Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
59
datasets available to search
ShareScore release 0.9.0
Dataset results
59 results for “mobile network”
Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network
<p><strong>Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong></p> <p>This dataset contains over 2 and a half years (04/2012-12/2014, >36 Mio samples) worth of ultra-fine particle (UFP) concentration measurements collected by a mobile senor network. The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.</p> <p><strong>Hardware:</strong></p> <ul> <li><strong>Ultrafine particle sensor</strong>: MiniDiSC (see also: Martin Fierz et al. Design, Calibration, and Field Performance of a Miniature Diffusion Size Classifier. Aerosol Science and Technology, Volume 45, 2011.)</li> <li><strong>GPS receiver</strong>: u-blox EVK-6p<br> </li> </ul> <p><strong>Sensor Data<br> ------------------</strong><br> <strong>ufp_data</strong><strong>*.csv column format:</strong></p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Number of particles [#/ccm]</li> <li>Average particle diameter [nm]</li> <li>LDSA: lung deposited surface area [um2 /cm3]</li> </ol> <p><strong>Data quality:</strong><br> The data has been post-processed by performing a periodic null-offset calibration and filtering samples during malfunction.</p> <p><strong>High-Resolution Maps<br> --------------------------------</strong></p> <p>The data has been used to create high-resolution ultrafine particle concentration maps. Four maps, which show the seasonal average particle concentration over seasonal periods, can be found in ufp_seasonal_maps_201204_201304.csv.</p> <p><strong>ufp_map*.csv column format:</strong></p> <ol> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>Estimated number of particles [#/ccm]</li> </ol> <p><strong>Map quality</strong></p> <p>Please have a look at the papers in References 1. and 2. (Hasenfratz et al. 2014 and 2015) for a detailed evaluation of the maps.</p> <p><strong>References<br> ----------------</strong><br> The dataset has been used and is described in more detail in the following publications:</p> <ol> <li>David Hasenfratz et al.<em> Pushing the Spatio-Temporal Resolution Limit of Urban Air Pollution Maps.</em> IEEE International Conference on Pervasive Computing and Communications (PerCom). Budapest, Hungary, March 2014. Best Paper Award. </li> <li>David Hasenfratz et al. <em>Deriving High-Resolution Urban Air Pollution Maps Using Mobile Sensor Nodes. </em>Pervasive and Mobile Computing. Elsevier, 2015. </li> <li>David Hasenfratz et al. <em>Demo Abstract: Health-Optimal Routing in Urban Areas.</em> ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). Seattle, USA, April 2015.</li> <li>Michael Müller et al. <em>Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network. </em>Atmospheric Environment. Elsevier, 2016.</li> </ol> <p>For further information, visit: <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a></p>
Replication materials for Schmutte (2014) "Free to Move? A Network Approach to the Analysis of Job Mobility"
<p>This folder contains datasets and coded needed to replicate results published in Schmutte, Ian M. (2014) "Free to Move? A Network Approach to the Analysis of Job Mobility, " Labour Economics vol.29 pp.49--61</p>
Healthy Immigrant Community: Mobilizing the Power of Social Networks
ClinicalTrials.gov study NCT05136339. IPD Sharing: Not stated. Countries: 1. Publications: 5.
TeamUp5G: A Multidisciplinary Approach to Training and Research on New RAN Techniques for 5G Ultra-Dense Mobile Networks
<p>This video presents a summary of the main research directions being followed in TeamUp5G European Training Network. This project is teaming up a new generation of researchers and entrepreneurs ready to address complex engineering problems and innovation to work both at university and industry in the 5G field. Research is focused on the radio access network (RAN) techniques for 5G, considering ultra-dense mobile networks as a key ingredient of the mobile networks and their evolution. It covers a wide spread of topics from the physical layer and medium access control to applications, looking at spectrum sharing and energy efficiency as important features.</p>
Data from: Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes
We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized.
