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151 results for “network scaling”
Fine-scale structure of the 2016-2017 Central Italy Seismic Sequence from data recorded at the Italian National Network
<p><strong>Data Set </strong></p> <p>Catalog of 33,983 earthquakes located during the 2016-2017 Central Italy seismic sequence. The velocity model used is the 1D gradient P- and S-wave velocity models (after Carannante et al., 2013). We used the highest quality P- and S-wave arrival times manually picked by analysts of the National Institute of Geophysics and Volcanology (INGV) seismic monitoring room, having an uncertainty lower than 0.6 s. </p> <p>Events were located by means of a 2-step procedure: the INGV routine absolute locations computation for all events with ML ≥ 1.5 that occurred in the study area between August 2016 and January 2018, using the method described in Chiaraluce et al. (2017); the determination of relative locations by applying the HypoDD code (Waldhauser, 2001) to the catalog picks and phase delay times measured from waveform cross correlation.</p> <p>The time domain cross-correlation method (Schaff et al., 2004; Schaff and Waldhauser, 2005) was applied to seismograms of all pairs of events separated by 3 km or less and recorded at common stations. Seismograms were filtered in the 1-15 Hz frequency range using a 4 pole, zero phase band‐pass Butterworth filter. The correlations measurements were performed on 0.7 s long window for P-waves and 1 s windows for S-waves. Only measurements with correlation coefficients greater than 0.7 were kept, resulting in a total of ~4.4 million P and ~1.1 million S wave delay times. </p> <p>We sub-divided the entire dataset in 18 rectangular boxes, containing a maximum of 6000 earthquakes, orthogonal to and centered on the mean strike of the seismic sequence. The overlap between neighboring boxes is 50% with respect to the NW-SE extension. HypoDD is run separately on each box. Resulting relative locations from all boxes were combined into a single catalog, computing the weighted mean of double hypocenters in the overlapping regions (Waldhauser and Schaff, 2008).</p> <p>The final double-difference catalog includes 33,982 events occurring between 24<sup>th</sup> of August 2016 and 18<sup>th</sup> of January 2018.</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-ingv: ingv eventid, useful to link to the QuakeML phase file through the INGV fdsnws/event webservice (<a href="https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2Fwebservices.ingv.it%2Fswagger-ui%2Fdist%2F%3Furl%3Dhttps%3A%2F%2Fingv.github.io%2Fopenapi%2Ffdsnws%2Fevent%2F0.0.1%2Fevent.yaml">http://webservices.ingv.it/swagger-ui/dist/?url=https://ingv.github.io/openapi/fdsnws/event/0.0.1/event.yaml</a>) and to the reported magnitude;</li> <li>Latitude(°) expressed in decimal degrees;</li> <li>Longitude(°) expressed in decimal degrees;</li> <li>Depth(km) hypocentral depth expressed in kilometers;</li> <li>Year of origin time in the format yyyy;</li> <li>Month of origin time in the format mm;</li> <li>Day of origin time in the format dd; </li> <li>Hour of origin time in the format hh;</li> <li>Minute of origin time in the format min;</li> <li>Second of origin time in the format ??.?????? s;</li> <li>Magnitude: the value available at the phases downloading time (see Id-ingv fdsnws/event)</li> </ul> <p> </p> <p> </p> <p> </p> <p><br> </p>
Supplementary data: Accurate large-scale simulations of siliceous zeolites by neural network potentials
