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59 results for “mobile network”
Web Experience in Mobile Networks: Lessons from Two Million Page Visits
<p>Measuring and characterizing web page performance is a challenging task.</p> <p>When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult.</p> <p>Aiming at reproducibility, we present a large scale measurements study of web page performance collected in eleven commercial mobile networks spanning four countries.</p> <p>We build a dataset of nearly two million web browsing sessions to we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance.</p> <p>We find that the impact of mobile broadband access is sizeable.</p> <p>For example, the median page load time using mobile broadband increases by a third compared to wired access.</p> <p>Mobility clearly stresses the system, with handover causing the most evident performance penalties.</p> <p>Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact.</p> <p>Our work highlights the importance of large-scale measurements.</p> <p>Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle.</p> <p>For this, we are releasing the dataset as open data for validation and further research.</p> <p>We also release together with the datasets we collected the scripts we use to produce the analysis we present in the paper. Please use plot_all.sh script to generate the plots in the paper, using the separate scripts from the "scripts" archive. </p> <p>Should you use any of these resources, please also make an attribution using the following reference (provided here in bibtex format):</p> <pre>@inproceedings{rajiullah2019web, title={{Web Experience in Mobile Networks: Lessons from Two Million Page Visits}}, author={Rajiullah, Mohammad and Lutu, Andra and Khatouni, Ali Safari and Fida, Mah-Rukh and Mellia, Marco and Brunstrom, Anna and Alay, Ozgu and Alfredsson, Stefan and Mancuso, Vincenzo}, booktitle={The World Wide Web Conference}, pages={1532--1543}, year={2019}, organization={ACM}, address = {San Francisco, CA, USA}, keywords = {Web Experience, HTTP2, QUIC, TCP, Mobile Broadband, Measurements} }</pre>
Data from: The impact of human mobility networks on the global spread of COVID-19
<p>This is empirical dataset from the paper "The impact of human mobility networks on the global spread of COVID-19". Specifically, the dataset includes several files: (a) the COVID-19 network - an origin/destination matrix (i.e., "covid_network.csv"); (b) the common language network - edgelist format (i.e. "edge_list_comlang.csv"); (c) the same continent network - edgelist format (i.e., "edge_list_continent.csv"; (d) the contiguity network (i.e., "edge_list_contig.csv"); (e) the migration network - edgelist format (i.e., "edge_list_migration_in.csv"; (f) the tourism network - edgelist format (i.e., edge_list_tourism_in.csv"); (g) the list of nodes (countries) corresponding to files (b)-(e) (i.e., "nodes.csv"). Additionally, we uploaded the Rcode used in the paper (i.e. "code"), as a .pdf file format, the data source for the figures included in the paper (i.e., "covid_network_matrix.csv", "matrix_migration_out.csv", "matrix_tourism.csv" - Figure 1; "Fig_2_a_matrix_comlang.csv", Fig_2_b_matrix_contig.csv", "Fig_2_c_matrix_continent.csv" - Figure 2; "Fig_3.graphmlz - Figure 3; Fig_4.graphmlz - Figure 4) and the "global network of COVID-19 onset" (an individual-level data) (i.e., "global_covid_network.csv"). </p> <p>For details, please, see the Methods section of the paper: The impact of human mobility networks on the global spread of COVID-19 (Hancean, M.-G., Slavinec, M., Perc, M). </p> <p> </p> <p> </p> <p> </p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Clock Synchronization Accuracy over Mobile Networks
<pre><code><strong>Dataset: Clock Synchronization Accuracy over Mobile Networks</strong> This dataset evaluates time synchronization accuracy between two devices operating in different mobile network environments and setups. The synchronization is achieved using Precision Time Protocol (PTP) version 2 (PTPv2) over the User Plane within the mobile network. The dataset includes two types of clock offset data: <strong> Calculated Offset:</strong> This data is extracted from the PTPd software logs on the Slave device. Specifically, the "Offset From Master" section in the log files provides the value (in seconds) representing how much the Slave device's internal clock was adjusted to align with the Master clock. However, this value does not represent the exact offset from the Master clock. Instead, it is derived from the PTPv2 synchronization process and reflects the clock adjustment calculated during synchronization. <strong>Pulse-Per-Second (PPS) Offset:</strong> The PPS offset represents the most accurate measurement of clock offset between two devices. It is obtained using an Analog Discovery 2 oscilloscope, which directly compares the PPS signals generated by the Network Interface Card (NIC) timers of both devices. This method provides a high-precision measurement of the synchronization accuracy. <strong>Dataset Structure</strong> Each folder in the dataset contains: Clock offset data: Organized by type (Calculated or PPS). Setup schematics: Detailed diagrams illustrating the complete clock synchronization setup used for data collection. <strong> Additional Information</strong> The data in the folder titled "Clock offset (Pulse-per-second offset) over private 5G SA mobile network" is further described in the following scientific publication: <strong>"Clock Synchronization and Network Delay Evaluation over a Private 5G Standalone Network"</strong> Authors: Marcis Kalnins, Artis Rusins, Atis Elsts.</code></pre>
