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286 results for “data mobilization.”
Measurement Data: Latencies and Traffic Traces in Global Mobile Roaming with Regional Breakouts
<h1>A Shortcut through the IPX: Measuring Latencies in Global Mobile Roaming with Regional Breakouts</h1> <p>This repository contains a description and sample data for the Paper<em> A Shortcut through the IPX: Measuring Latencies in Global Mobile Roaming with Regional Breakouts</em> published at the Network Traffic Measurement and Analysis (TMA) Conference 2024.<br>In the provided README.md file, we present example snippets of the datasets, including an explanation of all contained fields.</p> <p>We cover the three main datasets covered in the related paper:<br>- DT1: User plane traces captured at multiple GGSN/PGW instances of a globaly operating MVNO<br>- DT2: GTP echo round trip times between visited network SGSN/SGWs and home network GGSN/PGWs<br>- DT3: IPX routing information, as extracted from BGP routing tables</p> <p>For legal reasons, we are not able to publish the secondary datasets (DT4, DT5) covered in the manuscript.</p> <p>Finally, for privacy, security, and political reasons, certain fields in each of the datasets have been anonymized. These are indicated by the `_anonymized` prefix.<br>In case of IP addresses, the anonymization ist consistent across datasets, meaning that similar IPs have been anonymized such that their values are still identical after anonymization.</p> <h3>Contact</h3> <p>For questions regarding the dataset, contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de)</p> <p> </p>
Supplementary data for "Accelerated river mobility linked to water discharge variability"
<p>Supplementary data for the paper "Accelerated river mobility linked to water discharge variability", by Leenman et al.</p> <p>This repository contains 3 datasets underlying the figures in the manuscript and supporting material.</p> <p><strong>ds01_flow_gauge_metadata: </strong>Details and locations of flow records used in our analysis.</p> <p><strong>ds02_Qvar_and_TR: </strong>Floodplain reworking timescales, channel characteristics, sediment concentrations, and discharge variability metrics (i.e. all metrics used in our figures and modeling). Floodplain reworking timescale data are from <a href="https://doi.org/10.1029/2024GL108537">Greenberg et al., 2024</a>. Channel slopes, widths, planforms and catchment areas are from <a href="https://doi.org/10.1130/G49121.1">Galeazzi et al., 2021.</a> Bed-material sediment concentration estimates are generated from WBMsed (<a href="https://doi.org/10.1029/2021WR031583">Cohen et al., 2022)</a>. </p> <p><strong>ds03_channel_threads_GRWL: </strong>Data used to make one of our supplemental figures. Frequency count of the number of reaches that have a given number of channel threads in the Global River Widths from Landsat (GRWL) database (<a href="https://doi.org/10.1126/science.aat0636">Allen and Pavelsky, 2018</a>). Data downloaded via Google Earth Engine. Only reaches wider than 1000 m are included.</p> <p><strong>Code</strong> using these data to generate the plots in the paper / SM using these data can be found at <a href="https://github.com/a-leenman/Discharge-variability-river-mobility">https://github.com/a-leenman/Discharge-variability-river-mobility</a>. This is the 'live' code; the version at the time of paper submission is archived here: <a href="https://doi.org/10.5281/zenodo.12555069">https://doi.org/10.5281/zenodo.12555069</a>.</p>
Mobile comparison data and analysis between a new low-cost meteorological device and the Technical University of Dresden Chair of Meteorology's backpack meteorological device
<p>This dataset provides mobile comparison data from Tharandt and Dresden, Germany, which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to an established high-end backpack-mounted device from the Dresden University of Technology (TUD) Chair of Meteorology, Germany. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost WXT520 for air temperature and relative humdity and the low-cost SR2AD pyranometer vs. the high-cost SKS 1110 pyranometer. The ability of each device to predict the Universal Thermal Climate Index (UTCI), calculated using the software RayMan Pro, was also compared.</p>
Mobile Meteorological Data from Dresden city center (June-August, 2022)
<p>This file contains meteorological data collected with built-for-purpose low-cost meteorological device for mobile thermal comfort mapping. Data was collected in the city center of Dresden, Germany, around a 3.1km route for the 19th of June, 23rd of June, 19th of July, 25th of July, 16th of August, and 17th of August, 2022 in the morning (06:00), midday (12:00), afternoon (15:30), and evening (20:00) of each of the 6 monitoring days. Each data file also contains thermal indices calculated using the software RayMan Pro.</p> <p>Folders are labeled by date e.g. 19th of June is written as 1906. Subfolders are labeled to correspond with the starting time of each measurement where A corresponds to 06:00, B is 12:00, C is 15:30, and D is 20:00. For each of these time periods (A, B, C, D) 2 laps of the 3.1km route were made. </p>
5G-IANA: UC4 Dataset Mobile users data rate participating in the use case
<p>Location of the mobile user and its nearby place data including their type such as restaurant, café, market, banks and gas station</p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 4. Mobile Application to collect Sketch data
<p>The drawing of a tree is shown on the drawing canvas in Figure 4. It is clear from the figure that there is some empty area on top, right, left and bottom of the drawing sketch, which can cause problems while using this image for training the neural network.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 2. Data collection methods of the studies (Note: A study may involve more than one method.)
