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308 results for “Dynamic Network”
Opinion dynamics in social network under competition: the role of influencing factors in consensus reaching
<p>The profitability of opinion and the finiteness of individual attention have already spawned the extensive competition for individual preferences on social networks. It's quite necessary to investigate the opinion dynamics over social networks in a competitive environment. To this point, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize the opinion evolution in a competitive opinion dynamics. Based on the DGCG model, we obtain equilibrium results in the stable state of opinion evolution. Consecutively, we analyze what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions and network structure. Theoretical analyses and simulation experiments show that these factors can significantly sway the consensus and even reverse competition outcomes.</p>
Two Dynamic Attributed Networks: Enron & Jazz LastFM
<p><strong>Description. </strong>This repository contains two dynamic and attributed social networks extracted from the well-known Enron email dataset, and from the LastFM online music platform. We used both networks in the following papers:</p> <ol> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “A Method for Characterizing Communities in Dynamic Attributed Complex Networks,” in <em>IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM)</em>, 2014, pp. 481–484. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01011913">hal-01011913</a>⟩ DOI: <a href="http://doi.org/10.1109/ASONAM.2014.6921629">10.1109/ASONAM.2014.6921629</a></li> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “Interpreting communities based on the evolution of a dynamic attributed network,” <em>Social Network Analysis and Mining</em>, vol. 5, p. 20, 2015. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01163778">hal-01163778</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-015-0262-4">10.1007/s13278-015-0262-4</a></li> </ol> <p><strong>Citation. </strong>If you use these data, please cite the paper [1].</p> <p><br><code>@InProceedings{Orman2014,</code><br><code> author = {Orman, Günce Keziban and Labatut, Vincent and Plantevit, Marc and Boulicaut, Jean-François},</code><br><code> title = {A Method for Characterizing Communities in Dynamic Attributed Complex Networks},</code><br><code> booktitle = {IEEE/ACM International Conference on Advances in Social Network Analysis and Mining},</code><br><code> year = {2014},</code><br><code> pages = {481-484},</code><br><code> address = {Beijing, CN},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ASONAM.2014.6921629},</code><br><code>}</code></p> <p>----------------------------------------</p> <p><strong>Enron dataset. </strong>Enron is a well-known dataset in network science and text mining. It has been widely studied in academia. In network science, several different static networks appear in the literature. However, up to now, no dynamic network has been published, even though the email conversations have timestamps.</p> <p>We processed the original dataset to extract a dynamic network. There are 158 nodes representing Enron employees between 1997 and 2002. All the addresses in the <em>From</em> and <em>To</em> fields of each email are considered, resulting in a network of 28,802 nodes representing a distinct email addresses. A time span of one month is chosen for the time slices, generating 46 time slices. Two nodes are connected if the corresponding persons emailed each other during the given time slice. We did not make any distinction between sender and receiver, and thus produced an undirected dynamic network. </p> <p>----------------------------------------</p> <p><strong>LastFM dataset. </strong>LastFM is a music website that allows its members to register and listen to music online. It is also a social network platform, because its members can declare friendship relationships. In LastFM, members can join a predefined group related to their music tastes, and participate in music-related events such as concerts. Using the LastFM API, One can retrieve the information of the artist and track a user has listened to, with the exact timestamp. Moreover, it is also possible to get some information regarding the music-related events the users joined, including the exact timestamps.</p> <p>We extracted a network by focusing on the members of the <em>Jazz</em> group, which is supposed to include users appreciating this type of music. We took advantage of the LastFM API to retrieve the members of this group and the existing friendship connection between them. In the end, our network contains 1,702 nodes representing the <em>Jazz</em> users. The friendship relationships between them is static, though, in the sense that the LastFM API does not give access to any temporal information regarding their beginning or end. So, we decided to take advantage of some additional information to get a dynamic structure. We put a link between two nodes if two conditions were simultaneously true: 1) both considered users listened to at least one common artist for a specific period of time, and 2) they are friends on the LastFM platform. For the mentioned period of time, we decided to use 3 months with 1 month overlap, after having analyzed the dynamics of the platform. In other words, we extracted a dynamic network in which each time slice represents three months of LastFM usage for our 1,702 users of interest. There are one month overlap between two consecutive time slices.</p>
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning
<p>This is the source code and data for the publication "Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning". Preprint - https://arxiv.org/abs/2112.09182</p>
