaching with inorganic acids; Table S4: Bacterial leaching of PCBS varying pulp density; Table S5: Bacterial leaching of PCBS varying ferrous iron concentration; Table S6: Bacterial leaching of PCBS and tantalum capacitor scrap varying particle size; Table S7: Fungal leaching of metals by <em>A</em>. <em>niger</em> spores and filtrate.</p>
Dataset of "Tracking Urban Human Activity from Mobile Phone Calling Patterns" PLOS Computational Biology paper
<p>This are the dataset file for "Tracking Urban Human Activity from Mobile Phone Calling Patterns", to be published in PLOS Computational Biology.</p> <p>The files contain probability distributions of finding a first, last, or any call as a function of time, derived from anonymized call detail records for a 12 months period in the year 2007 from a mobile phone service provider in a European country. The first data file contains the data obtained fom 30 different cities. the second for the six most populated cities, splitting the data into different age and gender groups.</p> <p>Details in README files.</p>
Intervention fidelity and factors affecting the process of a mobile phone text messaging intervention among adolescents living with HIV: A convergent mixed methods study in southern Ethiopia
<p><em>Objective:</em> To assess the intervention fidelity and explore contextual factors affecting the process of a mobile phone text messaging intervention in improving adherence to and retention in care among adolescents living with HIV, their families, and their healthcare providers in southern Ethiopia.</p> <p><em>Design:</em> A convergent mixed-methods design guided by the process evaluation theoretical framework and the RE-AIM framework was used alongside a randomised controlled trial to examine the fidelity and explore the experiences of participants in the intervention.</p> <p><em>Setting:</em> Six hospitals and five health centres providing HIV treatment and care to adolescents in five zones in southern Ethiopia. Participants: adolescents (aged 10–19), their families and their healthcare providers.</p> <p><em>Intervention:</em> Mobile phone text messages daily for 6 months or standard care (control). Results: 306 participants were enrolled in the process evaluation. Among the intervention participants (N =153), 171 (55.9%) of whom were men, most resided in an urban area 225 (73.5%), and participants had a mean age of 15 (2.62). The overall experiences of implementing the text messages reminder intervention were described as helpful in terms of treatment support for adherence but had room for improvement. During the study, 30,700 text messages were sent, and fidelity was high, with 99.4% successfully receiving text messages during the intervention. Barriers such as failed text messages delivery, limitations in phone ownership, and technical limitations affected fidelity. Technical challenges can hinder maintenance, but a belief in the future of digital communication permeates the experiences of the text messages reminders.</p> <p><em>Conclusions: </em>Overall fidelity was high, and participants' overall experiences of mobile phone text messages were expressed as helpful. Contextual factors, such as local telecommunications networks and local electric power, as well as technical and individual factors must be considered when planning future interventions.</p>
The Concurrent Validity of the Internet Addiction Test (IAT) and the Mobile Phone Dependence Questionnaire (MPDQ)
<p>A full raw dataset for the following research study</p> <p> </p> <p>PONE-D-17-34552R1 <br> The Concurrent Validity of the Internet Addiction Test (IAT) and the Mobile Phone Dependence Questionnaire (MPDQ)</p>
A Mobile Phone Game to Prevent HIV Among Young Africans
ClinicalTrials.gov study NCT03054051. IPD Sharing: NO. Countries: 1. Publications: 3.
An Avatar-based Mobile Phone Intervention to Promote Health in African American MSM
ClinicalTrials.gov study NCT04217174. IPD Sharing: YES. Countries: 1. Publications: 3.
A Mobile Phone-based Intervention on Dementia Patients' Caregivers in Vietnam
ClinicalTrials.gov study NCT04958707. IPD Sharing: NO. Countries: 1. Publications: 4.
Mobile Phone Support for Adults and Support Persons to Live Well With Diabetes
ClinicalTrials.gov study NCT04347291. IPD Sharing: YES. Countries: 1. Publications: 3.
Study of Mobile Phone Delivered Intervention to Reduce Alcohol Consumption
ClinicalTrials.gov study NCT02158949. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Mobile Phone Text Messaging Plus Motivational Interviewing: Effects on Breastfeeding, Child Health Outcomes
ClinicalTrials.gov study NCT05063240. IPD Sharing: YES. Countries: 1. Publications: 2.
Clinical Trial of Smoking Cessation Mobile Phone Program
ClinicalTrials.gov study NCT02656745. IPD Sharing: NO. Countries: 1. Publications: 1.
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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.