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9,152 results for “Networking”
MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network
<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>
Perturbation response scanning of drug-target networks: Drug repurposing for multiple sclerosis
<p>This dataset contains molecular dynamics trajectories and corresponding input files used for simulations. The simulations were conducted using GROMACS and include detailed information about the simulated systems, such as the types of molecules, box dimensions, temperature, pressure, simulation time range, and time step.</p>
Supporting material for Kempf, M & Denis, S (2024): Resource dependency and communication networks in Early Neolithic Central-West Europe. Quaternary Environments and Humans
<p><span>Table 1. </span><span>Site names, location, and attributes of the sample used during the analysis. Sites are organised according to stage dependency. Coordinates are in WGS84, EPSG:4326.</span></p> <p><span><span>Table 2</span><span>.</span> Description of the site status<span>, based on the stages of the CO and the frequency of the sites according to the lengths of the calculated LCP at the different chronological stages (see repository for heatmaps of the LCP lengths: 10.5281/zenodo.10617484). Sites are classified following the 4 levels presented in Figure 5 in Kempf & Denis (2024). </span></span></p> <p> </p>
Dataset of paper "Neural Network Modeling of Black Box Controls for Internal Combustion Engine Calibration"
<p>These are the two training datasets for the Exhaust Temperature Model (ETM) and the Three-Way Catalyst Control Lambda Set Point (TWCC) Model, used in Black-Box Modeling, such as through Neural Networks.</p> <p>The datasets for each black box model are stored in XLSX files. Each dataset file contains ECU channel inputs, related Map parameters, and output. The ECU channel inputs and Map parameters are collected with Sobol methods. The output is generated by Hardware-in-the-Loop (HiL) corresponding to the related ECU channel inputs and Map parameters.</p> <p>The ETM model has four effective Maps, hence there are 32 corresponding shape-based algorithm parameters. Its output is the exhaust temperature. Its ECU channel inputs are as follows:</p> <p>CH_1: Intake valve cut-off condition</p> <p>CH_2: Thrust cut-off condition</p> <p>CH_3: Cylinder equalization control work cycle injection enabled</p> <p>CH_4: Ignition angle efficiency</p> <p>CH_5: Relative level of reduction</p> <p>CH_6: Lean engine lambda for cylinders on exhaust bank with lambda split</p> <p>CH_7: Rich engine lambda for cylinders on exhaust bank with lambda split</p> <p>CH_8: Exhaust gas mass flow</p> <p>CH_9: Engine speed</p> <p>CH_10: Relative air filling</p> <p>CH_11: Material temperature</p> <p>CH_12: Engine temperature</p> <p> </p> <p>The TWCC model has one Map, hence there are 8 corresponding shape-based algorithm parameters. Its output is lambda set point. Its ECU channel inputs are as follows:</p> <p>CH_1: Exhaust mass flow</p> <p>CH_2: Trigger bit for inhibiting the purge</p> <p>CH_3: Trigger bit for a rich mixture</p> <p>CH_4: Trigger bit for empty catalyst</p> <p>CH_5: Trigger bit for catalyst reset</p> <p>CH_6: Oxygen storage level</p> <p>CH_7: Desired air-fuel ratio</p>
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>
Dataset for "MA-MGAN: Mixed Attention Markovian Generative Adversarial Network for Meteorological Downscaling"
<p>Dataset for "MA-MGAN: Mixed Attention Markovian Generative Adversarial Network for Meteorological Downscaling", including the training set and testing set used by the model, as well as the experimental results in the main text and supplementary Information.</p>
Instances of the problem of Designing a Multi-sink Clustered Wireless Sensor Network.
Open the record for dataset details and reuse information.
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network
<p>This repository includes the code and all relevant files used throughout our study. These encompass everything from the initial sheets of the whole acoustic dataset utilised for selecting the annotated dataset to the notebook (.ipynb) and the recordings employed in training the model.</p> <p>The .wav file are compressed in the file: <a href="../api/records/11220741/draft/files/wind-noise-detection-main.7z/content" target="_blank" rel="noopener noreferrer">wind-noise-detection-main.7z</a>/data/208_file_recordings_paper_wind.tar</p> <p> </p> <p> </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>
Figure 2. Geographic distribution and haplotype networks for 12S in Effects of Quaternary climatic oscillations over the Chacoan fauna: phylogeographic patterns in the southern three-banded armadillo Tolypeutes matacus (Cingulata: Chlamyphoridae)
Figure 2. Geographic distribution and haplotype networks for 12S (top panels) and control region (bottom panels). The panels on the left plot the geographical distribution and frequency of haplotypes in the different localities analysed. Localities were labelled according to their ID (see Table 1). The right panels show the haplotype networks, where the dashes on the lines represent mutations, and the black circles represent intermediate variants not found. Principal Chacoan rivers are shown in light-blue labels. Capitalized labels indicate names of Argentinean provinces, and labels with all letters in uppercase refer to neighbouring countries.
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>
Networks files of main scenarios analysed in "Distributed photovoltaics provides key benefits for a highly renewable European energy system"
<p>This repository contains the network files (.nc) of the main scenarios (A, B, C, and D) used for analysis in the paper. The code for reproducing these files plus other network files used for sensitivity analysis plus the Jupyter notebooks used for creating all the figures in the paper are available at: https://github.com/Parisra/Distributed-PV-paper </p>
Synthetic dataset and prediction files for the paper "Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction", by Costantino et al. (2024)
<p>Synthetic database used for training and evaluation of SSEdenoiser</p>
MESA files for: "Progenitor stars calculated with small reaction networks should not be used as initial conditions for core collapse"
<p>Reproduction package for RNAAS [TBD].<br><br>Use MESA r24.03.1 and the provided template folder in `MESA_template.tar.xz` to reproduce the MESA models. Resolution tests can be done changing in `inlist1` the parameters `mesh_delta_coeff`, `mesh_time_coeff`, and `mesh_delta_coeff_for_highT` (see commented option).<br>Use the provided `environment.yml` and the script `compare_two_models.py` provided in `scripts.tar.xz` with a few dependencies to reproduce the figure in the research note.</p>
Collaborative High-Resolution Observation Datasets of an Eddy Using an Underwater Glider Network
<p>This matlab mat data provides data from 12 underwater gliders in the northern South China Sea in 2017.</p> <p>Please contact Haibo Tang at tanghb6@mail2.sysu.edu.cn for any questions. <br> <br>Wish you good luck!</p>
Temporal overlays of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and network for VOSViewer visualization
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Geographical overlays of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and network for VOSViewer visualization
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Base outline of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and Network for VOSViewer visualization
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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>
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.