<p><strong>Content</strong></p> <p><em>1. Zeolite databases</em></p> <ul> <li>Deem database containing 331170 hypothetical zeolite frameworks [Deem09, Pophale11] geometrically optimized at the NNPscan level (note, the first row of the database is alpha-quartz): "DEEM_NNPscan.db"</li> <li>Database of 236 exiting zeolite frameworks of the <a href="http://www.iza-structure.org/databases/">International Zeolite Association (IZA) </a>optimized at the NNPscan level: "IZA_NNPscan.db"</li> <li>Both databases are <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE SQLite database files</a> of the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment</a> containing the ASE <a href="https://wiki.fysik.dtu.dk/ase/ase/atoms.html">Atoms objects</a> with energies and forces (NNPscan level); readable with ASE's <a href="https://wiki.fysik.dtu.dk/ase/ase/io/io.html">I/O module</a></li> <li>Additionally, relevant quantities can be extracted with, e.g., the following queries (further information: ase db --help):</li> </ul> <pre><code class="language-bash">ase db DEEM_NNPscan.db -c id,formula,natoms,volume,mass,density,energy_per_tsite,n_tsites,relative_energy # Output id|formula|natoms| volume| mass|density|energy_per_tsite|n_tsites|relative_energy 1|O6Si3 | 9|111.161|180.249| 26.988| -31.796| 3| 0.000 2|O16Si8 | 24|433.858|480.664| 18.439| -31.638| 8| 15.265 3|O16Si8 | 24|421.114|480.664| 18.997| -31.596| 8| 19.359 4|O16Si8 | 24|426.557|480.664| 18.755| -31.614| 8| 17.613 5|O16Si8 | 24|412.410|480.664| 19.398| -31.613| 8| 17.677 6|O16Si8 | 24|393.544|480.664| 20.328| -31.594| 8| 19.546 7|O16Si8 | 24|422.400|480.664| 18.939| -31.657| 8| 13.476 8|O16Si8 | 24|394.405|480.664| 20.284| -31.581| 8| 20.797 9|O12Si6 | 18|265.201|360.498| 22.624| -31.611| 6| 17.868 10|O16Si8 | 24|357.047|480.664| 22.406| -31.581| 8| 20.785 11|O16Si8 | 24|434.894|480.664| 18.395| -31.621| 8| 16.911 12|O16Si8 | 24|384.158|480.664| 20.825| -31.657| 8| 13.448 13|O12Si6 | 18|258.977|360.498| 23.168| -31.679| 6| 11.278 14|O16Si8 | 24|466.429|480.664| 17.152| -31.593| 8| 19.588 15|O16Si8 | 24|423.469|480.664| 18.892| -31.639| 8| 15.179 16|O16Si8 | 24|450.716|480.664| 17.750| -31.628| 8| 16.219 17|O16Si8 | 24|331.528|480.664| 24.131| -31.642| 8| 14.857 18|O16Si8 | 24|458.573|480.664| 17.445| -31.635| 8| 15.572 19|O16Si8 | 24|359.298|480.664| 22.266| -31.655| 8| 13.636 20|O16Si8 | 24|464.264|480.664| 17.232| -31.612| 8| 17.750 Rows: 331171 (showing first 20) Keys: density, energy_per_tsite, n_tsites, relative_energy ase db IZA_NNPscan.db -c id,formula,natoms,volume,mass,density,energy_per_tsite,n_tsites,relative_energy,iza_code # Output id|formula |natoms| volume| mass|density|energy_per_tsite|n_tsites|relative_energy|iza_code 1|O16Si8 | 24| 435.488| 480.664| 18.370| -31.676| 8| 11.594|ABW 2|O32Si16 | 48| 961.419| 961.328| 16.642| -31.645| 16| 14.612|ACO 3|O96Si48 | 144|3154.579|2883.984| 15.216| -31.664| 48| 12.810|AEI 4|O80Si40 | 120|2102.921|2403.320| 19.021| -31.703| 40| 9.021|AEL 5|O96Si48 | 144|2417.286|2883.984| 19.857| -31.666| 48| 12.586|AEN 6|O144Si72| 216|4075.300|4325.976| 17.667| -31.674| 72| 11.831|AET 7|O96Si48 | 144|2786.810|2883.984| 17.224| -31.675| 48| 11.716|AFG 8|O48Si24 | 72|1400.247|1441.992| 17.140| -31.690| 24| 10.268|AFI 9|O64Si32 | 96|1764.823|1922.656| 18.132| -31.653| 32| 13.809|AFN 10|O80Si40 | 120|2080.330|2403.320| 19.228| -31.707| 40| 8.632|AFO 11|O64Si32 | 96|2097.384|1922.656| 15.257| -31.655| 32| 13.622|AFR 12|O112Si56| 168|3820.116|3364.648| 14.659| -31.650| 56| 14.150|AFS 13|O144Si72| 216|4732.720|4325.976| 15.213| -31.664| 72| 12.793|AFT 14|O60Si30 | 90|1897.074|1802.490| 15.814| -31.659| 30| 13.268|AFV 15|O96Si48 | 144|3154.885|2883.984| 15.214| -31.664| 48| 12.776|AFX 16|O32Si16 | 48|1137.335| 961.328| 14.068| -31.591| 16| 19.790|AFY 17|O48Si24 | 72|1283.812|1441.992| 18.694| -31.620| 24| 17.034|AHT 18|O96Si48 | 144|2479.287|2883.984| 19.360| -31.681| 48| 11.155|ANA 19|O64Si32 | 96|1797.086|1922.656| 17.807| -31.662| 32| 12.924|APC 20|O64Si32 | 96|1751.393|1922.656| 18.271| -31.678| 32| 11.422|APD Rows: 236 (showing first 20) Keys: density, energy_per_tsite, iza_code, n_tsites, relative_energy # Filtering of the database, e.g., for structures with relative energies < 10 kJ/(mol Si) ase db IZA_NNPscan.db relative_energy\<10 -c density,energy_per_tsite,n_tsites,relative_energy,iza_code # Output density|energy_per_tsite|n_tsites|relative_energy|iza_code 19.021| -31.703| 40| 9.021|AEL 19.228| -31.707| 40| 8.632|AFO 19.385| -31.695| 24| 9.802|ATV 18.778| -31.702| 34| 9.061|DOH 19.570| -31.693| 24| 9.959|EWO 18.401| -31.698| 32| 9.451|GON 18.551| -31.695| 112| 9.807|IHW 17.778| -31.693| 288| 9.972|IMF 19.154| -31.695| 6| 9.762|JBW 