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1,721 results for “network data”
Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
<p> </p> <div> <div><a href="https://arxiv.org/search/cs?searchtype=author&query=Tello,+A">Andres Tello*</a><em>, </em><a href="https://arxiv.org/search/cs?searchtype=author&query=Truong,+H">Huy Truong*</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lazovik,+A">Alexander Lazovik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Degeler,+V">Victoria Degeler</a>. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks. Engineering Proceedings. 2024; 69(1):50. <a href="https://doi.org/10.3390/engproc2024069050">https://doi.org/10.3390/engproc2024069050</a></div> <br> <div>(*) Both authors contributed equally.<br><br></div> <h2>Update</h2> <div>(04/09/2024): Citation is updated.<br>We have added headers for CSVs and auxiliary data (duration time, edge list, ordered names.. ) in the configuration file (JSON format). As such, corresponding INP files can be omitted when working with this version. <br>The EXN network has been included in this version, so the total number of processed networks is 11.<br>For more details, please read ZENODO_README.md.</div> <h2>Contact</h2> <div>For dataset-related questions: <a href="mailto:h.c.truong@rug.nl" target="_blank" rel="noopener">Huy Truong</a></div> <br> <div>For data acquisition: <a href="mailto:a.tello@rug.nl" target="_blank" rel="noopener">Andres Tello</a></div> <br> <div>If you use this dataset, please cite:</div> <blockquote>@article{tello2024largescale,<br> AUTHOR = {Tello, Andrés and Truong, Huy and Lazovik, Alexander and Degeler, Victoria},<br> TITLE = {Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks},<br> JOURNAL = {Engineering Proceedings},<br> VOLUME = {69},<br> YEAR = {2024},<br> NUMBER = {1},<br> ARTICLE-NUMBER = {50},<br> URL = {https://www.mdpi.com/2673-4591/69/1/50},<br> ISSN = {2673-4591},<br> DOI = {10.3390/engproc2024069050}<br>}</blockquote> </div>
Data for "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations"
<p>These data are shown in the figures included with the article "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations," submitted to Atmospheric Chemistry and Physics.</p>
Data for "CryoDRGN-ET: Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells"
<p>Trained models weights, training parameters, sampled density maps, reconstructed density maps for featured classes, and plotting scripts are included for each of the following datasets and training runs:</p> <ul> <li><em>M. pneumoniae</em> ribosome, initial training run with all 18,466 particles, 1 tilt per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 10 tilts per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 41 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, initial training run with all 119,031 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 93,281 filtered particles, 10 tilts per particle</li> <li><em>S. cerevisiae</em> ribosome, training run with 30,657 particles in the non-rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 62,624 particles in the rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, initial training run with all 33,492 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, training run with all 5,239 filtered particles, 10 tilts per particle</li> </ul>
Lausanne environmental data : Sentinel L1C Lausanne region data - NDVI, ARVI indices; Walkable network
<p>Multi-spectral band images captured by the Sentinel L1C satellites in August 2016 over Lausanne, Switzerland. Bands B02, B03, B04, B08 projected according to the Swiss coordinates system (EPSG:21781- CH1903 / LV03 -- Swiss CH1903 / LV03) and associated calculated ARVI and NDVI vegetation indices.</p> <p>Walkable network (2021) from OpenStreeMap using OSMnx python package</p>
Evaluating Cloud Properties Across New Zealand: Ceilometer Network Data
<p><span>A network of 16 Vaisala CL31 ceilometers operated by MetService at airports across New Zealand had data available and were processed using ALCF with a cloud threshold mask of 6e-6. Nine sites were in Te Ika-a-Māui / North Island, six in Te Waipounamu / South Island and one site on Chatham Island. Metadata for the 16 sites are available in the Processed Ceilometer Medata.csv file.<br></span></p>
Data for: Facile Preparation of Tunable Polyborosiloxane Networks via Hydrosilylation