Dataset: Mobile-health Network Solutions (MNDR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 16. Social media postings
<p>After different stages of experimentation, we produced different statistical analysis of our results:</p> <p>a. concerning the most accessed social media. It indicated that Panoramio was the most visited, and Google+ had the majority of postings (Figure 16). </p> <p>The statistics indicate that the different social networks had different levels of impact on the children and that in the future developments the focus should be on those more frequently accessed. </p> <p>b. concerning the pedagogical content. A survey was done among teachers and children to evaluate the interest in our solutions and the quality of the educational content and therefore of the overall efficiency</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 12. Children applying glass-engraving techniques under the control of the staff from the NUA Bucharest
<p>The information from a) and b) has been used by other colleagues in the local school and in the future will be used by other schools in the country or abroad. This represented the second educational level of the project, i.e. the analysis of the collected data. Information from c) was further analyzed by university teachers, filtered and added as an enhancement to the content of the AR platform. An example of user-created content are the movies made with smartphone cameras, recording the children2 while performing traditional crafts (Figures 11, 12). </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 13. School girl presenting Vădastra School's Facebook page
<p>In order to achieve the third educational level, we experimented the social media tools, i.e. the project’s Google+ educational blog, by posting information complementary to that available on the Time Maps website, and also thematic questionnaires to foster a question-answer (Q&A) learning style. This allowed us to monitor and evaluate the information retention. In this stage we encountered the challenge to stimulate children and teachers to continue using our learning system, by posting new educational content, announcements and social messages. After a period of experimentation we proceeded to a statistical evaluation of children’s answers, which is presented in the next section.</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 7. The projects's Twitter page for educational and research micro-blogging
<p>The fourth stage was the use of social media to build a social learning network based on content and experience sharing and creation. The following web 2.0 services were used as a distributed platform able to support our experimental learning system: a) Panoramio (http://www.panoramio.com/user/7606828) (Figure 6) as a geo-referenced photo-sharing service over Google Maps and Google Earth for sharing project’s essential results; b) Twitter (https://twitter.com/maps_of_time) (Figure 7), as a social network and micro-blogging service for short announcements and comments; c) Google+ (https://plus.google.com/114705936110835992130?hl=en#114705936110835992130/posts?hl=en) (Figure 8), as a platform for sharing and tagging multiple content (photo, video), blogging and video chatting service with the recent Google Hangout, for sharing educational content; d) Google Drive (Levin, 2013) for cloud storage and collaborative document editing. We also created a YouTube channel for public distribution of video content (https://www.youtube.com/TimemapsNet), (Rusu et al., 2013) and a Facebook page of Vădastra School (https://www.facebook.com/scoalaVădastra). </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 6. Virtual Space on Panoramio+ with the experiments carried in Vădastra village
<p>The fourth stage was the use of social media to build a social learning network based on content and experience sharing and creation. The following web 2.0 services were used as a distributed platform able to support our experimental learning system: a) Panoramio (http://www.panoramio.com/user/7606828) (Figure 6) as a geo-referenced photo-sharing service over Google Maps and Google Earth for sharing project’s essential results; b) Twitter (https://twitter.com/maps_of_time) (Figure 7), as a social network and micro-blogging service for short announcements and comments; c) Google+ (https://plus.google.com/114705936110835992130?hl=en#114705936110835992130/posts?hl=en) (Figure 8), as a platform for sharing and tagging multiple content (photo, video), blogging and video chatting service with the recent Google Hangout, for sharing educational content; d) Google Drive (Levin, 2013) for cloud storage and collaborative document editing. We also created a YouTube channel for public distribution of video content (https://www.youtube.com/TimemapsNet), (Rusu et al., 2013) and a Facebook page of Vădastra School (https://www.facebook.com/scoalaVădastra)</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 1. An experiment of building a prehistoric house, carried out by Professor Dragoş Gheorghiu in Vădastra
