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59 results for “graph neural networks”

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zenodo48/100

HEroBM: a deep equivariant graph neural network for high-fidelity backmapping from coarse-grained to all-atom structures

Molecular simulations play a pivotal role in chemistry, biology, and material sciences, enabling the study of complex dynamic properties within systems. Coarse-grained (CG) techniques have emerged as indispensable tools in this domain, facilitating the sampling of large-scale systems and extending simulation timescales by simplifying system representation. However, CG approaches involve a trade- off: they sacrifice atomistic details that may be crucial for understanding the underlying processes. To address this challenge, a recommended strategy is to identify key CG conformations and employ backmapping methods to retrieve atomistic coordinates. Currently, rule-based methods often yield suboptimal geometries and rely on energy relaxation, resulting in less-than-optimal outcomes. In contrast, machine learning techniques offer higher accuracy but may lack transferability between systems or be tied to specific CG mappings. In this study, we present HEroBM, a dynamic and scalable method that utilizes deep equivariant graph neural networks and a hierarchical approach to achieve high-resolution backmapping. HEroBM is capable of handling any type of CG mapping, providing a versatile and efficient protocol for reconstructing atomistic structures with high accuracy. Grounded in local principles, HEroBM spans the entire chemical space and can be applied across systems of varying composition and sizes. We demonstrate the versatility of our framework through a range of biological systems, including a complex real-case scenario. Here, our end-to-end backmapping approach accurately generates atomistic coordinates for a G protein-coupled receptor bound to an organic small molecule within a cholesterol/phospholipid bilayer. The high-fidelity HEroBM backmapping enables researchers to effortlessly transition between CG and all-atom simulations, opening unprecedented avenues for molecular investigations.

opencc-by-4.0Jun 2024View details →

zenodo48/100

SCG Dataset from Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

Abstract: Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

opencc-by-4.0Sep 2024View details →

zenodo48/100

Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Advection datasets from the paper:     Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679) The datasets are:   - AdvBox   - AdvInBox   - AdvTaylor   - AdvCircle   - AdvCircleAng   - AdvSquare   - AdvEllipseH   - AdvEllipseV   - AdvSpline   - AdvSquareAndCircle   - Adv3Circles 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.   To cite these datasets, use the following reference: 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). <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>

opencc-by-4.0Apr 2023View details →

zenodo44/100

scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data

This repository contains the training data and source code to reproduce the results of our paper: scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data More description can be also found in GitHub (https://github.com/LPH-BIG/scGraph2Vec).

opencc-zeroJun 2024View details →

zenodo44/100

Deep learning models predicting gene functions and pathways using public DRKG knowledge graph and graph neural network

The attached dataset contains pretrained link prediction models, as described in our paper 'Morphological Map of Under- and Over-Expression of Genes in Human Cells'.

opencc-by-4.0Nov 2024View details →

zenodo44/100

CNN Wild Park - Graph Neural Networks for Learning Equivariant Representations of Neural Networks

This repository contains the CNN Wild Park dataset from the paper: <blockquote> Graph Neural Networks for Learning Equivariant Representations of Neural Networks <a href="https://mkofinas.github.io/">Miltiadis Kofinas</a>*, <a href="https://bknyaz.github.io/">Boris Knyazev</a>, <a href="https://www.cyanogenoid.com/">Yan Zhang</a>, <a href="https://yunlu-chen.github.io/">Yunlu Chen</a>, <a href="https://gertjanburghouts.github.io/">Gertjan J. Burghouts</a>, <a href="https://egavves.com/">Efstratios Gavves</a>, <a href="https://www.ceessnoek.info/">Cees G. M. Snoek</a>, <a href="https://davzha.netlify.app/">David W. Zhang</a>* ICLR 2024 (oral) <a href="https://arxiv.org/abs/2403.12143">https://arxiv.org/abs/2403.12143</a> <a href="https://github.com/mkofinas/neural-graphs">https://github.com/mkofinas/neural-graphs</a> *Joint first and last authors </blockquote> We introduce a new dataset of CNNs, which we term CNN Wild Park. The dataset consists of 117,241 checkpoints from 2,800 CNNs, trained for up to 1,000 epochs on CIFAR10. The CNNs vary in the number of layers, kernel sizes, activation functions, and residual connections between arbitrary layers. More specifically, we construct the CNN Wild Park dataset by training 2,800 small CNNs with different architectures for 200 to 1,000 epochs on CIFAR10. We retain a checkpoint of its parameters every 10 steps and also record the test accuracy. The CNNs vary by: <ul> <li>Number of layers L in [2, 3, 4, 5] (note that this does not count the input layer).</li> <li>Number of channels per layer c_l in [4, 8, 16, 32].</li> <li>Kernel size of each convolution k_l in [3, 5, 7].</li> <li>Activation functions at each layer are one of ReLU, GeLU, tanh, sigmoid, leaky ReLU, or the identity function.</li> <li>Skip connections between two layers with at least one layer in between. Each layer can have at most one incoming skip connection. We allow for skip connections even in the case when the number of channels differ, to increase the variety of architectures and ensure independence between different architectural choices. We enable this by adding the skip connection only to the min(c_n, c_m) nodes.</li> </ul> We divide the dataset into train/val/test splits such that checkpoints from the same run are not contained in both the train and test splits.  <div> </div> <div> </div>