<p>Figure 2 shows the data collection methods applied in the studies. Surveys were used in most of the selected cases (94%), followed by interviews (38%) and experiments (34%). Apart from these approaches, observation (12%), field materials (8%), video recordings (6%) and discussion threads (2%) were also used in some of the studies. Thirty of the cases involved the use of more than one method, mostly combining a survey and another one or more method. These suggest that most of the studies involved quantitative data at least in part.</p>
Linked collectors and determiners for: Data mobilized by the National Herbarium of Benin in the framework of the JRS Biodiversity project of Benin.
Natural history specimen data linked to collectors and determiners held within, "Data mobilized by the National Herbarium of Benin in the framework of the JRS Biodiversity project of Benin". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9d850f67-e358-4dfb-8748-57cd668031e8">https://bionomia.net/dataset/9d850f67-e358-4dfb-8748-57cd668031e8</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9d850f67-e358-4dfb-8748-57cd668031e8">https://gbif.org/dataset/9d850f67-e358-4dfb-8748-57cd668031e8</a>. Formatted as a Frictionless Data package.
Mobile co-manipulation data
<p>Data acquired during large part co-manipulation processes. Specifically, trajectory percentage and trajectory deviation.</p> <p>Notation of files (i.e. "u2_AB_500.csv"):</p> <ul> <li><strong>User:</strong> u2 would be the second subject of the experiment.</li> <li><strong>Path:</strong> Two options, AB (station A to station B) and BA (station B to A).</li> <li><strong>Maximum allowed distance:</strong> Value which defined the width of the lane.</li> </ul>
TRA-DE 2022 German OAP survey data on mobility patterns, car ownership and use
<p>Online Access Panel-based survey data collected Jan-May 2022 in Germany within the research project <a href="https://energysufficiency.de/">Energy Sufficiency</a>. N=3422 (+N=510 for city of Flensburg). Data available as coded and labelled version, incl. codebook and method report from data collection contractor (all in German). Supplementary files: questionnaire (for survey implemented as online questionnaire), codebook and method report (German).</p>
Raw data for evaluation of Floral Volatile-Patterns in the Genus Narcissus using Gas Chromatography coupled Ion Mobility Spectrometry
<p>We used a commercial gaschromatography coupled ion mobility spectrometer, equipped with an integrated in-line enrichment system for fast, sensitive and automated analysis of floral volatile patterns in the genus <em>Narcissus</em>. The raw data of individual measurements are stored together with corresponding telemetry data as data matrices. The determined retention times and ion mobilities (series and columns) can be used for the identification of substances. The measured values (intensities) are used for a (semi)-quantoitative determination of individual substances.Based on these raw data, heatmaps can be generated in this way, which allow a comparison and potentially identification of floral volatiles. </p> <p>This data set is part of a proof of concept study for the use of GC-IMS in the investigation of flower volatiles.</p>
Data set for paper "Spatial Mobility Capital: A Valuable Resource for the Social Mobility of Border-Crossing Migrant Entrepreneurs?"
