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9,152 results for “Networking”
Communicating with the Network & Landscape Workshops - Dr Samantha Kanza (University of Reading, University of Southampton)
<p>This video is the eleventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Communicating with the Network & Landscape Workshops - Dr Samantha Kanza (University of Southampton).</p> <p>Bio: Dr Samantha Kanza is an Enterprise Fellow at the University of Southampton. She completed her MEng in Computer Science at the University of Southampton and then worked for BAE Systems Applied Intelligence for a year before returning to do an iPhD in Web Science (in Computer Science and Chemistry), which focused on Semantic Tagging of Scientific Documents and Electronic Lab Notebooks. She was awarded her PhD in April 2018. Samantha works in the interdisciplinary research area of applying computer science techniques to the scientific domain, specifically through the use of semantic web technologies and artificial intelligence. Her research includes looking at electronic lab notebooks and smart laboratories, to improve the digitization and knowledge management of the scientific record using semantic web technologies; and using IoT devices in the laboratory. She has also worked on a number of interdisciplinary Semantic Web projects in different domains, including agriculture, chemistry and the social sciences.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/fvRb9ULGU2I</p>
Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)
<p>This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)</p> <p>Bio: <strong><a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> </strong>Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ChdbggScUgo</p>
Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the first talk from our Landscape Report Funding Call Town Meeting that took place on the 03/02/2022.</p> <p>Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/03-02-2021-future-blood-testing-network-landscape-report-funding-town-meeting/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/I1xsig9C8w0</p>
Dataset of pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use
<p>This is the dataset of the manuscript entitled "Pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use", which was submitted for publication. The dataset include the functional traits of 162 insect pollinator species and 315 interactions with the mangrove species <em>Avicennia germinans, Conocarpus erectus, Laguncularia racemosa,</em> and <em>Rhizophora</em> <em>mangle</em>. The manuscript evaluates the effects of mangrove patch size and surrounding land use on pollinator functional diversity and plant-pollinator interactions in seven mangrove patches from the Colombian Caribbean region. Data variables are pollinator order, family, species, functional traits (pollinator guilds, body size, feeding preference, sociality, and nesting site) and frequency, interacting mangrove species, mangrove patch name, coordinates and size (ha), surrounding land use areas (urban areas, croplands, conserved dry forest, degraded vegetation areas, beach and water) and landscape diversity (Shannon H').</p>
Estimation of axial loads in tie-rods: Dataset generated from Finite Element simulations for training Artificial Neural Network
<p>Dataset employed for training the Artificial Neural Networks (ANNs) presented in the cited journal article. The trained ANNs were used to estimate the tensile force in tie-rods installed in a historical structure (the church of the monastery of Sant Cugat close to Barcelona) from dynamic parameters obtained from vibration testing.</p> <p>The dataset consists of input-otput data generated using finite element (FE) simulations. A blank column has been used to separate input data from output data.</p> <p>More details on the nature of the data and how it was employed can be found in the following journal article, which is supplemented by this upload:<br> <em><strong>Makoond N, Pelà L, Molins C. Robust estimation of axial loads sustained by tie-rods in historical structures using Artificial Neural Networks. Structural Health Monitoring. 2022;0(0). doi:</strong></em><strong><a href="https://doi.org/10.1177/14759217221123326">10.1177/14759217221123326</a></strong></p> <p><a href="https://www.researchgate.net/publication/364098652_Robust_estimation_of_axial_loads_sustained_by_tie-rods_in_historical_structures_using_Artificial_Neural_Networks">Link to author's version of accepted manuscript</a></p> <p>This work was supported by the Servei del Patrimoni Arquitectònic of the Generalitat de Catalunya through a project (managed by the City Council of Sant Cugat) aimed at monitoring the church of the Monastery of Sant Cugat (grant number C-10764). Financial support is also acknowledged from the Ministry of Science, Innovation and Universities of the Spanish Government and the ERDF (European Regional Development Fund) through the SEVERUS project (Multilevel evaluation of seismic vulnerability and risk mitigation of masonry buildings in resilient historical urban centres) (grant number RTI2018-099589-B-I00).</p>
Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data
<p><span><span>An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. <br></span></span></p> <p><span><span>In this article we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps.<br></span></span></p> <p><span><span>Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification.<br></span></span></p> <p><span><span>Using local data to adjust the recognition model for the target domain leads to improvement over general non-tailored solutions. The process introduced in this article can be applied to build a fine-tuned bird sound classification model for a specific environment.</span></span></p>
Exoplanet atmosphere evolution: emulation with neural networks: supplementary data
<p>Supplementary data for 'Exoplanet atmosphere evolution: emulation with neural networks'. Includes MCMC chain for Bayesian Hierarchical Model (BHM) including samples of core mass for all planets, as well 5 hyper parameters (see paper for details). Additionally, a machine readable version of Table 1 is made available.</p>
Machine Learning Enabled Multi-Radio Access Technology Selection in 5G Networks
<p>In this paper, we present a machine learning algorithm for effective RAT selection in 5G networks by considering the geo-location (latitude and longitude) of the user as well as the received signal strength intensity (RSSI) from the base station as basic parameters, real live data from a 5G network base-station were collated, divided into training and testing data-sets, the training data-sets (input) were used to train models of supervised machine learning classification algorithm: Decision Tree (DT), Extra Tree (XTREE), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost); these trained models are further tested with input test data-sets to predict/select the appropriate RAT (4G/5G) as labelled output. Evaluation of results showed a measure of accuracy of our chosen model of RAT selection; (XGBoost) at optimal level 93.86\%, which was further cross validated at 92.9\% when compared with other algorithms for its effectiveness on future data and mitigation ability on over-fitting and under-fitting issues, hence recommended for planning and optimization purposes in similar urban/dense-urban environment to assist in maintaining the rapidly increasing demand of network connections and devices.</p>
Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles
<p>Both individual and group behavior can influence individual fitness, but multilevel selection is rarely quantified on social behaviors. Social networks provide a unique opportunity to study multilevel selection on social behaviors, as they describe complex social traits and patterns of interaction at both the individual and group levels. In this study, we used contextual analysis to measure the consequences of both individual network position and group network structure on individual fitness in experimental populations of forked fungus beetles (<em>Bolitotherus</em> <em>cornutus</em>) with two different resource distributions. We found that males with high individual connectivity (strength) and centrality (betweenness) had higher mating success. However, group network structure did not influence their mating success. Conversely, we found that individual network position had no effect on female reproductive success but that females in populations with many social interactions experienced lower reproductive success. The strength of individual-level selection in males and group-level selection in females intensified when resources were clumped together, showing that habitat structure influences multilevel selection. Individual and emergent group social behavior both influence variation in components of individual fitness but impact male mating success and female reproductive success differently, setting up intersexual conflicts over patterns of social interactions at multiple levels. </p>
WaterGEMS models for flow starved water transmission network
<p><strong>This data contains WaterGEMS models of a Water Transmission Network (WTN) in India. It deploys a newly developed modeling approach, Pressure Driven Analysis for Flow Straved Condition(PDA-FSC). It is used for evaluating the performance of flow-starved WTN. </strong></p>
Figure 5 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 5. Moving colonies to imperialist in culture and language axes (AtashpazGargari et al. 2008).
Figure 2 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 2. Generalized semivariogram showing the range of spatial dependence, nugget effect (C0) variability associated with spatial dependence (C), and sill (C + C0).
Figure 5 in Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Iran
Figure 5. Tetranychus urticae distribution maps in actual (b, d and f) and classified conditions by MLPNN (c, e and a). The maps of a, c, e and b, d, f have been drawn according to economic threshold of 4, 8 and 12, respectively.
