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1,721 results for “network data”
Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the fifth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - 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/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/egINC9hJifI</p>
Learning about Learning: Mining Human Brain Sub-Network Biomarkers from fMRI Data
<p>These are coherence matrices originally described in: Dynamic reconfiguration of human brain networks during learning. Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. Proc Natl Acad Sci U S A. 2011 May 3;108(18):7641-6. doi:10.1073/pnas.1018985108. Epub 2011 Apr 18. Later the matrices were also studied in Cohesive network reconfiguration accompanies extended training. Telesford QK, Ashourvan A, Wymbs NF, Grafton ST, Vettel JM, BassettDS. Hum Brain Mapp. 2017 Sep;38(9):4744-4759. doi: 10.1002/hbm.23699. Epub 2017 Jun 24.</p>
The KISS principle in Software-Defined Networking: a framework for secure communications - dkreutz data
<p>Scripts, summarized data, plots</p> <p>type of data: raw data and processed data</p>
Supplementary Data:Risk Analysis for Real-time Flood Control Operation of a Multi-reservoir System Using a Dynamic Bayesian Network
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Flood data</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
Data for: Intersectin1 promotes clathrin-mediated endocytosis by organizing and stabilizing endocytic protein interaction networks
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Simulated performance data: MILC, LAMMPS, and uniform random traffic patterns on 72-ndoe dragonfly network
<p>Data generated from an old, private fork of the CODES simulation toolkit: https://github.com/codes-org/codes</p> <p>Data dictionary: https://github.com/kevinabrown/codes/wiki/Dragonfly-Dally-DEBUG-Metrics</p>
Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Results Data
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Data and Code for "Fault Network Geometry Influences Earthquake Frictional Behavior"
<p>This dataset contains the data and code necessary to reproduce the results presented in the paper, "Fault-Network Geometry Influences Earthquake Frictional Behavior" <em>Nature</em> (2024), authored by J. Lee, V. C. Tsai, G. Hirth, A. Chatterjee, and D. T. Trugman.</p> <p>https://doi.org/10.1038/s41586-024-07518-6</p>
Data for "Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure"
<p>New York City contact network data used in the publication “<a href="https://doi.org/10.1016/j.socnet.2024.06.003">Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure</a>” (LA-UR-23-26868). This contact network comes in the form of a weighted edge list. Each row describes an edge, with the first and second column containing the labels of the nodes connected by the edge, and the third column contains the corresponding weight of the edge. In this network, an edge encodes an interaction between two individuals and the weight describes the duration of the interaction in seconds. In total the edge list describes 6,376,729,847 interactions among 6,813,615 individuals; the first 10 interactions are listed below as an example.</p> <p>2, 1, 84121<br>4, 3, 83654.4<br>5, 3, 79591.4<br>5, 4, 87642<br>6, 3, 79853<br>6, 4, 81604<br>6, 5, 79146<br>8, 7, 80604<br>10, 9, 84259.6<br>12, 11, 68990.8</p> <p> </p> <p>This work is approved for public distribution under LA-UR-24-25046.</p>
Source research data for the article titled "A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN)".
<p>The source research data set developed and utilized while working on the article "A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN)" for Sensors SI. The data set includes simulation results from OMNeT++ and the data to evaluate the parameters of the algorithms.</p>
Dataset: Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies
<p>Dataset for the following paper: <a href="https://arxiv.org/abs/2405.04401">"Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies", Julien Baglio, arXiv:2405.04401</a></p> <p>It contains:</p> <ul> <li>one folder named "data_for_all_plots" containing the raw data for the s, t, and y distributions for all the figures of the paper as well as a Jupyter notebook to generate the figures.</li> <li>one file named "variance_calculations_qGAN.txt" containing the data to calculate the errors for the KL divergences.</li> </ul>
Data for "Investigating molecular transport in the human brain from MRI with physics-informed neural networks"
<p>Data analyzed in Zapf <em>et al.</em> <a href="https://www.nature.com/articles/s41598-022-19157-w">Investigating molecular transport in the human brain from MRI with physics-informed neural networks</a> (Scientific Reports 2022).</p> <p>The data consists of CSF tracer concentrations in the brain subregions analyzed in the article. The data was pre-processed as described in S1.1 in the supplementarty materials. </p> <p>In Python, load the data as numpy arrays using the nibabel package as</p> <pre><code>import nibabel data = nibabel.load("068/concentrations/24h.mgz").get_fdata() domain_mask = nibabel.load("068/masks/roi.mgz").get_fdata().astype(bool)<br>And view slices of the data as:</code></pre> <div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>150</span><span>, </span><span>0</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>100</span><span>, </span><span>1</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>100</span><span>, </span><span>2</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>show</span><span>()</span></div> </div> <pre> </pre>