18.187| -31.695| 96| 9.734|MFI 19.278| -31.709| 48| 8.443|MRE 18.035| -31.698| 90| 9.481|MSO 20.417| -31.724| 44| 7.003|MTF 19.227| -31.704| 136| 8.898|MTN 18.542| -31.693| 28| 9.966|MTW 19.137| -31.695| 60| 9.798|PCR 20.037| -31.709| 144| 8.464|PSI 18.843| -31.703| 64| 9.004|SAF 18.371| -31.703| 112| 8.975|STO 19.894| -31.706| 17| 8.671|VET Rows: 20 (showing first 20) Keys: density, energy_per_tsite, iza_code, n_tsites, relative_energy</code></pre> <ul> <li>The quantities shown above are available with the keys (besides standard ASE database keys):</li> </ul> <table> <thead> <tr> <th scope="col">Key</th> <th scope="col">Quantity</th> <th scope="col">Unit</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>Identifier</td> <td> </td> </tr> <tr> <td>formula</td> <td>Chemical formula of the unit cell</td> <td> </td> </tr> <tr> <td>natoms</td> <td>Number of atoms</td> <td> </td> </tr> <tr> <td>volume</td> <td>Unti cell volume</td> <td>Å<sup>3</sup></td> </tr> <tr> <td>mass</td> <td>Atomic mass of the unit cell</td> <td>amu</td> </tr> <tr> <td>density</td> <td>Framework density</td> <td>Si/nm<sup>3</sup></td> </tr> <tr> <td>energy_per_tsite</td> <td>NNPscan energy</td> <td>eV</td> </tr> <tr> <td>n_tsites</td> <td>Number of T-sites</td> <td> </td> </tr> <tr> <td>relative_energy</td> <td>Energy with respect to quartz</td> <td>kJ/(mol Si)</td> </tr> <tr> <td>iza_code</td> <td>only for 'IZA_NNPscan.db'</td> <td> </td> </tr> </tbody> </table> <ul> <li> Comma separated csv files for the quantities listed above: "DEEM_NNPscan.csv" and "IZA_NNPscan.csv"</li> </ul> <p><em>2. Neural network potentials (NNP) for silica</em></p> <ul> <li>SchNet [Schütt18,Schütt19] NNP files trained on DFT data at the PBE+D3 (NNPpbe) and SCAN+D3 level (NNPscan)</li> <li>Simulations can be performed using <a href="https://schnetpack.readthedocs.io/en/stable/getstarted/getstarted.html#references">SchNetPack</a> with its ASE calculator</li> <li>This example shows a simple single-point calculation</li> </ul> <pre><code class="language-python">import ase.io import torch from schnetpack.interfaces import SpkCalculator from schnetpack.environment import AseEnvironmentProvider # check if GPU(s) are available if torch.cuda.is_available(): device = "cuda" else: device = "cpu" # load the NNP model model = torch.load('SiOscan1', map_location=device) # read some structure atoms = ase.io.read( ... ) # define SchNetPack calculator calc = SpkCalculator(model=model, device=device, energy='energy', forces='forces', environment_provider=AseEnvironmentProvider(6.) ) # attach calculator to atoms object atoms.set_calculator(calc) # perform simulations, e.g., single-point calculation energy = atoms.get_potential_energy() print(energy)</code></pre> <p><em>3. Test set used for accuracy evaluation (ASE database: test_set_NNPscan.db)</em></p>
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"
<p>As the 3rd generation partnership project (3GPP) organization pushes out new releases,<br>positioning in heterogeneous mobile networks enables the achievement of the accuracy required<br>in the majority of industrial applications without dependence on global navigation<br>satellite systems (GNSS). This study presents the results gathered during an extensive measurement<br>campaign related to the practical applicability of localization in next-generation<br>heterogeneous networks. We present an accuracy comparison of basic timing advance (TA)<br>localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF),<br>extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural<br>network. Our results demonstrate that TA cannot be considered an optimal solution<br>from the perspective of localization accuracy because the error roughly corresponds to the<br>average separation distance from the base station (BS) to the end device (ED). In addition,<br>we found that the LSTM approach is not optimal for the outdoor localization of moving<br>ED because of the combination of multiple factors, with sparse deployment being the most<br>important. The median value of the location error of the LSTM was more than 200m higher<br>than that of the TA for the self-validation dataset. However, a simple KNN regression shows<br>solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN<br>provided the most accurate results of all methods, with median error values of approximately<br>12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.</p>