opencc-by-4.0May 2024View details →

zenodo44/100

Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"

Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks" URL: https://github.com/qianghuangwhu/benchtemp Openreview: https://openreview.net/forum?id=rnZm2vQq31 There are 19 (15+4)  benchmark temporal graph datasets: reddit, wikipedia, mooc, lastfm, enron, SocialEvo, uci, CollegeMsg, TaobaoSmall, CanParl, Contacts, Flights, UNtrade, USLegis, UNvote, DGraphFin, TaobaoLarge, YoutubeReddit, YoutubeRedditLarge   Each dataset has three files: 1. ml_{data_name}.csv - the csv file of the Temporal Graph. This file have five columns with properties: 'u': the id of the user. 'i': the id of the item. 'ts': the timestamp of the interaction (edge) between the user and the item. 'label': the label of the interaction (edge). 'idx': the index of the interaction (edge). For example: ,u,i,ts,label,idx 0,1,2,0.0,0.0,1 1,1,3,0.0,0.0,2 2,1,4,0.0,0.0,3 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph.. 3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.

opencc-by-4.0Aug 2023View details →

zenodo40/100

Dataset for monkeys A and B from AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity

ECoG data from two monkeys, affi (A) and beignet (B) used in AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity. Jingyuan Li, Leo Scholl, Trung Le, Pavithra Rajeswaran, Amy L Orsborn, and Eli Shlizerman. NeurIPS. 2023. https://openreview.net/forum?id=7ntI4kcoqGSee also https://github.com/shlizee/AMAG

opencc-by-4.0Nov 2023View details →

zenodo40/100

Data From: Emulation of Cardiac Mechanics using Graph Neural Networks

Contains simulation results of the forward displacement from beginning to end-diastole for approximately 3000 synthetically generated left ventricle geometries. The simulation results are split into training, validation and test data. The data is described in detail in a forthcoming publication in Computer Methods in Applied Mechanics and Engineering - further information will be provided upon publication. A GitHub repository will also be made available, with code for processing the simulation data and training a Graph Neural Network emulator.

opencc-by-4.0Sep 2022View details →

zenodo40/100

Conformer datasets for "Equivariant Graph Neural Networks for Toxicity Prediction"

Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain. PAPER https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.3c00032 CODE and MODELS: The conformer data sets and trained toxicity models will be published upon acceptance of this work. The code has been made available at <a href="https://github.com/jule-c/ET-Tox">https://github.com/jule-c/ET-Tox</a>, and the processed data as well as pretrained models for training and testing can be downloaded from <a href="../record/7942946">https://zenodo.org/record/7942946</a>. We can provide the full list of conformers as XYZ files upon request.