Structure and dynamics of enterovirus genotype networks
<p>Like all biological populations, viral populations exist as networks of genotypes connected through mutation. Mapping the topology of these networks and quantifying population dynamics across them is crucial to understanding how populations adapt to changes in their selective environment. The influence of mutational networks is especially profound in viral populations which rapidly explore their mutational neighborhoods via high mutation rates. Using a novel single-cell sequencing method, scRNAseq-Enabled Acquisition of mRNA and Consensus Haplotypes Linking Individual Genotypes and Host Transcriptomes (SEARCHLIGHT), we captured and assembled viral haplotypes from hundreds of individual infected cells to reveal the complexity of viral populations. We obtained these genotypes in parallel with host cell transcriptome information, enabling us to link host cell transcriptional phenotypes to the genetic structures underlying virus adaptation. Our examination of these structures reveals the common evolutionary dynamics of enterovirus populations and illustrates how viral populations reach through mutational 'tunnels' to span evolutionary landscapes and maintain connection with multiple adaptive genotypes simultaneously.</p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (inputs, outputs, analysis)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt: contains raw data that are used for analysis, also conatin folder for GaMD testing.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. GaMD-testing :</p> <p> 1. Input file of GaMD used to run testing and output gamd.log files for multiple run of σOP 1.2 - 1.4 and σOD 2.5.</p> <p> 4. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant: contains raw data that are used for analysis.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant: contains raw data that are used for analysis.</li> </ul> <p><br> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>03_TT_analysis.tar.gz - TransportTools: contains config file and all the raw data from all set and subset of reclustered (using in-house python script) caver calculations used for running TT.</li> </ul> <p> 1. Caver input data for comparison between 500ns, 1 us, 2.5 us and 5us between LinB-Wt and it’s mutants.<br> 2. TransportTools log file.<br> 3. Main statistics result of comparative analysis.</p> <ul> <li>04_reweighting.tar.gz: directory contains reweighted .csv files after running in-house reweighting protocol.<br> <br> 1. GaMD log files from each simulation of LinB-Wt and it’s mutants.<br> 2. CSV files from TT result folder.<br> 3. Result *.csv file contained reweighted tunnel properties in folder reweighted_filtered_new.</li> <li>05_caverdock.tar.gz: contains raw data for caverdock calculations uisng 100 best tunnels with four ligands 2-bromoethanol (be), 1,2-dibromoethane (dbe), Bromide ion (br-) and water (h2o).</li> </ul> <p> 1. Top 100 tunnels present in tunnel folder for all three tunnels ST, p1b and p3 with subdirectory containing three variants and four ligand, whichare used for running caverdock.<br> 2. Ligand *.pdbqt file and receptor *.pdbqt are present in each 100 tunnel folder of respective caverdock calculation.<br> 3. Inside each variant and each ligand, there is respective result of migration analysis with energy barrier calculation of respective tunnels *energy_barriers-new.log* and further simplied *.csv files that was used for preparing figure in manuscript.</p> <p> </p>
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
<p>Post-processed BOLD fMRI functional connectivity data from human subjects performing two distinct cognitive control tasks.</p> <p>See enclosed README file for information regarding data organization and handling.</p>
Data basis of "Investigation of Railway Network Capacity by Means of Dynamic Flows"
<p>Input data for the article <strong>Investigation of Railway Network Capacity by Means of Dynamic Flows (Nikolayzik, Maus and Nießen).</strong></p> <p>The dataset contains two files for each analysed scenario (complete network, upper subnetwork, lower subnetwork).</p> <p>The first file ("input_data_infrastructure_{scenario}.csv") contains information on the investigated infrastructure:<br>For each station the number of available tracks is listed and for the lines information on whether it is a single- or double-track line, the average minimum headway time, hourly capacity limits and travel times for the different train types are included. The information is thereby split into two parts, depending on whether the core network or the linking lines are described.</p> <p>The second file ("input_data_trains_{scenario}.csv") contains the trains that can generally be scheduled in the considered network, including information on the corresponding train type, departure frequencies, their routes and a minimally allowed dwell time.</p> <p> </p> <p>Further, the file "input_data_route_conflicts_nodes.py" contains the information on which routes inside a station exclude each other as is described in the article.</p> <p> </p> <p> </p>
Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data
<h1>Neural network potentials of an excess proton in bulk water, training data</h1> <p>This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3).</p> <p>The configurations are given as a single XYZ file: configurations.xyz</p> <p>The box dimensions are written in box.txt</p> <p>The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line)</p> <p>The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz</p> <p>The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz</p>
Trade network dynamics and alien plant pest introductions: A global analysis
