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250 results for “Synthetic Dataset”
Gaussian synthetic cluster datasets
<p>A collection of 20 structurally diverse synthetic datasets that consist of randomly generated gaussian distributions varying in number of objects (5000 or 10000), number of features (20,40,50,60), number of clusters (3,8,15,20), cluster sizes, cluster standard deviations, cluster overlap, and cluster anisotropy. Can be used to test clustering methods.</p>
Molecular Dynamics Simulation Dataset for "Hydrophobic Mismatch Drives Self-Organization of Designer Proteins into Synthetic Membranes"
<p>This repository contains molecular dynamics (MD) simulation data from the study on the self-organization of designer proteins in synthetic membranes. The data includes simulations for different single lipid compositions (DOPC, DPPC, DYPC) denoted as [lipid]-PL* where PL stands for the different TMD constructs. Multi component simulation are named accordingly. The repository provides initial (eqi.gro) and final (prod.gro) coordinates for each simulation. The 'cmd' file in each directory outlines the assembly process of each simulation, and the 'mdp' folder contains all input files for the simulations. </p>
Synthetic IMGUR5k dataset with latent diffusion models
<p>SyntheticHTR: Handwritten Text Image Synthesis based on Latent Diffusion Models</p>
Dataset for "Three-dimensional spin-wave dynamics, localization and interference in a synthetic antiferromagnet"
<p>Data availability for the article titled "Three-dimensional spin-wave dynamics, localization and interference <br>in a synthetic antiferromagnet" by Girardi et al. published in Nature Communications.</p> <p>This data repository contains the following folders and files:<br>• TR Lamni Raw Data - Co: Files with extension .hdf5 are Time-Resolved Soft X-Ray Laminography Raw Data for all projections acquired to obtain the 3D reconstruction for the CoFeB layer. The projections’ data have been organized in sub-folders for the 7 frames acquired during the measurements. For each frame, a final folder “analysis” contains an aligned_projection.mat file with the angles of all measured projections and the sinogram information.<br>• TR Lamni Raw Data - Ni: Files with extension .hdf5 are Time-Resolved Soft X-Ray Laminography Raw Data for all projections acquired to obtain the 3D reconstruction for the NiFe layer. The projections’ data have been organized in sub-folders for the 7 frames acquired during the measurements. For each frame, a final folder “analysis” contains an aligned_projection.mat file with the angles of all measured projections and the sinogram information.<br>• Magnetic reconstruction – Ni edge: contains the .mat files of the 3D magnetic reconstruction obtained for Ni-edge for all 7 frames. In each file, the Mx, My and Mz components of the magnetization associated with the 3D reconstruction of the volume of the sample are present. <br>• Magnetic reconstruction – Co edge: contains the .mat files of the 3D magnetic reconstruction obtained for Co-edge for all 7 frames. In each file, the Mx, My and Mz components of the magnetization associated with the 3D reconstruction of the volume of the sample are present. <br>• vtk files: contains the post-processed files for all 7 frames in the .vtk format for the 3D visualization with the Paraview software. In each file, both vectorial and scalar data associated with the 3D reconstruction of the volume of the sample and analyzed in the paper are present.<br>• Mumax3 simulation dispersion relation: contains .ovf files (containing information about the Mx, My, Mz components of the magnetization as a function of the spatial position) obtained from the mumax3 simulation to study the spin wave dispersion relation of the sample investigated. The total simulation runtime is 10 ns, and each .ovf file corresponds to a frame saved every 0.01 ns.<br>• Mumax3 simulation 3D interference: contains the .ovf files (containing information about the Mx, My, Mz components of the magnetization as a function of the spatial position) for the static magnetization and 3 frames used to simulate the 3D interference pattern observed experimentally. </p>
Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain
<p>This dataset gathers synthetic-yet-highly-realistic T2-weighted magnetic resonance images (MRI) of the fetal brain based on the latest development of our prototype Fetal Brain magnetic resonance Acquisition Numerical phantom that now simulates local heterogeneities within white matter tissues throughout maturation (FaBiAN v2.0).<br>This dataset is associated with the following paper:</p> <p><strong>- Lajous H. et al. (2024) A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain.</strong> Submitted to Nature Scientific Data, Pre-print available https://doi.org/10.1101/2024.04.08.588566</p> <p>We propose this unique, extensive fetal MRI dataset of simulated standard clinical fast spin echo sequences in both healthy and pathological neurodevelopmental trajectories to address data scarcity in this sensitive population, and therefore support the continuous endeavor of the community to develop advanced post-processing methods as well as cutting-edge artificial intelligence models. Automated brain tissue annotations of the two-dimensional, low-resolution series as well as super-resolution (SR) reconstructions of the fetal brain volumes are also included.