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250 results for “Synthetic dataset”
Synthetic gaussian datasets
<p>A collection of 16 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 feature anisotropy. Can be used to test clustering methods.</p>
SD4EO: AI-based synthetic solar panel dataset on urban areas
<p>This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>. It consists of aerial images of urban areas that mimic certain regions inside Poitiers, Bordeaux and Toulouse.<br>The images have been synthetized with a generative diffusion model, conditioned with schematic maps, and later augmented to include solar panels in the most sunlighted portions of the building roofs.</p> <p>Each entry has 4 types of files:</p> <ul> <li>A PNG image containing the binary mask with pixel-by-pixel segmentation of areas where solar panels have been installed.</li> <li>A PNG image which shows a variant of the synthetic image, each featuring differently placed panels with white support structures.</li> <li>A TXT file containing the locations of axis-aligned bounding boxes in YOLOv8 format (also compatible with YOLOv5). These are included only if panels have been added to the image, with one file per panel variant, to facilitate YOLO training without needing to modify its Dataset class for file reading.</li> <li>An NPZ file storing the coordinates of the bounding boxes aligned with the solar panels (not with the axes), intended for use in more advanced object detection models like YOLOv8.1 or Mask R-CNN.</li> </ul> <p>The dataset includes 46,872 synthetic images, which have been further enhanced by adding solar panels in strategic locations. The complete dataset with metadata has been subdivided into 18 ZIP files (each containing a different subfolder), totalling 6.5GB. This division was made to avoid overloading the storage system. By distributing the 156,127 files across multiple folders, we prevent potential issues on users' computers related to exceeding the maximum number of inodes in the file system and/or the operating system.</p> <p>The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.</p>
Synthetic dataset for the testing of local conditioning of regularization function using geological uncertainty.
<p>This companion datasets relates to the model shown supplementary information to the manuscript "<strong>Integration of geological uncertainty into geophysical inversion by means of local gradient regularization</strong>", by J. Giraud, M. Lindsay, V. Ogarko, M. Jessell, R. Martin and E. Pakyuz-Charrier, submitted to Solid Earth. The archive contains the input and output geophysical data, starting and inverted models, probabilistic geological model and conditioning volume derived from the calculation of Shannon's entropy. </p> <p>This description will be updated with the accurate reference to the journal publication and acknowledgement prior to publication. </p>
Synthetic dataset rain Samedan
<p>rain time series from Samedan (mm) and generated flows (m3/s) using a simple pyswmm model.</p>
Dataset of synthetic whole-body bone scintigraphy scans representing three clinical conditions
<p>We here provide an image dataset consisting of 1,000 synthetic whole-body bone scintigraphy scans (anterior projection) generated by a generative artificial intelligence model. This dataset consists of images representing three different clinical conditions: (1) bone uptake indicative of bone metastases, (2) cardiac uptake indicative of cardiac amyloidosis, and (3) none of the two.</p> <p>The clinical condition (label) of each image is provided in the csv file:</p> <ul> <li>Label 1: Bone uptake indicative of bone metastases (n=250 scans)</li> <li>Label 2: Cardiac uptake indicative of cardiac amyloidosis (n=250 scans)</li> <li>Label 0: None of the two (n=500 scans)</li> </ul> <p>This synthetic dataset does not comprise real patient data. The provided synthetic images were created by a generative artificial intelligence model. The model was trained on bone scintigraphy scans (radiotracer: 99mTc-DPD) from 9,170 patients from the Vienna General Hospital collected as part of the clinical routine. The training data covered a wide range of different pathologies, scanners, and imaging protocols. Hence, the provided synthetic dataset represents real-world data without disclosing patient privacy.</p> <p>More details about the dataset can be found in the corresponding paper (link added upon publication). Please cite this paper if you use the dataset.</p>