Data Used in [~Re] Setting Inventory Levels in a Bike Sharing Network
<p>Data used to reproduce the publication "Setting an Inventory Levels in a Bike Sharing Network" by Datner et al.</p> <p>This data correspond to the scenarios generated from the parameters given by the authors.</p>
Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"
<p><strong>DESCRIPTION</strong> 📓</p> <p>"data" folder directory contains the datasets for classification and detection. </p> <ol> <li>The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images.</li> <li>The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images</li> </ol> <p> </p> <p>The "weights" folder contains pretrained models for classification and detection tasks. </p> <ul> <li>The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia.</li> <li>The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>.</li> <li>You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below.</li> </ul> <p><em>Classification weights</em></p> <ol> <li>EfficientNetv2-M</li> <li><strong>ResNeSt-101e</strong> (🚀 RECOMMENDED)</li> <li>ResNet-101d</li> <li>ReXnet-100</li> <li>SeResNet-152d</li> </ol> <p><em>Detection weights</em></p> <ol> <li>YOLOR-W6-1280</li> <li>YOLOX-X-640</li> <li>YOLOv5-X-640</li> <li>YOLOv5-X-1280</li> <li>YOLOv5-M6-1280</li> <li><strong>YOLOv5-L6-1280</strong> (🚀 RECOMMENDED)</li> </ol> <p>Read README.md file for more details</p>
Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks
<p>Classification dataset used in Kosiba et al. 2020 (10.1093/mnras/staa1723) consisting of candidate clusters in the XCLASS survey</p> <p>Training and testing images and corresponding labels low-z (clusters, 0<z<0.3), hi-z (clusters, z>0.3), nearby galaxy, point source (point, double source, star/AGN), and other (artefact, edge)</p>
Social Network analysis on European countries involved in agroecology research
<p>All the 124 (68 European and 56 Transnational) agroecology research projects identified in the mapping activities carried out by the task 1.3 of the AE4EU project were used to perform a weighted social network analysis (SNA) having the participating countries as nodes and collaborations in projects as edges.</p> <p>This dataset contains data related to this SNA and consists of two sheets:</p> <ul> <li><strong>Indexes</strong> where values of some measures for each identified country in the social network analysis are reported (number of European agroecological research projects coordinated by the country; number of transnational agroecological research projects coordinated by the country; Degree Centrality; Closeness Centrality)</li> <li><strong>Edge_weights</strong> where the weights for each edge between two countries are provided according to the times two countries cooperated together for a European or a transnational project.</li> </ul>
Statistics of the Network of Linked Vocabularies
<p>Reusing terms in the <a href="https://lod-cloud.net/">Linked Open Data cloud</a> results in a <a href="https://sites.google.com/view/nelo-evolution">Network of Linked vOcabularies (NeLO)</a>, where the nodes are the vocabularies that use at least one term from some other vocabulary and thus depend on each other. These dependencies become a problem when vocabularies in the network change, e.g., when terms are deprecated or deleted. In these cases, all dependent vocabularies in the network need to be updated. To address this shortcoming, we compute the state of NeLO from the available versions of the vocabularies in a period of time of over 17 years.</p> <p>Specifically, we provide the following statistics for each vocabulary in each year from 2001 and 2018 in three different formats (RDF/XML, JSON-LD, and CSV):</p> <ul> <li>in-degree;</li> <li>out-degree;</li> <li>degree;</li> <li>eccentricity;</li> <li>closeness centrality;</li> <li>harmonic closeness centrality;</li> <li>betweenness centrality;</li> <li>Authority;</li> <li>Hub;</li> <li>PageRank.</li> </ul> <p>The publication in the reference contains also further information on the analyzed vocabularies and on the methodology.</p> <p>This dataset is provided for non-commercial use only. If you find this dataset useful in your work, please cite the publication in the reference.</p>
Speedtest-like Measurements in 3G/4G Networks: the MONROE Experience
<p>The goal of this research is to collect a wide range speedtest-like traces for the mobile broadband (MBB) networks, as seen from actual users. In our experiment, the MONROE node contains the core components, with containers that run active experiments. Traffic generated by the applications passes through the selected MiFi modem where a NAT is in place, then goes through the ISP network, and the Internet, toward the selected server. Each node runs also Tstat, a specialized passive sniffer. Tstat captures traffic on each MBB interface and extracts statistics by passively observing packets exchanged with the network. Another instance of Tstat runs on the server side, thus capturing and processing traffic at the other end of the path.<br> <br> Each MONROE node regularly runs a basic set of experiments. Among these, the HTTP download experiment uses single thread curl to download a 40 MB file for a maximum of 10 seconds from dedicated and not-congested servers in Sweden. we collected measurements during September and October 2016 in four countries and different sites. We consider only stationary nodes. The experiment ran every 3 hours in synchronized fashion. The active application and passive flow-level traces on the client and server sides cannot give us information about the technology and signal strength at the MBB channel during the experiment. Therefore, we use the metadata collected by the MONROE platform to augment the information about the access link status. The MONROE metadata are event-based data collected by passively monitoring the statistics exposed directly from the MiFi modems through their management interface. This data is transmitted and stored in the project database for analysis, and can be easily correlated to each node and interface.</p>
Supplementary material 2 from: Trochet A, Schmeller D (2013) Effectiveness of the Natura 2000 network to cover threatened species. Nature Conservation 4: 35-53. https://doi.org/10.3897/natureconservation.4.3626
Supplementary material 2 from: Trochet A, Schmeller D (2013) Effectiveness of the Natura 2000 network to cover threatened species. Nature Conservation 4: 35-53. https://doi.org/10.3897/natureconservation.4.3626
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.