<p>The learning experiment in Vădastra village began by geo-referencing some points of interest in the archaeological area (Gheorghiu & Stefan, 2013a; 2013b), which were identified from the archaeological published materials and from information provided by the villagers, as well as resulting from fieldwork conducted for over a decade (i.e. archaeological experimentation) in this village (Gheorghiu, 2001; 2008). The geographic data (POIs) was collected on the archaeological site by the K-12 children under the coordination of a project team member from the National University of Arts Bucharest (NUA). </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 2. An experiment with sunken up-draught kilns, carried out by Professor Dragoş Gheorghiu in Vădastra
<p>The second stage was represented by the physical reconstruction at real scale of a prehistoric house and of a corner of a Roman villa rustica, designed and coordinated by Professor Dragoş Gheorghiu. In these physical reconstructions video recordings and photo shoots were produced by specialist members of the project team, in order to be used later as augmentations in the AR application and for distribution by means of SNS. </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 4. Augmented Reality with video movie and social media (a vertical loom in front of two reconstructed kilns and a wall of a Roman villa rustica)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages. </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 9.The educational blog on Google+ Time Maps page–the weaving techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D reconstruction), slightly different from those available on the Time Maps web site, but containing the same information. The children had to make a little effort to relate this information with the one presented on the site, to make a connection between the questions, the fragments from videos at which the answers referred to and the information from the site. The set of questionnaires lead the school children through the majority of data offered by the web site regarding to the two historical periods (Figures 9, 10).</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 3. Augmented Reality with archaeological stratigraphy (a prehistoric house and a Roman villa reconstructed in 3D)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages. </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 15. Social media visits
<p>We also point out the following the advantages of Google+: - Google+ is a more user-friendly than other social environments and very suitable for use by school children than other blogging environments (e.g. Wordpress); thus it stimulated the play-like learning. - Google+ is more customizable than other social environments; - By allowing teachers to post questionnaires and to share images and videos or links to content on the Time Maps website, the Google+ page acted as an aggregator of information and a for scaffolding the learning process. After different stages of experimentation, we produced different statistical analysis of our results: concerning the most accessed social media. It indicated that Panoramio was the most visited (Figure 15)</p> <p>The statistics indicate that the different social networks had different levels of impact on the children and that in the future developments the focus should be on those more frequently accessed. </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 8. Virtual social space on Google+
<p>For micro-blogging we hashtagged the main topic as #maps_of_time and created keywords related to three ancient technologies specific for the studied contexts (textiles, glass, ceramics) to facilitate a categorization of the topics and their retrieval. To achieve a unified and coherent platform, the personal spaces of the social networks were customized with logos and landing pages, designed by Associate Professor Marina Theodorescu (NUA). </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 14. The survey as a public posting on Google+ Time Maps page
<p>During this experimentation phase a symposium was organized at Vădastra School with the purpose to present our learning experiment to a group of 30 teachers from the Olt County. An open history lesson on the Time Maps web site was held by a history teacher, and a school girl described the Facebook page of the Vădastra School (Figure13), maintained by both teachers and children. The invited teachers gave a feedback on the effectiveness and utility of the Time Maps learning system by responding to a questionnaire-based survey, which was posted on the Google+ page (Figure 14). </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 10. The educational blog on Google+ Time Maps page – the glass manufacturing techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D<br> reconstruction), slightly different from those available on the Time Maps web site, but containing<br> the same information. The children had to make a little effort to relate this information with the one<br> presented on the site, to make a connection between the questions, the fragments from videos at<br> which the answers referred to and the information from the site.<br> The set of questionnaires lead the school children through the majority of data offered by the<br> web site regarding to the two historical periods (Figures 9, 10).</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 5. Fiber artist Alexandra Rusu (NUA) working at a Roman vertical loom (video movie)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages</p>
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.