EMS3D-KITTI-Synthetic: A Synthetic 3D Dataset in KITTI Format with Balanced EMS Vehicle Distribution for Autonomous Driving AI Model Training
<p>A 3D synthetic dataset in KITTI format, focused on emergency vehicles such as ambulances and police cars. The dataset was generated across 8 towns within the CARLA simulator and converted into the KITTI format, ensuring compatibility for direct use in AI model training for autonomous driving applications.</p>
Synthetic dataset: Traffic Accidents & Vehicle Registry
<ul> <li><strong>Dataset A: Traffic Accidents</strong> -- Comprising 15,000 records with attributes such as Accident ID, Date, Time, Location, Vehicle ID, Severity, and Description.</li> <li><strong>Dataset B: Vehicle Registry</strong> -- Comprising 20,000 records with attributes including Vehicle ID, Owner Age, Owner Gender, Vehicle Model, and Registration Date.</li> </ul>
SynRS3D : A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery
<h1><strong>SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery</strong></h1> <h3><strong>Neural Information Processing Systems (Spotlight), 2024</strong></h3> <p>For more details, please refer to our <a href="https://arxiv.org/pdf/2406.18151">paper</a> and visit our <a href="https://github.com/JTRNEO/SynRS3D">GitHub repository</a>.</p> <h2><strong>Overview</strong></h2> <p><strong>TL;DR:</strong><br>SynRS3D is a comprehensive synthetic remote sensing dataset designed to improve global 3D semantic understanding from monocular high-resolution imagery. It includes data for three key tasks:</p> <ul> <li>Height estimation</li> <li>Land cover mapping</li> <li>Building change detection</li> </ul> <h2><strong>Dataset Structure</strong></h2> <p>The dataset consists of 17 folders and includes a total of 69,667 images at a resolution of 512x512. After downloading and extracting the files, ensure the directory structure follows this format:</p> <p>${DATASET_ROOT} # Example: /home/username/project/SynRS3D/data/grid_g05_mid_v1<br>├── opt # RGB images (.tif), also used as post-event images for building change detection<br>├── pre_opt # RGB images (.tif), used as pre-event images for building change detection<br>├── gt_nDSM # Normalized Digital Surface Model (nDSM) images (.tif)<br>├── gt_ss_mask # Land cover mapping labels (.tif)<br>├── gt_cd_mask # Building change detection masks (.tif, 0 = no change, 255 = change area)<br>└── train.txt # List of training data filenames</p> <p>The land cover mapping labels (`gt_ss_mask`) are mapped to the following categories:</p> <ul> <li>Bareland: 1</li> <li>Rangeland: 2</li> <li>Developed Space: 3</li> <li>Road: 4</li> <li>Trees: 5</li> <li>Water: 6</li> <li>Agriculture land: 7</li> <li>Buildings: 8</li> </ul> <h2><strong>Image Breakdown by Folder</strong></h2> <p>The dataset is organized into grid-like and irregular terrain. It includes a range of ground sampling distances (GSDs) and variations in building heights. The folder naming convention indicates these characteristics: <br>- `grid` = grid-like terrain <br>- `terrain` = irregular terrain <br>- `g005`, `g05`, `g1` = GSD ranges (0.05m–0.3m, 0.3m–0.6m, and 0.6m–1m, respectively) <br>- `low`, `mid`, `high` = building height variations</p> <p>The dataset includes the following image counts:</p> <p>- 1,430 images – `terrain_g05_mid_v1`<br>- 10,000 images – `grid_g05_mid_v2`<br>- 2,354 images – `terrain_g05_low_v1`<br>- 3,707 images – `terrain_g05_high_v1`<br>- 880 images – `terrain_g005_mid_v1`<br>- 2,127 images – `terrain_g005_low_v1`<br>- 11,325 images – `grid_g005_mid_v2`<br>- 1,212 images – `terrain_g005_high_v1`<br>- 348 images – `terrain_g1_mid_v1`<br>- 4,285 images – `terrain_g1_low_v1`<br>- 904 images – `terrain_g1_high_v1`<br>- 3,000 images – `grid_g005_mid_v1`<br>- 2,997 images – `grid_g005_low_v1`<br>- 4,000 images – `grid_g005_high_v1`<br>- 7,000 images – `grid_g05_mid_v1`<br>- 7,098 images – `grid_g05_low_v1`<br>- 7,000 images – `grid_g05_high_v1`</p> <h2><strong>Citation</strong></h2> <p>If you find SynRS3D useful in your research, please consider citing:</p> <div> <div>@article{song2024synrs3d,</div> <div>title={SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery},</div> <div>author={Song, Jian and Chen, Hongruixuan and Xuan, Weihao and Xia, Junshi and Yokoya, Naoto},</div> <div>journal={arXiv preprint arXiv:2406.18151},</div> <div>year={2024}</div> <div>}</div> </div> <h2><strong>Contact</strong></h2> <p>For any questions or feedback, feel free to reach out via email: <strong> song@ms.k.u-tokyo.ac.jp</strong>.</p> <p>Enjoy using SynRS3D!</p>
Dataset for the manuscript "Assessing the Role of Hydrodynamics in Enhancing HAND-Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan"
<p>This is the dataset for developing the HAND-hd workflow mentione in the manuscript "Assessing the Role of Hydrodynamics in Enhancing HAND-Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan". In the manuscript, we used the topographical cross-sectional survey data to generate topographical synthetic rating curves (RCb) to validate the performance of the HAND method based synthetic rating curves (HAND-SRC) produced by the HAND-hd workflow. Due to regulatory restrictions under Taiwanese law, public sharing of 5-meter (or finer) DEM data is prohibited. However, researchers interested in accessing processed HAND raster data for research purposes may contact the corresponding author.</p>
Benchmark datasets to study fairness in synthetic data generation
<p>The traveltime dataset is based on the Folktables project covering US census data. The target is a binary variable encoding whether or not the individual needs to travel more than 20 minutes for work; here, having a shorter travel time is the desirable outcome. We use a subset of data from the states of California, Florida, Maine, New York, Utah, and Wyoming states in 2018. Although the folktables dataset does not have any missing values, there are some values recorded as NaN due to the Bureau's data collection methodology. We remove the "esp" column, which encodes the employment status of parents, and has 99.55% missing values. We encode the missing values in the povpip, income to poverty ratio (0.85%), to -1 in accordance to the methodology in Ding et al.. See https://arxiv.org/pdf/2108.04884 for metadata.</p> <p>The cardio (a) dataset contains patient data recorded during medical examination, including 3 binary features supplied by the patient. The target class denotes the presence of cardiovascular disease. This dataset represents predictive tasks that allocate access to priority medical care for patients, and has been used for fairness evaluations in the domain.</p> <p>The credit dataset contains historical financial data of borrowers, including past non-serious delinquencies. Here, a serious delinquency is considered to be 90 days past due, and this is the target variable.</p> <p>The German Credit dataset (https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data) contains financial and personal information regarding loan-seeking applicants.</p>
SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection
<h2>Abstract </h2> <blockquote> <p><em>Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, synthetic data presents a promising and cost-effective solution to overcome this issue. Therefore, we present SynDroneVision, a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. Featuring diverse backgrounds, lighting conditions, and drone models, SynDroneVision offers a comprehensive training foundation for deep learning algorithms. To evaluate the dataset's effectiveness, we perform a comparative analysis across a selection of recent YOLO detection models. Our findings demonstrated that SynDroneVision is a valuable resource for real-world data enrichment, achieving notable enhancements in model performance and robustness, while significantly reducing the time and costs of real-world data acquisition. </em> </p> </blockquote> <h2><strong>Paper</strong></h2> <h3><strong>Published in the Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2025)!</strong></h3> <p>SynDroneVision is presented in the paper <strong>SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection</strong> by Tamara R. Lenhard, Andreas Weinmann, Kai Franke, and Tobias Koch. This work is published in the Proceedings of the <strong>2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2025)</strong>.</p> <p>The preprint is currently available on ArXiv: <a href="https://arxiv.org/abs/2411.05633v1">here</a></p> <p>The final version is now published in the proceedings of WACV 2025: <a href="https://ieeexplore.ieee.org/document/10943801" target="_blank" rel="noopener">here</a> </p> <h2><strong>Dataset Details</strong></h2> <p>SynDroneVision comprises a total of<strong> 140,038</strong> <strong>annotaed RGB images</strong> (131,238 for training, 8,800 for validation, and 4,000 for testing), featuring a resolution of <strong>2560x1489</strong> pixels. All images are recorded in a sequential manner using <a href="https://www.unrealengine.com/en-US/">Unreal Engine 5.0</a> in combination with <a href="https://github.com/CodexLabsLLC/Colosseum">Colosseum</a>. Apart from drone images, SynDroneVision also includes ~7% of background images (i.e., imag frames without drone instances).</p> <p><strong>Annotation Format:</strong> Annotations (bounding boxes) are provided via text files according to the <strong>YOLO standard format</strong></p> <pre><code><object-class> <x> <y> <width> <height></code></pre> <p>Here, <code><x></code> and <code><y></code> represent the normalized coordinates of the bounding box center, while <code><width></code> and <code><height></code> denote the normalized bounding box wisth and height. In SynDroneVision, <code><object-class></code> is always 0, indicating the drone class.</p> <h2><strong>Download</strong></h2> <p>The SynDroneVision dataset offers around 900 GB of data dedicated to image-based drone detection. To facilitate the download process, we have partitioned the dataset into smaller sections. Specifically, we have divided the training data into 10 segments, organized by sequences.</p> <p>Annotations are available below, with image data accessible via the following links:</p> <table> <tbody> <tr> <td><strong>Dataset Split<br></strong></td> <td><strong>Sequences<br></strong></td> <td><strong>File Name<br></strong></td> <td><strong>Link</strong></td> <td><strong>Size (GB)<br></strong></td> </tr> <tr> <td>Training Set</td> <td>Seq. 001 - 009</td> <td>images_train_seq001-009.zip</td> <td><a href="https://datastore.dlr-pi.de/s/ted96QK36RmXgLs" target="_blank" rel="noopener">Training images PART 1</a></td> <td>57</td> </tr> <tr> <td> </td> <td>Seq. 010 - 018</td> <td>images_train_seq010-018.zip</td> <td><a href="https://datastore.dlr-pi.de/s/28GtS3qeNGksWk7" target="_blank" rel="noopener">Trainng images PART 2</a></td> <td>95.4</td> </tr> <tr> <td> </td> <td>Seq. 019 - 027</td> <td>images_train_seq019-027.zip</td> <td><a href="https://datastore.dlr-pi.de/s/DLCwRBRnsf53Rg7" target="_blank" rel="noopener">Training images PART 3</a></td> <td>96.2</td> </tr> <tr> <td> </td> <td>Seq. 028 - 035</td> <td>images_train_seq028-035.zip</td> <td><a href="https://datastore.dlr-pi.de/s/oF9PmFCexHbB2bp" target="_blank" rel="noopener">Training images PART 4</a></td> <td>83.9</td> </tr> <tr> <td> </td> <td>Seq. 036 - 043</td> <td>images_train_seq036-043.zip</td> <td><a href="https://datastore.dlr-pi.de/s/eepMQrixWXNXNYS" target="_blank" rel="noopener">Training images PART 5</a></td> <td>77.1</td> </tr> <tr> <td> </td> <td>Seq. 044 - 050</td> <td>images_train_seq044-050.zip</td> <td><a