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9,152 results for “Networking”
Cancer-specific homogeneous networks for TREE
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Yggdrasil: Reducing Network I/O Tax with (CXL-Based) Distributed Shared Memory
<p>used for ICPP24 - Yggdrasil:Reducing Network I/O Tax with (CXL-Based) Distributed Shared Memory Evaluation code and image.</p>
Rewired SSCx network model with 2nd-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 2nd-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 3rd-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 3rd-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 1st-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 1st-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 4th-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 4th-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
PLANT SPECIES RECOGNITION USING LEAF IMAGES AND CONVOLUTIONAL NEURAL NETWORKS (CAAR dataset, version 1)
<p>The CAAR dataset contains leaf images from plants obtained from the Arboreal Collection at Augusto Ribas Agricultural College (CAAR/UEPG). This plant collection is situated in Augusto Ribas College, located at the Ponta Grossa State University, Ponta Grossa, Paraná, Brasil. The images were taken using a smartphone camera with a resolution of 1659 x 2658 pixels and 24 bits of color depth. For each plant, images samples were collected using a white paper sheet background, varying the leaf orientation. The<br>number of plant species used to build the dataset is equal to 35. Data augmentation, using rotation and zoom, were used to increase the data size from 730 to 1986 images in this dataset.</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>
Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks
<p>This dataset contains the behavioral choice data obtained from N = 230 participants that played a two-armed bandit task (139 females, age: 34 +/- 10 years) in partial and complete feedback conditions, as described in (Findling, Skvortsova et al., 2019, Nature Neuroscience, https://doi.org/10.1038/s41593-019-0518-9).</p> <div> <div> <div> <p>The experiment was performed on the Prolific platform (prolific.co) and the research was carried out following the principles and guidelines for experiments including human participants provided in the declaration of Helsinki and approved by the relevant authorities (Inserm Ethical Review Committee, IRB #00003888). All participants provided written informed consent prior to their inclusion.</p> </div> </div> </div>
Exploring the potential effects of Periplaneta americana (L.) extract on Alzheimer's disease based on network pharmacology combined with cellular experiments
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Achieving Peak Performance: Unveiling the Best Hyperparameter Combination for Accurate Skin Disorders Pose Classification with Convolutional Neural Network
<p>This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.</p>
Evolution of digital consumer behavior: A bibliometric analysis of the impact of social networks on purchase determinations
<p><span>In the current context of a globalized and globalized and highly digitized marketplace, the study of customer behavior has become fundamental to behavior has become fundamental to understanding the complex interactions between social, cultural and commercial between social, cultural and commercial factors that influence purchasing decisions. purchasing decisions. This research aims to analyze the the impact of social media and purchase determinations on customer behavior through a bibliometric behavior through a bibliometric study. A qualitative, descriptive, non-experimental descriptive approach with a non-experimental and longitudinal design, analyzing 1,213 documents 1,213 documents extracted from Scopus, using VOSviewer and Bibliometrix for scientific mapping and bibliometric scientific mapping and bibliometric analysis. The results reveal a rapidly growing field (13.33% per Year field (13.33% per year) with an average of 16.63 citations per document and a 28.77% co-citation rate and 28.77% of international co-authorships. Emerging trends were identified the influence of social networks on purchase intent, the impact of influencers and the importance of social commerce. Semantic análisis semantic analysis highlighted the centrality of terms such as "social media", "consumer behavior" and "purchase intention", "consumer behavior" and "purchase intention". It is concluded that the field is dynamic, interdisciplinary and globally diverse, with a growing integration of technology and consumer behavior. integration of technology and consumer behavior. The implications suggest the need for more sophisticated digital marketing strategies that consider the influence of that consider the influence of social networks, the credibility of influencers and the influencer credibility and trust, and the need for more sophisticated digital marketing strategies that consider the influence of social networks, influencer credibility and trust.</span></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>
Data from: Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas
<p><strong><span>General Information</span></strong></p> <p><span>This dataset was used in the study titled "Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas". The study employs citizen science data collected from the Infomedusa application to assess the presence of jellyfish on beaches along the Andalusian coast, along with environmental data to analyze the factors influencing jellyfish distribution. The study aims to employ machine learning techniques, specifically a Multi-Layer Perceptron (MLP) neural network, to classify user comments on the presence or absence of jellyfish and analyze how environmental factors such as sea surface temperature, wind direction, and wind speed influence jellyfish distribution.</span></p> <p><strong><span>Dataset Columns</span></strong></p> <ul> <li><strong><span>Fecha</span></strong><span>: Timestamp of the comment made by the user in the Infomedusa application.</span></li> <li><strong><span>Municipio</span></strong><span>: Name of the municipality where the beach mentioned in the comment is located.</span></li> <li><strong><span>Jellyfish</span></strong><span>: Binary variable indicating the presence (1) or absence (0) of jellyfish according to the user’s comment.</span></li> <li><strong><span>Comunidad</span></strong><span>: Autonomous community to which the municipality belongs.</span></li> <li><strong><span>Provincia</span></strong><span>: Province to which the municipality belongs.</span></li> <li><strong><span>Latitud</span></strong><span>: Geographical latitude of the municipality where the comment was made.</span></li> <li><strong><span>Longitud</span></strong><span>: Geographical longitude of the municipality where the comment was made.</span></li> <li><strong><span>Set</span></strong><span>: Set of grouped beaches for geographical analysis. Each set includes beaches close to each other and the nearest weather station.</span></li> <li><strong><span>Month</span></strong><span>: Month when the comment was made.</span></li> <li><strong><span>Longitud_sea</span></strong><span>: Longitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>Latitud_sea</span></strong><span>: Latitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>SST</span></strong><span>: Sea Surface Temperature at the nearest point in the sea to the municipality, obtained from the Copernicus Marine Environment Monitoring Service.</span></li> <li><strong><span>Wind_dir</span></strong><span>: Wind direction measured at the weather station closest to the municipality, provided by the Spanish Meteorological Agency (AEMET).</span></li> <li><strong><span>Wind_speed</span></strong><span>: Wind speed measured at the weather station closest to the municipality, provided by the AEMET.</span></li> </ul> <p><strong><span>Data Sources</span></strong></p> <ul> <li><strong><span>Infomedusa APP</span></strong><span>: Application developed by the Provincial Council of Malaga and Aula del Mar of Malaga to monitor the presence of jellyfish through citizen participation.</span></li> <li><strong><span>Copernicus Marine Environment Monitoring Service (CMEMS)</span></strong><span>: Provides data on sea surface temperature with an hourly temporal resolution and a spatial resolution of 0.0625° x 0.0625°.</span></li> <li><strong><span>Agencia Estatal de Meteorología (AEMET)</span></strong><span>: Provides daily data on wind direction and speed.</span></li> </ul>
Replication package for: Network formation and efficiency in linear-quadratic games: An experimental study
<p>This is a replication package for the manuscripted titled "Network formation and efficiency in linear-quadratic games: An experimental study" published in the Economic Journal. </p>
Revealing the psychological structure of Pancasila values in social networks
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Accelerating Whole-Sample Polarization-Resolved Second Harmonic Generation imaging in Mammary Gland Tissue via Generative Adversarial Networks
<p>Authors:</p> <p>Arash Aghigh, Jysiane Cardot, Melika Saadat Mohammadi, Gaëtan Jargot, Heide Ibrahim, Isabelle Plante, François Légaré</p> <p>Affiliations:</p> <p> 1. Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada.<br> 2. Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique, Laval, Québec, Canada.</p> <p>Corresponding Author:</p> <p>Arash Aghigh, arash.aghigh@inrs.ca</p> <p>Description:</p> <p>This dataset accompanies the research on improving whole-sample Polarization-Resolved Second Harmonic Generation (P-SHG) imaging in mammary gland tissue using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). The novel approach significantly reduces imaging time while maintaining high image quality and analytical accuracy, demonstrating a reduction in imaging time by more than 95%. This method also minimizes laser-induced photodamage, lowers costs of optical components, and increases the accessibility and applicability of P-SHG imaging in various fields.</p> <p>Keywords:</p> <p>Polarization-Resolved Second Harmonic Generation, P-SHG, Generative Adversarial Networks, GAN, ESRGAN, Mammary Gland Imaging, Super-Resolution, Image Upscaling, Deep Learning, Biomedical Imaging</p> <p>Funding Information:</p> <p> • Canada Foundation for Innovation<br> • Fonds de recherche du Québec–Nature et technologies<br> • Natural Sciences and Engineering Research Council of Canada<br> • New Frontiers Research Fund<br> • NSERC CREATE program (scholarship for Arash Aghigh)</p> <p>Related Identifiers:</p> <p> • GitHub repository for ChaiNNer program: https://github.com/chaiNNer-org/chaiNNer<br> • Download links for models used: https://openmodeldb.info</p> <p>Additional Information:</p> <p>Animal studies were conducted according to the procedures provided by the Canadian Council on Animal Care. The protocol (2005-02) was reviewed and approved by the Institutional Committee for Animal Protection of the Laboratoire National de Biologie Expérimentale (LNBE), the animal facilities based at the Institut National de Recherche Scientifique (INRS).</p>
Convolutional Neural Networks and their Activations: An Exploratory Case Study on Mounded Settlements
<p>This data set represents the output of our paper "Convolutional Neural Networks and their Activations: An Exploratory Case Study on Mounded Settlements". It comprises png images of 400x800 pixels. Half of the image represents a section of CORONA imagery where a site (files named Site_.png) or no site (files named NonSite_.png) is present. The other half of the image contains the activation map produced by various explainability techniques.</p>
ScienceDex guides
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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.