<p>Data set for paper "Spatial Mobility Capital: A Valuable Resource for the Social Mobility of Border-Crossing Migrant Entrepreneurs?"</p>
DynamicRead: Eye Movement Data of Reading on Handheld Mobile Devices under Dynamic Conditions
<p>The DynamicRead dataset contains eye movement data from 20 participants engaged in reading tasks on a handheld mobile device, while sitting and walking. Participants read 10 texts using four gaze interaction methods based on scrolling techniques, as well as one touch-based interaction method. The dataset captures the impact of motion on eye movement and features an unstable eye-movement sampling frequency ranging from 8-12fps/hz, which poses a challenge for traditional eye-movement toolkits. This dataset can be valuable for academic research on the impacts of motion on eye movement and the development of robust gaze interaction methods for handheld mobile devices under dynamic conditions.<br> <br> ###<br> Folder Structure:<br> -'eye_data' -- contains eye movement of reading data<br> -'text_screenshot' -- contains different reading material screenshot<br> -'heatmap_img' -- contains heat map of each reading page <br> -'scanpath_img' -- contains scan path of each reading page<br> -'text' -- contains reading material<br> -'code' -- contains R code for reading eye movement visualisation</p> <p><br> Name Method<br> - GAP1_GazeA_log.txt -- Group A, Participant No1, Scrolling technique: GazeA<br> - Gaze A: Auto-scrolling<br> - Gaze B: Heatbox<br> - Gaze C: Eye-Swipe<br> - Gaze D: Moving-bar<br> </p>
Data for: Mobility of the human foot's medial arch helps enables upright bipedal locomotion
<p class="MsoNormal"><span>Developing the ability to habitually walk and run upright on two feet is one of the most significant transformations to have occurred in human evolution. Many musculoskeletal adaptations enabled bipedal locomotion, including dramatic structural changes to the foot and, in particular, the evolution of an elevated medial arch. The foot's arched structure has previously been assumed to play a central role in directly propelling the center of mass forward and upward through leverage about the toes and a spring-like energy recoil. However, it is unclear whether or how the plantarflexion mobility and height of the medial arch support its propulsive lever function. Here we show, using high-speed biplanar x-ray, that regardless of intraspecific differences in medial arch height, arch recoil enables a longer contact time and favorable propulsive conditions at the ankle for walking upright on an extended leg. This mechanism may have helped drive the evolution of the longitudinal arch after our last common ancestor with chimpanzees, who lack this plantarflexion mobility during push-off. We discovered that the generally overlooked navicular-medial cuneiform joint is primarily responsible for arch recoil in human arches, suggesting that future morphological investigations of this joint will provide new interpretations of the fossil record. Our work further suggests that enabling longitudinal arch recoil in footwear and surgical interventions may be critical for maintaining the ankle's natural propulsive ability.</span></p>
Hyperlocal Environmental Data with a Mobile Platform
<p>This document provides expanded definitions, units, and more information on the data provided in the .CSV files collected using the City Scanner platform, <a href="http://senseable.mit.edu">MIT Senseable City Lab </a>over the years 2019-2022</p> <p><strong>Data Fields</strong></p> <p><em>time</em>: The time field is in Unix time and can be converted to local time with a simple script or using online tools.</p> <p><em>latitude, longitude</em>: The latitude and longitude readings from the devices are determined via GPS and given in degrees for projected corrdinates.</p> <p><em>pm1, pm25, pm10</em>: The particulate matter (pm) readings from the optical particle counter after calibration. The units for each are instantaneous mass concentrations in μg/m3.</p> <p><em>no2</em>: The nitrogen dioxide (no2) readings from the gas sensors after conversion and calibration. The unit is instantaneous concentrations in parts per billion (ppb).</p> <p><em>bin0-bin23</em>: Twenty-four bins from the OPC containing particle number counts for different sizes, ranging from 0.35 to 40 μm. The bin boundary sizes are as follows from bin0 to bin23: 0.35, 0.46, 0.66, 1.0, 1.3, 1.7, 2.3, 3.0, 4.0, 5.2, 6.5, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0 , 20.0, 22.0, 25.0, 28.0, 31.0, 34.0, 37.0, 40.0</p> <p>temperature: Ambient temperature reading in degrees Celsius.