<p>This is a supplement to "Trade network dynamics and alien plant pest introductions: A global analysis", Diversity and Distributions (<a href="https://doi.org/10.1111/ddi.13963">https://doi.org/10.1111/ddi.13963</a>). These data were partially derived from the following resources available in the public domain: <a href="http://www.cepii.fr/CEPII/fr/welcome.asp">www.cepii.fr/CEPII/fr/welcome.asp</a>; Seebens et al. (2017) (dataset DOI: <a href="https://doi.org/10.12761/sgn.2016.01.022">https://doi.org/10.12761/sgn.2016.01.022</a>); Fenn-Moltu et al. 2022 (dataset DOI: <a href="https://doi.org/10.5061/dryad.8931zcrrq">https://doi.org/10.5061/dryad.8931zcrrq</a>).</p>
Data from: Personality and social network structure influence cooperative dynamics across canid species
<p>In canids, cooperative behaviour occurs in many scenarios. However, most studies focus on single-species observations, not accounting for variation beyond the species-level. We modelled cooperative behaviour using Eigenvalue centrality as well as boldness combined with biological traits such as kinship, sex, age, mating system and foraging strategy in multiple canid species with Bayesian inference, Tukey HSD and distance correlation.</p>
Optimal Dynamic Service Restoration of Distribution Networks Considering Energy Storage System Data
<p>The power distribution system presented is composed with 53 node and 61 branches and can be employed in multi-time service restoration problem, islading operation and energy storage system optimal operation. The system was designed based on a 53 node system (available <a href="https://ieee-dataport.org/documents/optimal-service-restoration-active-distribution-networks-considering-microgrid-formation">here</a>). The dataset was modified to include 6 photovoltaic generation, 3 energy storage system and time-changing demand load nodes.</p>
Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model
<p>Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models, which simulate material (carbon) transfer in trophic networks from producers to consumers based on mass-specific metabolic rates, provide an attractive framework to study consumer-resource interactions from organisms to ecosystems. However, the developed ATN models rarely consider temporal changes in some key abiotic drivers that affect e.g. consumer metabolism and producer growth. Here, we evaluate how temporal changes in carrying capacity and light-dependent growth rate of producers and in temperature-dependent mass-specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity and standing stock biomass of different trophic guilds, including age-structured fish communities. Our simulations of the pelagic Lake Constance (LC) food web indicated marked effects of temporally changing abiotic parameters on seasonal biomass accumulation of different guild groups, particularly among the lowest trophic levels (primary producers and invertebrates). While the adjustment of average irradiance had a minor effect, increasing metabolic rate associated with 1–2˚C temperature increase led to a marked decline of larval (0-year age) fish biomass, but to a substantial biomass increase of 2- and 3-year-old fish that were not predated by ≥4-year-old top predator fish, European perch. However, when averaged across the 100 simulation years, the inclusion of seasonality in abiotic drivers caused only minor changes in standing stock biomasses and productivity of different trophic guilds. Our results demonstrate the potential of introducing seasonality in and adjusting the average values of abiotic ATN model parameters to simulate temporal fluctuations in food-web dynamics, which is an important step in ATN model development aiming to e.g. assess potential future community-level responses to ongoing environmental changes.</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>
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>
Data from: Urbanization alters the spatiotemporal dynamics of plant-pollinator networks in a tropical megacity
<p><span>Urbanization is a major driver of biodiversity change but how it interacts with spatial and temporal gradients to influence the dynamics of plant-pollinator networks is poorly understood, especially in tropical urbanization hotspots. Here, we analyzed the drivers of environmental, spatial, and temporal turnover of plant-pollinator interactions (interaction β-diversity) along an urbanization gradient in Bengaluru, a South Indian megacity. The compositional turnover of plant-pollinator interactions differed more between seasons and with local urbanization intensity than with spatial distance, suggesting that seasonality and environmental filtering were more important than dispersal limitation for explaining plant-pollinator interaction β-diversity. Furthermore, urbanization amplified the seasonal dynamics of plant-pollinator interactions, with stronger temporal turnover in urban compared to rural sites, driven by greater turnover of native non-crop plant species (not managed by people). Our study demonstrates that environmental, spatial, and temporal gradients interact to shape the dynamics of plant-pollinator networks and urbanization can strongly amplify these dynamics. </span></p>
The cacao gene atlas: A transcriptome developmental atlas reveals highly tissue-specific and dynamically-regulated gene networks in Theobroma cacao L
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Data from: Urbanization alters the spatiotemporal dynamics of plant-pollinator networks in a tropical megacity
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Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients
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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.