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102 results for “Network simulation”
Simulated results from an agent-based model examining inequality and innovation in social networks
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Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
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Accelerating development in UAV network digital twins with a flexible simulation framework
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Datasets for reproduction of simulation study results presented in article "Network Aggregation to Enhance Results Derived from Multiple Analytics"
<p>The included datasets can be used to reproduce the simulation study results presented in the paper:<br> Duroux D., Climente-González H., Wienbrandt L., Van Steen K. (2020) Network Aggregation to Enhance Results Derived from Multiple Analytics. In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 583. Springer, Cham.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 813533.</p>
Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks
<p>This dataset contains the data and scripts for the publication "<a href="https://doi.org/10.1029/2023MS003829">Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks</a>" in <em>Journal of Advances in Modeling Earth Systems</em>.</p> <p>Before going into details, here is a reminder that the NEMO runs for the training dataset are called 'OPM+number'. These are the corresponding names given in the manuscript: OPM006=HIGHGETZ, OPM016=WARMROSS, OPM018=COLDAMU and OPM021=REALISTIC. For the testing dataset: 'bf663' is the REPEAT1970 run and 'bi646' is the 4xCO2 run.</p> <p>Most of the formatting and preprocessing of the training data has been made for <a href="https://tc.copernicus.org/articles/16/4931/2022/">Burgard et al. 2022</a>. The raw and to-some-degree processed data can therefore be found here: <a href="https://doi.org/10.5281/zenodo.7308352">https://doi.org/10.5281/zenodo.7308352</a>. <br>The raw data for the testing dataset is from <a href="https://doi.org/10.1029/2021MS002520">Smith et al. 2021</a>, you can find it here: <a href="doi.org/10.5281/zenodo.7886986">https://doi.org/10.5281/zenodo.7886986</a></p> <p>The following folders and files can be found here:</p> <p>===============<br><strong>raw/</strong></p> <p>Some geometrical files needed for initial data formatting and masking.</p> <p>===============<br><strong>interim/</strong></p> <ul> <li><strong>ANTARCTICA_IS_MASKS</strong>/ (<em>from INTERIM_ANTARCTICA_IS_MASKS.zip</em>): contains <ul> <li>masks and geometric information for the testing dataset to be included in the input file of the neural network and for the classic parameterisations.</li> <li>local bedrock and ice meridional and zonal slopes</li> </ul> </li> <li><strong>BOXES/</strong> (<em>from INTERIM_BOXES.zip</em>): contains variables needed to apply the box parameterisation for the testing dataset</li> <li><strong>PLUMES/ </strong>(<em>from INTERIM_PLUMES.zip</em>): contains the variables needed to apply the plume parameterisation for the testing dataset</li> <li><strong>SMITH_bf663/</strong><em><strong> and </strong></em><strong>SMITH_bi646/ </strong>(<em>from INTERIM_SMITH*.zip</em>): for testing dataset, <ul> <li>corrected_draft_bathy_isf.nc: file containing ice draft and bathymetry corrected by ice draft concentration to account for the biased draft and bathymetry at the grounding line resulting from the interpolation from the native NEMO grid to the stereographic grid (values under ice shelf and NaNs over land</li> <li>custom_lsmask_Ant_stereo_clean.nc: land-sea mask giving 0 = ocean, 1 = shelf, 2 = land</li> <li>isfdraft_conc_Ant_stereo.nc: ice-shelf concentration resulting from the interpolation from the native NEMO grid to the stereographic grid</li> <li>other_mask_vars_Ant_stereo.nc: contains other variables used for the masks</li> <li>the reference melt: 1D containing integrated melt, 2D containing melt fields, box1 containing melt near the grounding line</li> </ul> </li> <li><strong>T_S_PROF/ </strong>(<em>from INTERIM_T_S_PROF.zip</em>) <ul> <li>Mean profiles used as input for traditional parameterisations</li> <li>T and S 2D fields, extrapolated from the mean profiles to the local ice draft depth (needed as input for the neural network)</li> <li>Fields of mean and standard deviation T and S