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102 results for “Network simulation”
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>
Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data
<p>Files uploaded here are related to the paper titled "Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data". Here we investigate how generative adversarial networks can be used to match simulated- and experimental INS data.</p>
Shifts from non-obligate generalists to obligate specialists in simulations of mutualistic network assembly
<p>Understanding ecosystem recovery after perturbation is crucial for ecosystem conservation. Mutualisms contribute key functions for plants such as pollination and seed dispersal. We modelled the assembly of mutualistic networks based on trait matching between plants and their animal partners that have different degrees of specialization on plant traits. Additionally, we addressed the role of non-obligate animal mutualists, including facultative mutualists or non-resident species that have their main resources outside the target site. Our computer simulations show that non-obligate animals facilitate network assembly during the early stages, furthering colonization by an increase in niche space and reduced competition. While non-obligate and generalist animals provide most of the fitness benefits to plants in the early stages of the assembly, obligate and specialist animals dominate at the end of the assembly. Our results thus demonstrate the combined occurrence of shifts from diet, trait, and habitat generalists to more specialised animals.</p>
Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques
<p>The pathways through which primates acquire skills are a central focus of cultural evolution studies. The roles of social and genetic inheritance processes in skill acquisition are often confounded by environmental factors. Hybrid macaques from Koram Island, Thailand provide an opportunity to examine the roles of inheritance and social learning to skill acquisition within a single ecological setting. These hybrids are a cross between tool-using Burmese long-tailed (<em>Macaca</em> <em>fascicularis</em> <em>aurea</em>) and non-tool-using common long-tailed macaques (<em>Macaca</em> <em>fascicularis</em> <em>fascicularis</em>). This population provides an opportunity to explore the roles of social learning and inheritance processes while being able to exclude underlying ecological factors. Here, we investigate the roles of social learning and inheritance in tool use prevalence within this population using social network analysis and simulation. Agent-based modeling (ABM) is used to generate expectations for how social/asocial learning and inheritance structure the patterning in a social network. The results of the simulation show that various transmission mechanisms can be differentiated based on associations between individuals in a social network. The results provide an investigative framework for discussing tool-use transmission pathways in the Koram social network. By combining ABM, network analysis, and behavioral data from the field we can investigate the roles social learning and inheritance play in tool acquisition in wild primates. </p>
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
<p>We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages, or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example.</p> <p>We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at <a href="https://github.com/cecileane/PhyloCoalSimulations.jl">https://github.com/cecileane/PhyloCoalSimulations.jl</a>.</p>
Biochemical networks with simulation-based estimations of dynamical properties
<p>This datasets collection was first introduced in the article: </p> <p><a href="https://academic.oup.com/bioinformatics/article/39/11/btad678/7407341" target="_blank" rel="noopener">Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs</a>. (Bioinformatics 2023)</p> <p> </p> <p>The collection contains three datasets that contain information about three dynamical properties computed on a set of 483 biochemical pathways downloaded from the BioModels database. The three dynamical properties are:</p> <ul> <li>robustness</li> <li>sensitivity</li> <li>monotonicity</li> </ul> <p>The files are organized as follows:</p> <ol> <li>The `pathways` directory contains 483 files in .dot format for each biochemical pathway downloaded from the BioModels database (May 2021), represented in Petri net format (see <a href="https://doi.org/10.5220/0008964700320043">this article</a> for the exact definition). The file name is the ID of the pathway in the BioModels database.</li> <li>The other folders contain one .csv file for each property. A single .csv file contains 4 columns: <ol> <li>`PathwayID`: the ID of the Pathway in the BioModels database</li> <li>`Input`: the input molecular species on which the property has been assessed</li> <li>`Output`: the output molecular species on which the property has been assessed</li> <li>`Property`: the value of the property assessed with numerical simulations on the pathway for that particular input/output species pair.</li> </ol> </li> <li>The `loader.py` file is an optional script that allows to use the data in python. The script requires that the libraries `networkx`, `pandas`, and `pydot` are installed in the target machine.</li> </ol>
ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments
<p>We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms.<br> <br> To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>.<br> <strong>Please read the license file (available in the GitHub repository) before using the database.</strong></p>
UKESM1.0-ice simulation output used as test data in Burgard et al., Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks
<p>These files contain NEMO ocean model output and domain definitions for the Southern Ocean from UKESM1.0-ice simulations described in section 6.3.2 of Smith et al. "Coupling the U.K. Earth System Model to Dynamic Models of the Greenland and Antarctic Ice Sheets" , Journal of Advances in Modeling Earth Systems, 2021</p> <p>Files labelled "bf663" are the UKESM simulation referred to in that section as "constant 1970 greenhouse gas and other forcings". Files labelled bi646 are the UKESM simulation referred to in that section as "instantaneously quadrupled 1970 CO<sub>2</sub> concentrations".</p> <p>They were used as test data for the performance of neural networks in Burgard et al., "Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks", Journal of Advances in Modeling Earth Systems 2023.</p>