Supporting data for "Neural Network-Based Interatomic Potential for the Study of Thermal and Mechanical Properties of Siliceous Zeolites"
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Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data
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Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
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Data from: Ecosystem engineers shape ecological network structure and stability: a framework and literature review
<p>Ecosystem engineering is a ubiquitous process where species influence the physical environment and thereby structure ecological communities. However, there has been little effort to synthesise or predict how ecosystem engineering may impact the structure and stability of interaction networks. To assess the current scientific understanding of ecosystem engineering impacts via habitat forming, habitat modification, and bioturbation on interaction networks/food webs, we reviewed the literature covering marine, freshwater, and terrestrial food webs, plant-pollinator networks, and theory. We provide a conceptual framework and identify three major pathways of engineering impact on networks through changes in resource availability and energy flow, habitat heterogeneity, and environmental filtering. These three processes often work in concert and most studies report that engineering increases species richness. This is particularly marked for engineers that increase habitat heterogeneity and thereby the number of available niches. The response of network structure to ecosystem engineering varies, however some patterns emerge from this review. Engineered habitat heterogeneity leads to a higher number of links between species in the networks and increases link density. Connectance can be negatively or positively affected by ecosystem engineer impact, depending on the engineering pathway and the engineer impact of species richness. We discuss how ecosystem engineers can stabilize or destabilize communities through the changes in niche space, diversity, network structure, and the dependency on the engineering impact. Theory and empirical evidence need to inform each other to better integrate ecosystem engineering and ecological networks. A mechanistic understanding how ecosystem engineering traits shape interactions networks and their stability will be important to predict species extinctions and can provide crucial information for conservation and ecosystem restoration.</p>
Code and Data for "Efficiency and Resilience: Key Drivers of Distribution Network Growth"
<p>This repository contains the code and data used in the paper titled "Efficiency and Resilience: Key Drivers of Distribution Network Growth".</p> <p>The data includes adjacency matrices for the 22 distribution networks analyzed in the study.</p> <p>The code implements the network growth model presented in the paper.</p>
Dataset of miRNA-Disease Relations Extracted from Textual Data using Transformer-based Neural Networks
<p>Supplementary Data.</p>
Data from: Pharmacoeconomic study of anti-influenza virus drugs in Japan based on a network meta-analysis
<p><strong>Objectives:</strong> An analysis was conducted in Japan to determine the most cost-effective neuraminidase inhibitor for the treatment of influenza virus infections from the healthcare payer's standpoint.</p> <p><strong>Methods:</strong> This study reanalyzed the findings of a previous study that had some limitations (no probabilistic sensitivity analysis, quality of life scores measured by the EQ-5D-3L instead of the EQ-5D-5L, and the use of a decision tree model with only three health conditions) by using data from a network meta-analysis study. A decision tree model with eight health conditions was constructed, and costs were identified as medical costs and drug prices (the 2020 version of the Japanese medical fee index). The effectiveness outcomes were measured using EQ-5D-5L questionnaires for adult patients who had previously experienced influenza virus infections. The time horizon was 14 days. Both deterministic and probabilistic sensitivity analyses were performed to examine the robustness of the results.</p> <p><strong>Results:</strong> The base-case cost-effectiveness analysis revealed that oseltamivir outperformed laninamivir, zanamivir, and peramivir, making it the most cost-effective neuraminidase inhibitor. The deterministic and probabilistic sensitivity analyses showed robust results that validated oseltamivir as the most cost-effective among the four neuraminidase inhibitors.</p> <p><strong>Conclusions:</strong> This study thus reconfirmed oseltamivir's position as the most cost-effective neuraminidase inhibitor for the treatment of influenza virus infections in Japan from the standpoint of healthcare payment. These findings can help decision-makers and healthcare providers in Japan, including pharmacists, create and manage formularies.</p>
Replication data for: Processed food intake assortativity in the personal networks of older adults
<p>This is the replication data for the scientific paper titled "Processed food intake assortativity in the personal networks of older adults." For details on how to use the data files, please consider the "Supplementary Material" file and the paper preprint where the context of the study and the variables of interest are presented.</p>
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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.