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Dataset results
19 results for “dynamic graphs”
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>
PubMed-Temporal: A dynamic graph dataset with node-level features
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Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks - Dataset
<p>This is the dataset of "Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks"</p>
An example of citation graphs showing year by year dynamics
<p>This is an example of citation graphs showing year by year dynamics. On x-axis there are years of publication while on y-axis the total number of citing articles for a given paper. Size of node also shows the total number of citing articles. This visualization is made with help of the Gephi application.</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>
Supplementary Materials for Time-Aligned Edge Plots for Dynamic Graph Visualization
<p><strong>Abstract</strong>: We present <em>time-aligned edge plots</em>: time- and edge-scalable representations of dynamic graphs. Vertices are mapped to two vertical parallel axes. The left axis depicts the source vertices, whereas the right one depicts the destination vertices. The time axis is horizontally embedded in-between the two axes, resulting in a two-dimensional graph layout. Edges are added by drawing straight lines connecting the corresponding source and destination vertices through time, while the pixels along the lines are used to encode the time-varying information. In this way, the depiction of edges at the individual timepoints is reduced to only a few pixels, resulting in a less cluttered representation of dynamic graphs, while the alignment of edges over time reveals the temporal patterns in the data and preserves the users' mental map. We evaluate our approach by comparing it theoretically and empirically against the state-of-the-art using dynamic graphs of varying complexities.</p>
A dynamic ancestral graph model and GPU-based simulation of a community based on metagenomic sampling
<p>In this paper we present an ancestral graph model of the evolution of a guild in an ecological community. The model is based on a metagenomic sampling design in that a random sample is taken at the community, as opposed the taxon, level and species are discovered by genetic sequencing. The specific implementation of the model envisions an ecological guild that was founded by colonization at some point in the past that then potentially undergoes diversification by natural selection. Within the graph, species emerge and evolve through the diversification process and their densities in the graph are dynamic and governed by both ecological drift and random genetic drift, as well as differential viability. We employ the 3% sequence divergence rule at a marker locus to identify Operational Taxonomic Units. We then explore approaches to see if there are indirect signals of the diversification process, including population genetic and ecological approaches. In terms of population genetics, we study the joint site frequency spectrum of OTUs, as well its associated statistics. In terms of ecology, we study the species (or OTU) abundance distribution. For both we observe deviations from neutrality, which indicates that there may be signals of diversifying selection in metagenomic studies under certain conditions. The model is available as a GPU-based computer program in C/C++ and using OpenCL, with the long-term goal of adding functionality iteratively to model large-scale eco-evolutionary processes for metagenomic data.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Helheim Glacier, Greenland.</p> <ul> <li>ISSM_DGL_Helheim.py: Python file for training GNN models</li> <li>ISSM_CNN_Helheim.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results</li> </ul>
Dynamic Knowledge Graphs for Continual Learning of Embeddings
<p>These datasets are generated from real world usecases. They are treated as Knowledge graphs and include 20 snapshots, where between two snapshots there are 10% added links and 10% deleted links, making the first and last snapshot non-overlapping.</p>
A dynamic ancestral graph model and GPU-based simulation of a community based on metagenomic sampling
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Supplementary material for "Comprehensive framework for dynamic energy assessment of building systems using IFC graphs and Modelica"
<p>The provided files include the following:</p> <ul> <li>The IFC model of the demo building used in Section 2.</li> <li>The parsed space boundaries graph.</li> <li>The resulting fragmented space boundaries graph.</li> </ul>
Appendix for "Is JavaScript Call Graph Extraction Solved Yet? A Comparative Study of Static and Dynamic Tools"
<p><strong>Abstract</strong></p> <p>The popularity and wide adoption of JavaScript both at the client and server-side makes its code analysis more essential than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on the call graph representation of the underlying program. Luckily, there are quite a few tools to get this job done already. However, their performance in vitro and especially in vivo has not yet been extensively compared and evaluated.</p> <p>In this paper, we systematically compare five static and two dynamic approaches for building JavaScript call graphs on 26 WebKit SunSpider benchmark programs and two static and two dynamic methods on 12 real-world Node.js modules. The tools under examination using static techniques were <em>npm call graph</em>, <em>IBM WALA</em>, <em>Google Closure Compiler</em>, <em>Approximate Call Graph</em>, and <em>Type Analyzer for JavaScript</em>. We performed dynamic analyzes relying on the <em>nodejs-cg</em> tool (a customized Node.js runtime) and the <em>NodeProf </em>instrumentation and profiling framework.</p> <p>We provide a quantitative evaluation of the results, and a result quality analysis based on 941 manually validated call edges. On the SunSpider programs, which do not take any inputs, so dynamic extraction could be complete, all the static tools also performed well. For example, TAJS found 93% of all edges while having a 97% precision compared to the precise dynamic call graph. When it comes to real-world Node.js modules, our evaluation shows that static tools struggle with parsing the code and fail to detect a significant amount of call edges that dynamic approaches can capture. Nonetheless, a significant number of edges not detected by dynamic approaches are also reported. Among these, however, there are also edges that are real, but for some reason the unit tests did not execute the branches in which these calls were included.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Pine Island Glacier, Antarctica
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Pine Island Glacier, Antarctica</p> <ul> <li>ISSM_DGL_PIG2.py: Python file for training GNN models (*single.py: code for single GPU environment)</li> <li>ISSM_CNN_PIG.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results (graphs for GNNs)</li> <li>*.pkl: Datasets of the ISSM transient simulation results (grids for CNNs)</li> </ul>
Data for "Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks"
<p>This dataset contains snapshots of cell monolayers, represented as graphs, along with their corresponding average displacement measurements.</p>
Anomaly Detection on Dynamic Knowledge Graphs
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Functionally Important Residues from Graph Analysis of Co-evolved Dynamical Couplings
<p>This dataset contains input files and trajectories for class A β-lactamase SHV-1.</p>
Analysis Titanic Survival through Graph-Based Node with Neo4j: Unraveling Social Dynamics (Dataset & Cypher)
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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.