<p>Polyborosiloxanes are used in a variety of fields due to their unique and useful dynamic properties. Traditionally, crosslinked polyborosiloxanes are prepared by incorporating boric acid into siloxane pre-polymers, a process that is time consuming, energy intensive, and challenging due to the immiscibility of the reagents. Here, we report a versatile synthetic method to rapidly cure polyborosiloxane networks via hydrosilylation of chain-end or backbone functionalized polydimethylsiloxane (PDMS) derivatives with an inexpensive trivinylboronate. Networks synthesized from these readily available building blocks cure in ~2 minutes at convenient temperatures (<em>e.g.</em>, 90 °C) and exhibit enhanced viscoelastic behavior when compared to traditional polyborosiloxane networks fabricated via the conventional condensation route. By virtue of using efficient hydrosilylation chemistry, another key advantage of this synthetic platform is the ability to synthesize dynamic polyborosiloxanes with different network connectivity by simply using silicones with Si–H moieties placed at the chain ends (telechelic) or distributed throughout the repeat-unit structure (copolymers). The availability of other alkenes amenable to hydrosilylation provides an additional formulation handle to synthesize mixed dynamic–static networks with tunable control over stress relaxation and solvent resistance. In summary, the synthetic approach disclosed herein is a simple and accessible platform for preparing dynamic polyborosiloxanes with tunable material properties.</p>
The output data of the 1D Venusian chemistry-diffusion model of Dai et al. (2024) and the adopted chemical network
<p>To use these data, please cite the paper: Dai et al. (2024, doi: 10.1051/0004-6361/202450552) </p> <p>Supplemental_Tables_for_Dai_et_al_2024.pdf: the chemical network adopted in the model</p> <p>Nominal.txt: the chemical network adopted in the model</p> <p>modify_chem.py: the additional adjustments of the reactions</p> <p>Nominal_Bkzz_SO2.vul and A_Dkzz_SO2.vul: the output data of the nominal model and model A, respectively</p> <p>reading_data.py: the methods to read the output files</p> <p> </p> <p>*Errata: </p> <p>1) R296 in Supplemental Table: "1×10^7+0.05n_atm" should have been "1×10^17+0.05n_atm"</p> <p>2) R329 in Supplemental Table: should have been removed</p> <p>The errata do not affect the results of this study.</p>
Data set in the form a relational database (sql) to denote a network of service providers, service clients and recommenders
<p>This data-set pertains to a network (i.e. graph) represented in the form of a relational data-base of service providers (nodes), service clients (nodes), service recommenders (nodes) and relationaships between then (i.e. a client used a provider, a recommender recommended a service to another client), along with some initial values of the QoS level perceived by any client whi have used a service and the reputation of a recommender. The data-set can be used for developing a reputation-based trust system. </p>
Thermodynamics of alkali feldspar solid solutions with varying Al–Si order: atomistic simulations using a neural network potential - Accompanying Data
<p>This dataset accompanies the manuscript: "Thermodynamics of alkali feldspar solid solutions with varying Al–Si order: atomistic simulations using a neural network potential". It contains:</p> <ul> <li>LAMMPS-data files of the relaxed 8x6x8 systems for the three ordering types across Na-K composition, </li> <li>template input files for the minimization and for the semi grand canonical Monte Carlo + molecular dynamics simulation,</li> <li>the training and testing data with and without the point charge correction,</li> <li>the neural network potential committee and a modified n2p2 source that is necessary for running the special weighted atom centered symmetry functions. </li> </ul> <p>The algorithm to create the Al-Si and Na-K disorder is hosted on <a href="https://github.com/alexgorfer/Alkali-feldspar-disorder-generator">https://github.com/alexgorfer/Alkali-feldspar-disorder-generator</a> instead.</p>
Selected CO2 and Meteorological Data from BErkeley Atmospheric CO2 Observation Network
<p>Selected CO<sub>2</sub> and meteorological data from BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N) for use in the characterization of the heterogeneity of greenhouse gas concentrations around the San Francisco Bay Area and the constraint of CO<sub>2 </sub>emissions from mobile sources.</p>