for all points (needed as input for the neural network)</li> </ul> </li> <li><strong>NN_MODELS/</strong><em><strong> </strong>(from </em>INTERIM_NN_MODELS<em>.zip</em>) contains all neural networks trained for this paper (for the cross validation and over the whole dataset for testing)</li> <li><strong>INPUT_DATA/ </strong>(from INTERIM<em>_</em>INPUT_DATA.zip) contains all input csv files containing the input datasets for the different training and testing iterations. Also contains the metrics to normalise the input. For the cross-validation, the input csv files are not included because they are too large. However, they can be reconstructed from the individual files for ice shelves and time blocks. The metrics to normalise the data during the cross-validation are included in EXTRAPOLATED_ISFDRAFT_CHUNKS_CV!</li> </ul> <p>===============<br><strong>processed/MELT_RATE/</strong></p> <p>Contains resulting melt rates</p> <ul> <li><strong>CV_ISF :</strong> Cross-validation results over ice shelves</li> <li><strong>CV_TBLOCKS : </strong>Cross-validation results over time</li> <li><strong>SMITH_bf663 : </strong>Neural network results for REPEAT1970</li> <li><strong>SMITH_bf663_CLASSIC : </strong>"Traditional" parameterisation results for REPEAT1970</li> <li><strong>SMITH_bi646 :</strong> Neural network results for 4xCO2</li> <li><strong>SMITH_bi646_CLASSIC:</strong> "Traditional" parameterisation results for 4xCO2</li> </ul> <p>=====================</p> <p>The explanation around the scripts can be found in README.rst with the scripts in<strong> scripts_paper_simpleNN_basal_melt.zip</strong>.<br><em>Note that these are the scripts needed to produce the results in the paper. You can also find them on Github: </em><a href="https://github.com/ClimateClara/https://github.com/ClimateClara/scripts_paper_simpleNN_basal_melt"><em>https://github.com/ClimateClara/scripts_paper_simpleNN_basal_melt</em></a>, <em>find the most up-to-date version of the package 'multimelt' here: </em><a href="https://github.com/ClimateClara/multimelt"><em>https://github.com/ClimateClara/multimelt</em></a><em> and a version you can install via pip here: </em><a href="https://github.com/ClimateClara/multimelt"><em>https://pypi.org/project/multimelt/</em></a></p> <p>Finally, if anything is unclear, check out the "Methods" section of the paper: <a href="https://doi.org/10.1029/2023MS003829">https://doi.org/10.1029/2023MS003829</a></p>
Simulation data for the manuscript "Characterizing Optimal Signal Propagation in the Human Brain Network."
<p>See <a href="https://github.com/kuffmode/OI-and-CMs">https://github.com/kuffmode/OI-and-CMs</a></p>
Data for "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes"
<p>This is sampling data of "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes".</p> <p>'qv_nn_in' for the large scale specific humidity, [kg/kg]<br> 'T_nn_in' for the large scale temperature, [K]<br> 'dqvls_nn_in' for the large scale moisture advection, [kg/kg/s]<br> 'dTls_nn_in' for the large scale moisture advection, [K/s]<br> 'qtend_check' for the moistening rate by CRM, [kg/kg/s]<br> 'stend_check' for the heating rate by CRM, [K/s]<br> 'SOLS' for direct shorwave solar radiation down to surface, [W/m2]<br> 'SOLSD' for diffusive shortwave solar radiation down to surface, [W/m2]<br> 'SOLL' for direct near infrared solar radiation down to surface, [W/m2]<br> 'SOLLD' for diffusive near infrared solar radiation down to surface, [W/m2]<br> 'SOLIN' for insolation at model top, [W/m2]<br> 'FSNS' for net shortwave radiation at model surface, [W/m2]<br> 'FSNT' for net shortwave radiation at model top, [W/m2]<br> 'FLNS' for net longwave radiation at model surface, [W/m2]<br> 'FLNT' for net longwave radiation at model top, [W/m2]<br> 'SPPS' for surface pressure, [Pa]</p> <p>To download the full dataset of the SPCAM simulation in 1998. Please click the dropbox link: https://www.dropbox.com/s/p841v1tw00rokdy/SPCAM_VAR_1998.tar.gz?dl=0</p>
Accuracy Study and Improvement of Network Simulation in the SimGrid Framework (datasets)
<p>Data sets and code for https://hal.inria.fr/inria-00361031 and the Rescience replication.</p> <p>The content.org file is the content of the Rescience article from which all the commands for the replication can be found.</p>
Supplementary data for the article "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."