</p> <p><strong>Work using any of these data should cite the following references:</strong></p> <ul> <li>Lajous, H. et al. A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data (2024), https://doi.org/10.1101/2024.04.08.588566</li> <li>Lajous, H. et al. Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Zenodo (2024). 10.5281/zenodo.10940427</li> <li>Lajous, H., le Boeuf Fló, A., Esteban, O. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v2.0 (2.0). Zenodo (2023), 10.5281/zenodo.5471094</li> </ul> <p>This work was supported by the Swiss National Science Foundation through grant 182602, and by the ProTechno Foundation. We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).</p> <p>Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2024.</p> <p>Note: <em>Terms of use for the original cohort (</em>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI, 2021<em>) are for research and education purposes only.</em></p>
MR3D - Synthetic Resistivity Well Logs Dataset
<p>Marlim R3D (MR3D) is an open-source realistic geoelectric model for CSEM simulations of the post-salt turbiditic reservoirs at the Brazilian offshore margin.</p> <p><br>Here, we make available a set of 27 synthetic resistivity well logs that complement the MR3D model (available at https://zenodo.org/badge/DOI/10.5281/zenodo.400233.svg). These logs include fine-scale variations summed to the low-frequency resistivity property extracted from the MR3D model, making them suitable for educational and research purposes. Under the Creative Commons License, these elements are freely available for research or commercial use.</p> <p>The logs are provided in LAS format</p>
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)
<p><strong>This page only provides the </strong><strong>drone-view image</strong><strong> dataset. </strong></p> <ul> <li><strong>For the ground-level image dataset, please visit <a href="https://zenodo.org/records/13570934" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)</em></a>.</strong></li> <li><strong>For the point clouds, please visit <em><a href="https://zenodo.org/records/14228467" target="_blank" rel="noopener">SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)</a>.</em></strong></li> </ul> <p>The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a <code>color_palette.xlsx</code> file, which contains the RGB_id and corresponding RGB values.</p> <p>All files follow the naming convention: <code>{central_tree_id}_{timestamp}</code>, where <code>{central_tree_id}</code> represents the ID of the tree centered in the image, which is typically in a prominent position, and <code>timestamp</code> indicates the time when the data was collected.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>rgb</strong>: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: <code>Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36</code>.</p> </li> <li> <p><strong>depth_pfm</strong>: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.</p> </li> <li> <p><strong>instance_segmentation</strong>: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps <code>tree_id</code> to <code>RGB_id</code>. The <code>tree_id</code> can be used to look up detailed information about each tree in <code>obj_info_final.xlsx</code>, while the <code>RGB_id</code> can be matched to the corresponding RGB values in <code>color_palette.xlsx</code>. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.</p> </li> <li> <p><strong>obj_info_final.xlsx</strong>: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).</p> </li> <li> <p><strong>landscape_info.txt</strong>: This file contains the ground location information within the scene, sampled every 0.5 meters.</p> </li> </ul> <p>For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets:</p> <ul> <li><strong>{name}_coco.json</strong>: This file contains the annotation of each tree in the scene.</li> <li><strong>{name}_filtered.json</strong>: This file is derived from the previous one, but filtering is applied to rule out overlapping instances.</li> </ul> <p>⚠️: 7z files that begin with "<strong>!</strong>" indicate that the RGB values in the images within the <code>instance_segmentation</code> folder cannot be found in <code>color_palette.xlsx</code>. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in <a href="https://github.com/microsoft/AirSim/issues/3423" target="_blank" rel="noopener">link1</a> and <a href="https://github.com/microsoft/AirSim/issues/1852" target="_blank" rel="noopener">link2</a>.</p>
Synthetic dataset for the job-shop problem (JS_DS_01)
<p>Synthetic dataset containing parts routes, setup and processing times for a job-shop problem of 1.000 parts and 200 machines.</p>
DeepCAD-RT dataset: synthetic calcium imaging data
<p>DeepCAD-RT dataset: synthetic calcium imaging data</p>
Classification of a synthetic strain rate dataset acquired with Distributed Acoustic Sensing (DAS).