Data 13 Bus Distribution Network
<p>Data for research paper Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City.</p> <p> </p> <p>Energies special issue intelligent transportation system for electric vehicles.</p>
Training material for analysis small RNA-seq data (Galaxy Training Network tutorial)
<p>The data provided here is part of the Galaxy Training Network tutorial for analysis of small RNA-seq (sRNA-seq) data using mirdeep2 and miranda. This dataset is provided by INRA (Le Rheu, France).</p>
Vehicle trajectory data in simulation network
<p>The project will use vehicle trajectory data generated from simulation platform. The simulation network was built in VISSIM containing a four-leg intersection with left-turn, through, and right-turn movements. The trajectory data were generated based on various traffic demand levels. The data set contains second-by-second vehicle speed and location. Details of the data set are explained as:</p> <p>Column 1 (NO): Number (Number/Index of the vehicle)<br> Column 2 (SimSec): Simulation second (Simulation time [s]) [s]<br> Column 3 (Lane\Link\No): Lane\Link\Number (Unique number of the link or connector)<br> Column 4 (Lane\Index): Lane\Index (Unique number of the lane)<br> Column 5 (Speed): Speed (Speed at the end of the time step) [km/h]<br> Column 6 (Pos): Position (Distance on the link from the beginning of the link or connector) [m]</p>
Data and code for training neural network parameterizations from an near-global aqua-planet simulation
<p>This commit contains the code, coarse-grained data, processed training data, neural network models, and coupled NN-GCM simulations. It can be extracted by running</p> <pre><code>tar xzf <archive></code></pre> <p>While this archive contains code (it is slightly out of date). This is the up-to-date code: <a href="https://zenodo.org/record/3248586">https://zenodo.org/record/3248586</a></p> <p>Move the "nn", "debiased", and "data" folders from this archive into that code directory.</p> <p> </p> <p> </p>
Wind Technology Network data
<p>This dataset contains nodes and edges of the wind technology diffusion network inferred in the paper:</p> <p> </p> <p>S. Halleck-Vega, A. Mandel & K. Millock, (2018). "Accelerating diffusion of climate-friendly technologies: A network perspective." Ecological Economics, Vol.152, pp 235-245.</p>
Trimmed RNASeq pair for the Galaxy Training Network tutorial - "Metatranscriptomics analysis using microbiome RNASeq data"
<p>Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. <br> This Galaxy training network tutorial will introduce researchers to the basic concepts and tools from the published ASaiM workflow (Batut et al, <em>GigaScience</em> (2018), 7 (6),<a href="http://dx.doi.org/10.1093/gigascience/giy057"> http://dx.doi.org/10.1093/gigascience/giy057</a>). </p> <p>The dataset is a trimmed version of one of the time points from a cellulose degradation biogas reactor dataset. The dataset has been trimmed to facilitate running the workflows for this tutorial. Any biological interpretation from the results would be incorrect, due to the trimmed version of the dataset.</p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
Original Data of Paper: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis
<p>Paper title: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis. Our paper was submitted to PeerJ recently. This data file is the original data of this study which contains all the original data involved in this work.</p>
Supervised learning is an accurate method for network-based gene classification - Data
<p>This file contains the data that was used in the paper titled "Supervised learning is an accurate method for network-based gene classification" (https://doi.org/10.1093/bioinformatics/btaa150). Some data was excluded if the license was not permissive enough.</p>
GIS data - Model sítě vodních toků v ČR | GIS data - Water Stream Network Model for the Czech Republic
<p>GeoTIFF layers (8 x 8 m) containing a modeled network of watercourses in the landscape of the Czech Republic: (1) "streams_def_cr.tif" layer - network of natural watercourses with Strahler order determination; (2) "vodni_toky.tif" layer - model of potentially navigable watercourses. For a detailed description of layers, see https://doi.org/10.5281/zenodo.3367296</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.