opencc-by-4.0May 2024View details →

zenodo40/100

Training data set for: Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries

<h2>Overview</h2> This is a synthetic volcano deformation dataset accompanying the publication of Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries, on the journal Volcanica. Synthetic, quasi-static deformation is computed for magma chambers of various geometries, parameterized as spheroids or superpositions of spherical harmonics. Surface deformation is computed using the boundary element method (BEM) of Nikkhoo & Walter (2015). Please reference our paper for details of computational methods. The dataset contains 50,000 realizations of magma chamber geometries/orientations/centroid depths and associated deformation fields. Surface deformation fields are sampled at discrete locations, with a uniform random distribution within [Lh x Lh], and a distribution that concentrates near the chamber (at radial distances, r = 10^(-3  random number) * Lh/2). Note this dataset contains only a small fraction of the total dataset. In total, 824,393 realizations of magma chambers were used to train our emulators. For accessing the complete training data set, please contact the authors.  Each .mat file contains the deformation field associated with a single chamber geometry. Use visData.m to visualize chamber geometry and associated surface displacement. Each file contains two MATLAB structures, "input" and "output".  <h2>Naming of each zip file</h2> The numbers after the underscore, N:M, indicate that this file contains N of the M total chamber realizations for this particular setup.  <a href="../api/records/13800065/draft/files/sph_20AspRatios_1e4:151211.zip.zip/content" target="_blank" rel="noopener noreferrer">sph_20AspRatios_1e4:151211.zip</a>: deformation corresponding to spheroidal magma chambers parameterized by aspect ratios.  <a href="../api/records/13800065/draft/files/sh_complex_1e4:152283.zip/content" target="_blank" rel="noopener noreferrer">sh_complex_1e4:152283.zip</a>: deformation corresponding to chamber geometry produced by superposition of spherical harmonic modes.  <a href="../api/records/13800065/draft/files/sh_mode_approx_1e4:138380.zip/content" target="_blank" rel="noopener noreferrer">sh_mode_approx_1e4:138380.zip</a>: deformation corresponding to chamber geometries corresponding to individual spherical harmonic modes, combined with a spherical mode (the spherical mode prevents chamber surfaces from having zero radii locally) <a href="../api/records/13800065/draft/files/sh_spheroid_approx1e4:202272.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_approx1e4:202272.zip</a>: deformation corresponding to chambers approximating spheroids, but parameterized by spherical harmonics. <a href="../api/records/13800065/draft/files/sh_spheroid_perturb_1e4:180247.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_perturb_1e4:180247.zip</a>: same as above, but with additional random perturbations parameterized in spherical harmonics. <h2>Variables in each file</h2> Input contains the following fields: dp2mu: pressure change to shear modulus ratio. dx, dy, dz: the coordinates of chamber centroid [meters] mu: dimensionless crustal shear modulus (always set to 1) nu: crustal Poisson's ratio (always set to 0.25) Ns: number of points on the surface where displacements are computed Lh, Lv: horizontal and vertical dimensions of the model domain [meters]. Lh is determined such that at the edge of the model domain, the displacement magnitude is below 10 percent of the maximum. Lv = Lh/2 + abs(dz) for the spheroids ----------------------------------------------------------------------------------------------------------- the input files contain asp: aspect ratio of chamber (length of the semi-major axis divided by that of the semi-minor axis) ra, rb: semi-major, -minor, axis length [meters] thetax, thetay, thetaz: counterclockwise rotation angles with regard to x, y, z axis [degrees]. thetax = [0, 90] degrees, thetay = 0 degrees, thetaz = 360 degrees. for the general geometries-------------------------------------------------------------------------------------------------- the input files contain ls, ms, fs: degree, order, coefficients of spherical harmonic modes. Spherical harmonics are sampled up to degree 5. fs is a complex vector of coefficients such that the resulting shape is real.  normF: normalization factor applied to the shape parameterized by ls, ms, fs, such that the shape as a maximum radius of unity. rmax: scale factor to scale the spherical harmonics parameterized shape to real dimensions [meters]. ============================================================================================= Output contains the following fields, X, Y, Z: coordinates of points where displacement vectors are computed [meters] Ux, Uy, Uz: displacements in x, y, z directions [meters] P, T: coordinates [meters] of vertices for the triangular mesh used in BEM calculation, and the connectivity matrix  C: coordinates [meters] of the center of each triangular element that, dhat, nhat: unit vectors for orthogonal coordinate systems local to each triangular element. that ("t-hat") extends from vertex one to vertex two, nhat is outward normal, and dhat = cross (nhat, that). Reference: 1. Nikkhoo, M., & Walter, T. R. (2015). Triangular dislocation: an analytical, artefact-free solution. Geophysical Journal International, 201(2), 1119-1141.

