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9,152
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9,152 results for “Networking”
Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: a Bayesian Network Modeling Approach
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An Interpretable 3D Multi-hierarchical Representation-based Deep Neural Network for EH&S Properties Prediction
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Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism
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Supplementary material 3 from: Paquet J-Y, Swinnen K, Derouaux A, Devos K, Verbelen D (2022) Sensitivity mapping informs mitigation of bird mortality by collision with high-voltage power lines. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 215-233. https://doi.org/10.3897/natureconservation.47.73710
Table S4
Supplementary material 2 from: Paquet J-Y, Swinnen K, Derouaux A, Devos K, Verbelen D (2022) Sensitivity mapping informs mitigation of bird mortality by collision with high-voltage power lines. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 215-233. https://doi.org/10.3897/natureconservation.47.73710
Table S2, S3
Research on key generic technology prediction based on graph neural networks under the perspective of patent citation - An example from the field of genetic engineering
<p>In this research, we adopted graph neural network models for key generic prediction based on cited patent data. Through the construction of the patent citation network and the design of a key generic evaluation system, 20879 relevant patents and 51,610 irrelevant patents were screened out. Further, we utilized the LDA topic model to interpret technical topics at a finer granularity. Finally, to test the effectiveness of this method, we took the field of genetic engineering as an example for key generic technology prediction, with an accuracy rate of 95%.</p>
Dataset for "Reconstruction of Excitation Waves from Mechanical Deformation using Physics-Informed Neural Networks"
<p>Archive consisting of the synthetic datasets used in "Reconstruction of Excitation Waves from Mechanical Deformation using Physics-Informed Neural Networks" (<a title="https://doi.org/10.1038/s41598-024-67597-3" href="https://doi.org/10.1038/s41598-024-67597-3" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1038/s41598-024-67597-3</span></a>) as well as the results after PINN optimization. Datasets include the electrical simulation data, generated active tension and resulting deformation. The PINN_results.zip file covers all results discussed in the paper. Short txt files give more information on the data format. The code used to create the synthetic dataset, construct and optimize the PINNs, and generate the figures from the paper can be found on <a href="https://gitlab.com/heartkor/scripts-2d-deformation-pinn">https://gitlab.com/heartkor/scripts-2d-deformation-pinn</a>.</p>
Data series of seismic events for the article "Seismic monitoring using the telecom fiber network"
<p>The file "catalog.h5" contains the catalog of seismic events analyzed in the paper "Seismic monitoring using the telecom fiber network" by S. Donadello et al., Commun Earth Environ 5, 178 (2024) <a href="https://doi.org/10.1038/s43247-024-01338-2">https://doi.org/10.1038/s43247-024-01338-2</a> (formerly "Earthquake observatory with coherent laser interferometry on the telecom fiber network" on arXiv preprint).</p> <p>The reported data correspond to raw recordings, acquired by coherent interferometry techniques on a telecommunication fiber (see also <a href="doi.org/10.1109/TIM.2023.3288255">https://doi.org/10.1109/TIM.2023.3288255</a>), and decimated to a lower sampling rate.</p> <p>The catalog is organized as about 900 seismic events in the period between June 19th, 2021 and Sept. 26th, 2022, between Feb. 6th and March 23th 2023, and between Nov. 9th, 2022 and Nov. 18th, 2022, according to the criteria described in the paper.</p> <p>A detailed description of the ".h5" file format is provided in "h5_file_description.txt".</p> <p>A Python3 script "h5_cat_parser.py" for data interpretation in terms of standard python structures is provided.</p>
Solution-State Methyl NMR Spectroscopy of Large Non-Deuterated Proteins Enabled by Deep Neural Networks
<p>This dataset relates to the publications:</p> <p><a href="https://doi.org/10.1038/s41467-024-49378-8">Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks.</a> <span>Karunanithy G, Shukla VK, Hansen DF<strong>. </strong></span><span>Nat Commun. 2024 Jun 13;15(1):5073. doi: 10.1038/s41467-024-49378-8.</span></p> <ul> <li>Training data for Deep Neural Networks developed in the manuscript "Solution-State Methyl NMR Spectroscopy of Large Non-Deuterated Proteins Enabled by Deep Neural Networks"</li> <li>Experimental cross-validation data: <ul> <li>2D spectra of HDAC8, MSG, and a7a7 proteasome</li> <li>3D NOESY spectra of MSG</li> </ul> </li> </ul> <p>Please see GitHub (https://github.com/gogulan-k/FID-Net) for additional scripts and details. </p>
Data from: Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation
<p>Simulation data from Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation, doi: 10.3389/fsysb.2024.1291293</p> <p>There are 10 csv files per network type, each dataset contains the Timestep, Node, Strain, Inducer Concentration/Duration, and Node weights for the timecourse of the simulation. </p>
Applying High-Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network
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Data and Software: Upsampling Monte Carlo Reactor Simulation Tallies in Depleted SFR Assemblies using a Convolutional Neural Network
<p>Datasets and code used in upsampling OpenMC SFR simulation neutron flux tallies.</p>
STCGAN: a novel Cycle-Consistent Generative Adversarial Network for Spatial Transcriptomics Cellular Deconvolution
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Raw data for 'Long-baseline Quantum Sensor Network as Dark Matter Haloscope'
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Neural network for predicting Peierls barrier spectrum and its influence of dislocation motion
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SNSynergy: similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations
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Deep Neural Network Surrogate for Surface Complexation Model of Metal Oxide/Electrolyte Interface
<p>These files are the data used in the paper "<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ncAYQ4MAAAAJ&sortby=pubdate&citation_for_view=ncAYQ4MAAAAJ:LkGwnXOMwfcC">Deep neural network surrogate for surface complexation model of metal oxide/electrolyte interface</a>".</p> <ul> <li>CSV files are used to train the DNN model.</li> <li>NPZ files are used to train the random forest model. </li> </ul>
MANAGEMENT SYSTEM MODELS OF MULTISERVICE NETWORKS BUILDING ON A SYSTEMATIC APPROACH
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Interactome Analysis: miRNA-gene networks from Austrolebias charrua exposed to Roundup (Cytoscape files .cys)
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Part of the dataset and trained models in "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media" by Meng et al. in Geophysical Research Letters
<p><strong>This repository is created to contain part of the data, codes and trained models in the research project titled "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media".</strong></p> <p> </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.