<p>The videos show the real-time acquisition of a strain rate dataset acquired with a Distributed Acoustic Sensing (DAS). The observations are collected per bloc of 4s and are segmented in six sources: noise, pedestrian, impact, backhoe, compactor, leaks. The first video shows the source identification and segmentation using a Random Forest classifier; the second video combines a Random Markov Field to the Random Forest classifier.</p>
(SEN12MS) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>SEN12MS </strong>NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p>
(capsicum) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>capsicum</strong> NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p>
SynPhoRest - Synthetic Photorealistic Forest Dataset with Depth Information for Machine Learning Model Training
<p><strong>SynPhoRest </strong>is a synthetic dataset collected on virtual forests. It features RGB images, semantic segmentation maps, depth maps and the projection of LIDAR point clouds on the RGB FOV for two different LIDAR scanning patterns. The dataset has a total of 3154 frames.<br> <br> A description of the available data follows:</p> <p><strong>RGB images</strong></p> <ul> <li>Resolution: 848 x 480 pixels.</li> <li>PNG files with 8 bits encoding per channel.</li> </ul> <p><strong>Segmentation Maps</strong></p> <ul> <li>Resolution: 848 x 480 pixels.</li> <li>PNG files with a single 8 bit channel.</li> <li>Classes are encoded as follows:</li> <li> <table> <thead> <tr> <th scope="col"><strong>Value</strong></th> <th scope="col"><strong>Class</strong></th> </tr> </thead> <tbody> <tr> <td>0</td> <td>Background</td> </tr> <tr> <td>1</td> <td>Soil</td> </tr> <tr> <td>2</td> <td>Traversable</td> </tr> <tr> <td>3</td> <td>Canopy</td> </tr> <tr> <td>4</td> <td>Fuel</td> </tr> <tr> <td>5</td> <td>Trunks</td> </tr> </tbody> </table> <p>Fuel represents flammable material such as shrubbery and grass.</p> </li> </ul> <p><strong>Depth Maps</strong></p> <ul> <li>Resolution: 848 x 480 pixels.</li> <li>PNG files with a single 16 bit channel</li> <li>The depth value is encoded in the unsigned integer format.</li> <li>Infinite depth is represented by the value 65535.</li> <li>To obtain the depth values in meters, the original values must by divided by 256.</li> <li>The FOV of the virtual depth camera was the same as the FOV of the RGB camera.</li> </ul> <p><strong>LIDAR Point Cloud Projections on the RGB Camera FOV</strong></p> <ul> <li>Resolution: 848 x 480 pixels.</li> <li>PNG files with a single 16 bit channel.</li> <li>The distance values are encoded in the unsigned integer format.</li> <li>To obtain the distance values in meters the original values must by divided by 256.</li> <li>On average, the projection images have a point density of 5.5%. In practice, this means that 5.5% of the pixels in the projection image have distance information.</li> <li>Two LIDAR Point Cloud Projections were made available. One for a LIDAR with a repeating line pattern and other resembling the commercially available Livox Horizon LIDAR scanner.</li> </ul>
(nirscene) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>nirscene</strong> NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p> <p> </p>
Sample Synthetic Training Dataset: Part1
<p>A sample dataset that we sub-sampled in order to train the yes/no lizard and species identification machine learning model displayed in the nature methods brief communication paper. </p> <p>This Part Contains the Data For E. e. croceater and "Blank Backgrounds".</p> <p> </p>
Creating Site Specific Synthetic ML Training Datasets for Conservation: Sample Synthetic Training Dataset [Resized]
<p>These files support the paper "Creating Site Specific Synthetic Machine Learning Training Datasets for Conservation" by providing a sample of the synthetic data generated using the described methodology. Additionally, a sample of this dataset could and has been used to train different image classifier machine learning models. </p> <p>The provided ZIP file contains four folders of synthetic training data JPEG images, separated by species, then background-type within the subsequent sub-folders. All of these folders and sub-folders are clearly labeled. The folder marked "Blank Backgrounds" is the only folder with images that DO NOT contain any herpetofauna. Instead, this folder consists of only background images, which were used to train an image classifier model to recognize the presence of (or lack there-of) a herpetofauna specimen within an image. </p> <p><strong>*NOTE - These images were resized for the purposes of efficient upload to Github//Zenodo. There may be same quality/detail loss compared to the original images...*</strong></p>
The synthetic and field seismic datasets for "ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation"
<p>This is the synthetic and field siesmic dataset used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". The dimensions of the large-scale and small-scale synthetic seismic datasets are 1600×256 pixels and 900×256 pixels. The field seismic data contains the subsets of the F3 Block, Australia Poseidon, Alaska North Slope seismic data.</p>
Synthetic dataset and prediction files for the paper "Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction", by Costantino et al. (2024)
<p>Synthetic database used for training and evaluation of SSEdenoiser</p>
Synthetic Activity Dataset
<p>Synthetic activity dataset representing daily living of elderly people in a nursery home. The dataset represents the location in a room-level (i.e room, gym, therapy room, terrace,...) from each person along the time</p>
Synthetic dataset for the exomoon candidate around Kepler-1625 b
<p>We provide the dataset as discussed in the paper to the community for reproducibility and encourage further blind retrievals.</p>
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.