opencc-by-4.0Sep 2024View details →

zenodo40/100

Data and models for: Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks

Data (ver 1.1) and trained models for our paper "<a href="https://arxiv.org/abs/2409.13851">Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks</a>". If you use such data or models, please cite our paper. These three directories need to be downloaded and copied into our source codes in order to reproduce our paper: <a href="https://github.com/learningmatter-mit/PerovskiteOrderingGCNNs">https://github.com/learningmatter-mit/PerovskiteOrderingGCNNs</a> <ul> <li>data: All data files for training and evaluating GCNNs, with a copy archived on the Materials Data Facility (<a href="https://doi.org/10.18126/ncqt-rh18">DOI: 10.18126/ncqt-rh18</a>)</li> <li>saved_models: All saved model files for evaluating GCNNs</li> <li>best_models: All best model files for evaluating GCNNs</li> </ul>

opencc-by-4.0Sep 2024View details →

zenodo40/100

WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks

Dataset release for our IEEE Symposium on Security and Privacy 2022 paper entitled "WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks" These files are pretty self-explanatory: there are node/edge features of a specified (full graph or random 5K graph). To use these files, make sure to check out GitHub <a href="https://github.com/jun521ju/IEEE_SP_2022_WtaGraph">here</a>. Basically, you need put each of these files in a correct folder as detailed on GitHub.

opencc-by-4.0Aug 2021View details →

zenodo40/100

Visual Genome - Visual Relationship Detection - Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks

This repository contains a processed version of Visual Genome for Visual Relationship Detection, from the Diploma (MSc) thesis Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks by Miltiadis Kofinas, supervised by Christos Diou and Anastasios Delopoulos. The original thesis is written in Greek <blockquote> Νευρωνικά Δίκτυα Ανταλλαγή&sigmaf; Μηνυμάτων και Συνελικτικά Δίκτυα Γράφων για Εξαγωγή Γράφου Σκηνή&sigmaf; Εικόνων Μιλτιάδη&sigmaf; Κοφινά&sigmaf; <a href="https://ikee.lib.auth.gr/record/300900">https://ikee.lib.auth.gr/record/300900</a> </blockquote> A summarized English version of the thesis can be accessed <a href="https://www.dropbox.com/s/m87ixw8c8ecrswm/mkofinas_thesis_english_scene_graph_generation.pdf?dl=0">here</a>. It contains region proposals for VGG-16 for all images, and metadata about the bounding box distribution and the predicate classes.

opencc-by-4.0Jan 2018View details →

zenodo40/100

NsCircle datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

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:     Multi-scale rotation-equivariant graph neural networks for     unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679) The datasets are:   - train/NsEllipse   - test/NsEllipseLowRe   - test/NsEllipseHighRe   - test/NsEllipseThin   - test/NsEllipseThick   - test/NsEllipseNarrow   - test/NsEllipseWide   - test/NsEllipseAoA     To cite these datasets, use the following reference: 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). @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}, }

opencc-by-4.0Apr 2023View details →

zenodo40/100

NsEllipse datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Datasets with simulations of the incompressible flow around an elliptical cylinder as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:     "Multi-scale rotation-equivariant graph neural networks for     unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679) The datasets are:   - train/NsEllipse   - test/NsEllipseLowRe   - test/NsEllipseHighRe   - test/NsEllipseThin   - test/NsEllipseThick   - test/NsEllipseNarrow   - test/NsEllipseWide   - test/NsEllipseAoA   To cite these datasets, use the following reference: 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). @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}, }

opencc-by-4.0May 2023View details →

zenodo40/100

Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

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). The training code is available at https://github.com/mario-linov/graphs4cfd.

opencc-by-4.0May 2023View details →

zenodo40/100

Supplementary dataset for "Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking"

The supplementary dataset for the paper "Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking". We include the splits for cora, citeseer, and pubmed, the hard negative samples, and the node2vec embeddings. We also include a jupyter file read_data.ipynb to show how to read the non-txt file. <ul> <li>heart_test_samples.npy, heart_valid_samples.npy: the heard negative samples</li> <li>*-n2v-embedding.pt: node2vec embeddings</li> <li>test_samples_index.pt, valid_samples_index.pt: the node index of the selected samples in ogbl-ppa under HeaRT</li> <li>gnn_feature: the input feature of cora, citeseer, pubmed</li> </ul> More details for our code and how to use the dataset are on the code repository: https://github.com/Juanhui28/HeaRT .

opencc-by-4.0Aug 2023View details →

zenodo40/100

Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"

opencc-by-4.0Aug 2023View details →

dryad40/100

Data from: Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations

Open the record for dataset details and reuse information.

publicJun 2025View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record