<p>Supplementary figures, tables and raw data for the article " "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."</p> <p><strong>Food sharing can occur in both social and non-social species but is crucial in eusocial species in which only some group members collect food. This food collection but also intranidal food distribution through trophallactic (i.e. mouth-to-mouth) exchanges are fundamental issues in eusocial insects. However, the behavioural rules underlying the regulation and the dynamics of food intake and the resulting networks of exchanges are poorly understood. In this study, we provide new insights </strong><strong>into</strong><strong> the behavioural rules underlying the structure of trophallactic networks and food dissemination dynamics within the colony. We build a simple data-driven model that implements interindividual variability and division of labour to investigate the processes of food accumulation/dissemination inside the nest, both at the individual and collective </strong><strong>levels</strong><strong>. We also test the alternative hypotheses (no variability and no division of labour). Division of labour with inter-individual variability predicts contrary to other models the food dynamics and exchange networks. We establish the links between the interindividual heterogeneity of the trophallactic behaviours, the food flow dynamics and network of trophallactic events</strong>.<strong> Despite the relative simplicity of the model rules, efficient trophallactic networks may emerge as the ones observed in ants leading to better understanding of evolution of such societies.</strong></p> <p> </p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation
<p>Documentation to reproduce in silico analyses related to the manuscript<br> <strong>Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure</strong></p> <p>by</p> <p>Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih</p> <p>Published in<br> Nature Communications (doi: 10.1038/s41467-022-33464-w)</p> <p>All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426). </p> <p>For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366).</p> <p><br> <strong>Simulation results:</strong></p> <p>All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: <em>verticesDict.pkl</em> and <em>edgesDict.pkl</em>containing all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder <em>Baseline</em> contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder <em>Dilated</em> contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders <em>dc_x.x</em> contain the simulation results for the different diameter changes. Note that, folder <em>dc_0.0</em> contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: <em>toDilate</em> and <em>base_capillary</em>). </p> <p> </p> <p><strong>Reproducing figure 8:</strong></p> <p>Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0).<br> Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure.<br> Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below). </p> <p><br> Output: All created Figures are saved in the folder <em>Figures</em>. The associated source data is available in Excel format in the folder <em>SourceData</em>.</p> <p> </p> <p><strong>Edge attributes:</strong></p> <p>diameter: vessel diameter [µm]<br> mainAV: 1 if ascending venule main branch, 0 otherwise<br> connectivity: vertex tuple to define location of edge<br> flow: flow rate [µm<sup>3</sup>/ms]<br> mainDA: 1 if descending arteriole main branch, 0 otherwise<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> htt: tube hematocrit [-]<br> toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise<br> base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise</p> <p> </p> <p><strong>Vertex attributes:</strong></p> <p>index: vertex index<br> pressure: pressure [mmHg]<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> coords: vertex coordinates x,y,z [µm]<br> pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes</p> <p> </p> <p><strong>Obtaining simulation results:</strong><br> General:</p> <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> <p>Specific for current application:</p> <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> <p>Demo:</p> <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (<em>G_averaged.pkl</em>)</li> </ul>
Simulated performance data: MILC, LAMMPS, and uniform random traffic patterns on 72-ndoe dragonfly network
<p>Data generated from an old, private fork of the CODES simulation toolkit: https://github.com/codes-org/codes</p> <p>Data dictionary: https://github.com/kevinabrown/codes/wiki/Dragonfly-Dally-DEBUG-Metrics</p>
Technical Report and Simulation Results for the Paper "It's About Time: On Optimal Virtual Network Embeddings under Temporal Flexibilities"
<p>Archiving the data and the technical report corresponding to our publication "It’s About Time: On Optimal Virtual Network Embeddings under Temporal Flexibilities".<br> The data was previously publicly available at https://net.t-labs.tu-berlin.de/~stefan/tvnep.html.</p>