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
Hierarchically embedded scales of movement shape the social networks of vampire bats
<p>Social structure can emerge from <em>hierarchically embedded scales of movement</em>, where movement at one scale is constrained within a larger scale (e.g., among branches, trees, forests). In most studies of animal social networks, some scales of movement are unobserved, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (<em>Desmodus rotundus</em>), we created an agent-based model of how three nested scales of movement—among roosts, clusters, and grooming partners—each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically-derived rates that were either fixed or individually variable and either independent or correlated across scales. We found the number of partners groomed per bat was driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks.</p>
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network
<p>Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, <em>"Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"</em></p>
The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse
<p>The Reddit Politosphere is a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. Based on the <a href="https://doi.org/10.5281/zenodo.3608135">Pushshift Reddit Dataset</a>, it is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data. We also release annotated metadata for subreddits and users.</p> <p>Documentation and scripts for easy data access are provided in an associated <a href="https://github.com/valentinhofmann/politosphere">repository</a> on GitHub.</p>
Supplementary Material of : Large-Scale 3D Image Segmentation Using Scattering Networks
<p>The reader will find here the supplementary material associated with the manuscript "Large-Scale 3D Image Segmentation Using<br> Scattering Networks" submitted to IEEE Transaction of Pattern Analysis and Machine Intelligence (TPAMI), 2022.</p>
MCPNet : A parallel maximum capacity-based genome-scale gene network construction framework
<p>This deposit contains the gene expression profile datasets used for the paper titled "MCPNet : A parallel maximum capacity-based genome-scale gene network construction framework". </p> <p>There are three sets of data:</p> <ul> <li>Simulated yeast data from NetBenchmark, with random noise injected, as well as the ground truth network matrix. In "SimulatedYeast.zip".</li> <li>Real Yeast dataset and the ground truth network as an adjacency list file. in "yeast_data.exp" and "yeast_gs1_list_filtered.tsv". Data acquired from "Castro DM, de Veaux NR, Miraldi ER, Bonneau R (2019) Multi-study inference of regulatory networks for more accurate models of gene regulation. PLoS Comput Biol 15(1): e1006591. https://doi.org/10.1371/journal.pcbi.1006591", <a href="https://github.com/simonsfoundation/multitask_inferelator/tree/AMuSR">https://github.com/simonsfoundation/multitask_inferelator/tree/AMuSR</a>.</li> <li>Real Arabidopsis athaliana datasets for 5 tissues and 1 environmental challenge. <ul> <li>athaliana_gs_probes.tsv : ground truth as an adjacency list</li> <li>microarray gene expression profiles for "development", "leaf", "seed", "flower", "seedling1week", "hormone-aba-iaa-ga-br".</li> <li>Aathaliana.Datasets-CEL-File-URLs.xlsx: list of SRA accession numbers for the A. athaliana datasets</li> </ul> </li> </ul>
The Virtual Macaque Brain: A macaque connectome for large-scale network simulations in TheVirtualBrain
<p>A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing and diffusion-weighted imaging data. Created for modeling brain dynamics using TheVirtualBrain platform. Website: thevirtualbrain.org</p>
Results from Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics
Open the record for dataset details and reuse information.
Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks
<p>Information processing and communication in neuronal circuits is enabled by dynamic networks of inter-areal coupling of neuronal oscillations in which hubs play a central role for regulation of communication. Oscillations are shaped by interactions between pyramidal cells and interneurons and are locally influenced by neuromodulatory systems. Here, we set out to investigate how sparial variability in neurotransmitter receptor and transporter density influences frequency-specific large-scale networks of phase-synchrony (PS) and amplitude-correlation (AC) in human magnetoencephalography data. We found that node centrality - indexing which individual brain regions function as hubs - covaried positively with GABA, NMDA, dopaminergic, and most serotonergic receptor and transporter densities in lower frequency bands (delta to low-alpha for PS, and delta for AC) and in the gamma band, but negatively in between. These results establish how local microarchitecture influences large-scale connectivity networks of neuronal oscillations in the human brain in frequency- and spatially-specific patterns.</p>
Dataset for: IoT deployment for city scale air quality monitoring with Low-Power Wide Area Networks
<p>Air Quality (AQ) is a very topical issue for many cities and has a direct impact on the health of its citizens. We propose to investigate the air quality of a large UK city using low-cost commodity Particulate Matter (PM) sensors, and compare them with government operated air quality stations. In this pilot deployment we design and build six AQ IoT devices, each with four different low-cost PM sensors and deploy them at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide-Area Network network coverage. We conclude that some low-cost PM sensors are viable for monitoring AQ and demonstrate that our device design can be used via LoRaWAN to facilitate more granular city coverage without limitations of network access. Based on these findings we intend to deploy a larger LoRaWAN enabled Air Quality sensor network deployment across the city.</p>
Research Artefact: What network simulator questions do users ask? a large-scale study of stack overflow posts
<p><strong>Research Artefact: What network simulator questions do users ask? a large-scale study of stack overflow posts</strong></p> <p>This is a research artefact for the paper: <strong>What network simulator questions do users ask? a large-scale study of stack overflow posts</strong>. This artefact is a repository consisting of the collected dataset including 2,322 network-simulator-related Stack Overflow questions. This artefact aims to enable researchers to replicate our dataset of the paper and reuse the dataset for further research.</p>
NsCircle datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Datasets with the simulations of the incompressible flow around an elliptical as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:<br> Multi-scale rotation-equivariant graph neural networks for<br> unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - train/NsEllipse<br> - test/NsEllipseLowRe<br> - test/NsEllipseHighRe<br> - test/NsEllipseThin<br> - test/NsEllipseThick<br> - test/NsEllipseNarrow<br> - test/NsEllipseWide<br> - test/NsEllipseAoA</p> <p> </p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <p>@article{lino2022multi,<br> author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},<br> title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},<br> journal = {Physics of Fluids},<br> volume = {34},<br> year = {2022},<br> url = {https://doi.org/10.1063/5.0097679},<br> }<br> </p>
NsEllipse datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Datasets with simulations of the incompressible flow around an elliptical cylinder as described by the incompressible Navier-Stokes equations.</p> <p>These simulations were used to train and test the MuS-GNN models in the paper:<br> "Multi-scale rotation-equivariant graph neural networks for<br> unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - train/NsEllipse<br> - test/NsEllipseLowRe<br> - test/NsEllipseHighRe<br> - test/NsEllipseThin<br> - test/NsEllipseThick<br> - test/NsEllipseNarrow<br> - test/NsEllipseWide<br> - test/NsEllipseAoA</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <p>@article{lino2022multi,<br> author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},<br> title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},<br> journal = {Physics of Fluids},<br> volume = {34},<br> year = {2022},<br> url = {https://doi.org/10.1063/5.0097679},<br> }<br> </p>
Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679).</p> <p>The training code is available at https://github.com/mario-linov/graphs4cfd.</p>
A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements
<p>Mobile networks have become highly complex systems. In order to better understand how network features affect performance and suggest additional improvements, it is crucial to examine them from an empirical perspective. In the following, we present a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT) and 5G New Radio (NR) connectivity. We collected our dataset during a period of seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization tasks.</p> <p><br>If you use our dataset in your research, we kindly request that you cite the following paper:</p> <p>K. Kousias <em>et al</em>., "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," in <em>IEEE Communications Magazine</em>, vol. 62, no. 5, pp. 44-49, May 2024, doi: 10.1109/MCOM.011.2200707.</p>
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.