Pretrained models and simulated data for MICCAI paper Unsupervised Domain Transfer with Conditional Invertible Neural Networks
<p>Simulated data and the pretrained models used for the publication "Unsupervised Domain Transfer with Conditional Invertible Neural Networks", see https://link.springer.com/chapter/10.1007/978-3-031-43907-0_73 published at MICCAI 2023.</p>
Required data for simulating a typical large-scale urban traffic network
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Simulation of ripple oscillations in a large interneuron network under different levels of constant external drive
<p>Example simulation to be used with code on GitHub repository: https://github.com/NatalieSchieferstein/interneuron_ripples_with_ifa.git .</p><p>Simulation data was generated using pypet (pypet.readthedocs.io/) and Brian2.</p><p>Code and simulation data are Supplement to publication: 10.1101/2023.01.30.526209 .</p><p> </p>
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
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Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques
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Shifts from non-obligate generalists to obligate specialists in simulations of mutualistic network assembly
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Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
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Simulations of gene regulatory networks with transcriptional adaptation
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Simulated trajectories of city population: Levy walks along Buffalo, NY, street network
<p>Simulated trajectories of the full 2000 census population of Buffalo, New York. Each simulated person was placed randomly along a road within his or her census tract of residence and then independently performed road-network-constrained, truncated Lévy walks for 8 hours of simulated time, moving at 4 km per hour. The trajectories were then sampled at 30 minute intervals. The original dataset was created for studying indexes of activity-space segregation. It is archived at <a href="https://zenodo.org/record/2865830#.XpYviKsza00">https://zenodo.org/record/2865830#.XpYviKsza00</a> and described more fully in Palmer (2013), Activity-Space Segregation: Understanding Social Divisions in Space and Time (http://arks.princeton.edu/ark:/88435/dsp01k643b130h).</p> <p>This version has been created to aid in testing contact-tracing apps and mobility analysis tools. The fields are:</p> <p>latitude: latitude</p> <p>longitude: longitude</p> <p>time: UNIX time in milliseconds</p> <p>ID: random ID assigned to each individual</p>
Simulated trajectories of city population: Levy walks along Utica, NY, street network
<p>Simulated trajectories of the 2000 census population of Utica, New York. Each of 53,971 simulated people was placed randomly along a road within his or her census tract of residence and then independently performed road-network-constrained, truncated Lévy walks for 8 hours of simulated time, moving at 4 km per hour. The trajectories were then sampled at 30 minute intervals. The original dataset was created for studying indexes of activity-space segregation. It is archived at <a href="https://zenodo.org/record/2865830#.XpYviKsza00">https://zenodo.org/record/2865830#.XpYviKsza00</a> and described more fully in Palmer (2013), Activity-Space Segregation: Understanding Social Divisions in Space and Time (http://arks.princeton.edu/ark:/88435/dsp01k643b130h).</p> <p>This version has been created to aid in testing contact-tracing apps and mobility analysis tools. The fields are:</p> <p>latitude: latitude</p> <p>longitude: longitude</p> <p>time: UNIX time in milliseconds</p> <p>ID: random ID assigned to each individual</p>
Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models
<p>This dataset contains the .npy (numpy) files of the simulation models and reference discussed in the paper "Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models".</p> <p>The folders contain all simulation models and reference maps already divided in subregions. Each .npy file is a numpy 2D array with delta IP or delta Sw values. The csv files contain the 3-tuples and the selected model in each.</p> <p>There are two csv files: the first is the dataset used for training the CNN, with 1280 labeled tuples evaluated by a single specialist. The second is the ground-truth, with 164 tuples evaluated by three specialists (in which 2 or more agreed on the selected model), used for validating the models and comparing different approaches.</p> <p>We also provide a Python code to read and visualize the .npy files.</p>
Simulation results of routing algorithms for multilayer networks
<p>Most current routing protocols are based on path computation algorithms in graphs (e.g., Dijkstra, Bellman-Ford, etc.). These algorithms have been studied for a long time and are very well understood, both in a centralized and distributed context, as long as they are applied to a network having a single communication protocol. The problem becomes more complex in the multi-protocol case, where there is a possibility of encapsulation of some network protocols into others, therefore inducing nested tunnels. The classic algorithms cited above no longer work in this case because they cannot manage the protocol encapsulations and the corresponding protocol stacks. In this work, we propose a highly parallelizable algorithm that takes into account protocol encapsulations as well as protocol conversions in order to compute shortest paths in a multi-protocol network. To achieve this computation efficiently, we study the transitive closure between subpaths (i.e., the concatenation of two subpaths to obtain a longer one) in the case where each subpath induces a protocol stack, and thus tunnels. Leveraging on Software-Defined Networks with a controller having a highly parallel architecture enables us to compute the routing tables of all nodes in a very efficient way. Experimentation results on both random and realistic topologies show that our algorithm outperforms the previous solutions proposed in the literature.</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.