raw data and simulation scripts for "Exotic states in a simple network of nanoelectromechanical oscillators"
<p>Presented are raw data and simulation python scripts used to create figures from the manuscript "Exotic states in a simple network of nanoelectromechanical oscillators". Data is formatted by {time, mag_1, phase_1, mag_2, phase_2, mag_3, phase_3, mag_4, phase_4, mag_5, phase_5, mag_6, phase_6, mag_7, phase_7, mag_8, phase_8} for 17 column data files, and {time, phase_1, phase_2, phase_3, phase_4, phase_5, phase_6, phase_7, phase_8} for 9 column data. The data is organized by Figure number within manuscript and supplementary information.</p>
Dataset of "Detecting Weaknesses and Analyzing Cascading Failures in Critical Infrastructure Networks Using an Interconnected Simulation System"
<p>This article introduces an innovative simulator for critical infrastructure, developed to enable comprehensive<br>simulation of data and power grid and their interconnections. The simulator offers user-input functionality,<br>allowing for detailed modelling and analysis of various impact scenarios on infrastructures. A key feature<br>of this tool is the use of a state matrix to represent the current state of infrastructures, which is updated<br>after each simulation. This update facilitates accurate and dynamic modelling of changes in infrastructure<br>networks and their interconnections.<br>The simulator is designed to test the functionality of both individual components and the overall network<br>integrity, including the analysis of the cascading failures, where a failure in one part can impact other parts<br>of the infrastructure. This capability is essential for a deeper understanding of risks and for developing<br>effective strategies to protect and ensure the resilience of critical infrastructures. The simulator represents a<br>significant advancement in the field of critical infrastructure simulation, providing a tool for better prediction,<br>identification, and mitigation of potential threats, thereby enhancing the security and resilience of critical<br>systems.</p>
ClimateNet Dataset as used in "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data"
<p>ClimateNet dataset as it was used by us for the study: "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data" (https://gmd.copernicus.org/preprints/gmd-2024-60/).</p> <p> </p> <p>For the original dataset refer to: https://portal.nersc.gov/project/ClimateNet/</p>
Efficient parallelization of tensor network contractions for simulating quantum computation
<p> In this paper, we demonstrate a classical simulation framework for quantum computation by contracting tensor networks of sizes previously deemed out of reach. The main contribution of this work is a parallelization scheme called <em>index slicing</em> that breaks down an infeasibly large tensor network contraction task into smaller subtasks that can be executed fully in parallel, without interdependencies or intermediate communications. As a benchmarking example, we show that our algorithm can reduce the simulation of the Sycamore random circuit sampling task to less than 20 days, achieving an acceleration of over five orders of magnitude compared to the original proposal. We then showcase the capabilities of the simulation framework via investigations of near-term quantum algorithms and quantum error correction. Given the ubiquity of tensor networks in quantum information science, we believe that our simulation framework will be a valuable tool in the era of quantum information technology.</p>
Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks
<p>The processed data and codes in the study are included. </p> <p><strong>Source data:</strong></p> <p>The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder.</p> <ul> <li><em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station</li> <li><em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km).</li> <li><em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre</li> <li><em>The meteorological simulation data</em> comes from the simulation results of the WRF model</li> </ul> <p><strong>Codes:</strong></p> <ul> <li>preprocessing of raw CMAQ data, observed pollution data and meteorological data</li> <li>bulid and train process of fully connected neural networks</li> <li>calculation of correlation between variables</li> <li>feature selection method</li> <li>contribution analysis</li> </ul>
Step-by-step simulation of the ROUTR algorithm on a road-network graph.
<p>This video simulates, step-by-step, the application of the ROUTR algorithm on a small road-network graph.</p>
Simulation of collaboration networks in software development
<p>Dataset resultant from Master Thesis 'Simulation of collaboration networks in software development' authored by José Miguel Gomes.</p>
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics [Dataset]
<p>Supplementary data for 'Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics'. </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.