ATELIER. QoE and Net.KPIs for On-Demand video transmission in Mobile Networks (emu.) part 1
<p>This dataset contains all the raw (and processed) information for thousands of experiments that transfer a video on-demand from a core network to a UE emulated through SRSRan inside docker containers. The dataset contains both the network statistics but also the evaluation of the QoE through VMAF for each experiment. In total, 3 different videos has been used with different characteristics and different network impairments has been applied.</p> <p>First part. Please download both parts of raw logs before unzipping.</p> <p>The logs contain network KPI information collected during the transfer of a video-stream using FFMpeg from the core net to a UE using SRS-RAN.<br>Each experiment contains the full logs with the full network traces, for a total of more than 4000 experiments.<br>The transferred and received videos are excluded due to the total weight.</p> <p>For a more detailed explanation on how the dataset has been generated, please refer to the associated article and GitHub repository.<br>Title of the article: “ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning”.</p>
ATELIER. QoE and Net.KPIs for On-Demand video transmission in Mobile Networks (emu.) part 2
<p>This dataset contains all the raw (and processed) information for thousands of experiments that transfer a video on-demand from a core network to a UE emulated through SRSRan inside docker containers. The dataset contains both the network statistics but also the evaluation of the QoE through VMAF for each experiment. In total, 3 different videos has been used with different characteristics and different network impairments has been applied.</p> <p>Second part. Please download both parts of raw logs before unzipping.</p> <p>The logs contain network KPI information collected during the transfer of a video-stream using FFMpeg from the core net to a UE using SRS-RAN.<br>Each experiment contains the full logs with the full network traces, for a total of more than 4000 experiments.<br>The transferred and received videos are excluded due to the total weight.</p> <p>For a more detailed explanation on how the dataset has been generated, please refer to the associated article and GitHub repository.<br>Title of the article: “ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning”.</p>
The appendix for dynamic model of respiratory infectious disease transmission by population mobility based on city network
<p>First, a scale-free city network was established, and the shortest path between any two nodes was determined. Second, the movement path of tourists was designed based on the shortest path. Subsequently, every infected person's information, such as the city, infection time, onset, and hospitalisation, was confirmed based on their movement path. Third, the features of the transmission path and time distribution of the epidemic were characterised after summarising the information. Finally, the reliability of the model was verified.</p>
CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks - Dataset
<p>This contains the raw and processed data for the paper "CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks" by H. Wolf, M. Szell and A. R. Vierø.</p>
Social Networking on Mobile Phone to Improve Maternal and Neonatal Outcomes
ClinicalTrials.gov study NCT02371213. IPD Sharing: NO. Countries: 1. Publications: 7.
Screening and Follow-up Study of Neonatal Jaundice Based on Mobile Network
ClinicalTrials.gov study NCT04251286. IPD Sharing: YES. Countries: 1. Publications: 1.
Proof-of-Concept Study of an Integrated Mobile and Social Network Weight Loss Intervention
ClinicalTrials.gov study NCT05295849. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes
Open the record for dataset details and reuse information.
Mobile-based Online Social Network Intervention to Increase Physical Activity
ClinicalTrials.gov study NCT02736903. IPD Sharing: NO. Countries: 0. Publications: 3.
Broadcasting with mobile agents in dynamic networks (video)
Full video presentation of the paper: Broadcasting with mobile agents in dynamic networks.<br><br>Appears in Session 1 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Mobile and Digital Application in Heart Failure Networks Berlin/Brandenburg
ClinicalTrials.gov study NCT05422859. IPD Sharing: NO. Countries: 1. Publications: 0.
Mobilizing Community Networks to Optimize Child Well-being
ClinicalTrials.gov study NCT02622399. IPD Sharing: NO. Countries: 1. Publications: 0.
Mobilizing Social Network Resources for HIV Care Support
ClinicalTrials.gov study NCT05123274. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Brain Networks and Mobility Function: B-NET
ClinicalTrials.gov study NCT03430427. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.