</p> <p>humidity: Ambient relative humidity reading in percentage</p> <p><strong>Overview of Data Collection Studies</strong></p> <table> <caption>Datasets</caption> <thead> <tr> <th scope="col">City</th> <th scope="col">Sensing target pollutants</th> <th scope="col">Sensing fleet</th> <th scope="col">Duration</th> <th scope="col">Files</th> </tr> </thead> <tbody> <tr> <td>New York City (USA)</td> <td>Particulate matter, particle size distribution, NO2</td> <td>Five municipal vehicles</td> <td> <p>Oct. 2020 - Feb. 2021</p> <p>Sep. 2021 - Dec. 2021</p> </td> <td> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot1_PM.csv?download=1">NYC_Pilot1_PM.csv</a> </p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_NO2_Part1.csv?download=1">NYC_Pilot2_NO2_Part1.csv</a></p> <p> </p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_NO2_Part2.csv?download=1">NYC_Pilot2_NO2_Part2.csv</a></p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_PM_Part1.csv?download=1">NYC_Pilot2_PM_Part1.csv</a></p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_PM_Part2.csv?download=1">NYC_Pilot2_PM_Part2.csv</a></p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_PM_Part3.csv?download=1">NYC_Pilot2_PM_Part3.csv</a></p> </td> </tr> <tr> <td>Boston (USA)</td> <td>Particulate matter, particle size distribution, NO2</td> <td>One mobile air laboratory</td> <td>Feb. 2022 - Apr. 2022</td> <td><a href="https://zenodo.org/record/7762000/files/Boston_Pilot_PM_NO2.csv?download=1">Boston_Pilot_PM_NO2.csv</a></td> </tr> <tr> <td>Beirut (Lebanon)</td> <td>Particulate matter, particle size distribution</td> <td>Two Taxies </td> <td>Feb. 2022 - Jun. 2022</td> <td><a href="https://zenodo.org/record/7762000/files/Beirut_Pilot_PM.csv?download=1">Beirut_Pilot_PM.csv</a></td> </tr> </tbody> </table> <p> </p>
Data set for manuscript 'Quantifying geomorphically effective floods using satellite observations of river mobility'
<p>Data underlying the plots / used in the modelling work for the paper 'Quantifying geomorphically effective floods using satellite observations of river mobility', submitted to <em>Geophysical Review Letters.</em></p>
Data for paper "The Two Faces of AI in Green Mobile Computing: A Literature Review"
<p>This is the data associated with the literature review presented in the paper “The Two Faces of AI in Green Mobile Computing:<br> A Literature Review” accepted at SEAA 2023.</p>
Experimental data of a channel bifurcation with mobile-bed without and with vane-fields
<p>Experimental data of laboratory experiments of a channel bifurcation: a main and a lateral diversion channel, both with mobile bed, rectangular cross-section, performing a 90º angle between them.<br> Experiments run until the equilibrium bed was reached.</p> <p>4 experiments:<br> (i) no vanes - NV;<br> (ii) with a vane-field - VF;<br> (iii) with a second vane-field configuration with the alignment of the vanes (angle beta) = 30º - VF30;<br> (iv) with the same vane-field configuration as (iii) but with angle beta = 10º - VF10.</p> <p>Water depth at the downstream end of the main channel is equal to 0.10 m.<br> Width of the main channel = 0.68 m in (i)-(ii), and 0.67 in (iii)-(iv).<br> Width of the diversion channel = 0.26 m in (i)-(ii), and 0.25 in (iii)-(iv).<br> Discharge at the entrance of the main channel = 29 l/s.<br> Discharge ratio per unit width of the channels: 0.5 in (i)-(ii), and 0.2 in (iii)-(iv).<br> Height of the vanes = 0.03 m above the average bed level of the approach flow.</p> <p>Data measured provided for each experiment: <br> (i) profiles along direction x of the water surface levels and the bed topography;<br> (ii) average values and fluctuations of the 3 components of velocities at a dense grid of points (see figures with the plan views of the measured grid). In the vertical direction z, the points are spaced in 0.5 cm or 1 cm.<br> All the data was measured for the equilibrium bed.</p> <p>The velocities were measured with a side-looking Vectrino. Data was despiked (see Goring and Nikora, 2002 DOI 10.1061/(ASCE)0733-9429(2002)128:1(117) ), and points with correlation < 70% or SNR < 15 db were discarded.</p> <p>For further information see:</p> <p>Baltazar, J.; Alves, E.; Bombar, G.; Cardoso, A.H. Effect of a Submerged Vane-Field on the Flow Pattern of a Movable Bed Channel with a 90º Lateral Diversion. Water 2021, 13, 828. https://doi.org/10.3390/w13060828</p> <p>PhD thesis "Sediment control at lateral water intakes through submerged vane-fields", by Joana Baltazar (Instituto Superior Técnico, Lisbon, Portugal)<br> </p>
Higher-order Mobility Flow Data
<p>This dataset is a collection of higher-order mobility datasets, primarily aimed at trajectory data mining applications. These datasets have been created using the Point2Hex tool, allowing us to transform traditional GPS-based geolocations and check-in data into sequences of higher-order geometric elements, particularly hexagons. This transformation has various advantages, including reduced sparsity, analysis at different levels of granularity, improved compatibility with common machine learning architectures, enhanced generalization and overfitting reduction, and efficient visualization.</p> <p>Seven popular mobility datasets, typically utilized in various trajectory-related tasks and technical problems, were subjected to this transformation process. These include applications like trajectory prediction, classification, clustering, imputation, and anomaly detection, among others.</p> <p>To foster the culture of reusability and reproducibility, we are providing not only the transformed higher-order mobility flow datasets but also the source code for the Point2Hex tool and comprehensive documentation. This offering aims to streamline the generation process, ensuring that users have clear guidance on how to reproduce curated or customized versions of these datasets. The material is stored in publicly accessible repositories, ensuring its widespread accessibility.</p>
Mobile Technology and Data Analytics to Identify Real-time Predictors of Caregiver Well-Being
ClinicalTrials.gov study NCT04556591. IPD Sharing: YES. Countries: 1. Publications: 4.
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.