href="https://datastore.dlr-pi.de/s/FPnyEAcmpjomY9q" target="_blank" rel="noopener">Training images PART 6</a></td> <td>84.7</td> </tr> <tr> <td> </td> <td>Seq. 051 - 056</td> <td>images_train_seq051-056.zip</td> <td><a href="https://datastore.dlr-pi.de/s/awsHAJqKe85AqiG" target="_blank" rel="noopener">Training images PART 7</a></td> <td>86.8</td> </tr> <tr> <td> </td> <td>Seq. 057 - 065</td> <td>images_train_seq057-065.zip</td> <td><a href="https://datastore.dlr-pi.de/s/2LH8TmAjG94r26P" target="_blank" rel="noopener">Training images PART 8</a></td> <td>86.2</td> </tr> <tr> <td> </td> <td>Seq. 066 - 070</td> <td>images_train_seq066-070.zip</td> <td><a href="https://datastore.dlr-pi.de/s/BGkLANPT3mAwEw8" target="_blank" rel="noopener">Training images PART 9</a></td> <td>75.7</td> </tr> <tr> <td> </td> <td>Seq. 071 - 073</td> <td>images_train_seq071-073.zip</td> <td><a href="https://datastore.dlr-pi.de/s/WCEQPCioNxdqJr9" target="_blank" rel="noopener">Training images PART 10</a></td> <td>38.5</td> </tr> <tr> <td>Validation Set</td> <td>Seq. 001 - 073</td> <td>images_val.zip</td> <td><a href="https://datastore.dlr-pi.de/s/9nYjwxGXJe5ws7g" target="_blank" rel="noopener">Validation images</a></td> <td>55.2</td> </tr> <tr> <td>Test Set</td> <td>Seq. 001 - 073</td> <td>images_test.zip</td> <td><a href="https://datastore.dlr-pi.de/s/5YgqR75oBByEz6R" target="_blank" rel="noopener">Test images</a></td> <td>26.5</td> </tr> </tbody> </table> <h2><strong>Citation</strong></h2> <p>If you find SynDroneVision helpful in your research, we kindly ask that you cite the associated paper. Below is the citation in BibTeX format for your convenience:</p> <p><strong>BibTeX:</strong></p> <pre>@INPROCEEDINGS{10943801, author={Lenhard, Tamara R. and Weinmann, Andreas and Franke, Kai and Koch, Tobias}, booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, title={SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection}, year={2025}, volume={}, number={}, pages={7637-7647}, doi={10.1109/WACV61041.2025.00742}} <br><br></pre> <p><em>SynDroneVision uses Unreal® Engine. Unreal® is a trademark or registered trademark of Epic Games, Inc. in the United States of America and elsewhere.</em></p>
pGAN Synthetic Dataset: A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
<p>Synthetic dataset for <strong>A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs</strong></p> <p><strong> Dataset specification:</strong></p> <ul> <li>MRI images of Vertebral Units labelled based on region</li> <li>Dataset is comprised of 10000 pairs of images and labels</li> <li>Image and label pair number k can be selected by: synthetic_dataset['images'][k] and synthetic_dataset['regions'][k]</li> <li>Images are 3D of size (9, 64, 64)</li> <li>Regions are stored as an integer. Mapping is 0: cervical, 1: thoracic, 2: lumbar</li> </ul> <p>Arxiv paper: <a href="https://arxiv.org/abs/2106.13199">https://arxiv.org/abs/2106.13199</a><br> Github code: <a href="https://github.com/tcoroller/pGAN/">https://github.com/tcoroller/pGAN/</a></p> <p>Abstract:</p> <p>Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.</p>
Synthetic 4D STEM dataset based on a SrTiO3 supercell with two additional artificial spatial frequencies
<p>This dataset allows to investigate phase contrast methods for 4D scanning transmission electron microscopy, such as ptychography.</p> <p>A synthetic dataset has been simulated, based on an SrTiO<sub>3</sub> unit cell as a starting point. Then, a five by five super cell was created by repetition. Two artificial spatial frequencies were added to the phase grating, one with a wavelength of a single unit cell and one with a wavelength of the super cell. To eliminate dynamical scattering, a 4D-STEM simulation with 20 × 20 scan points per unit cell was performed using only one slice with a thickness of one unit cell along electron beam direction [001].</p> <p><strong>Files</strong></p> <ul> <li><em>conf_01.mat</em>: HDF5 file with the phase grating.</li> <li><em>Data extraction and plot of the phase grating.ipynb</em>: Jupyter notebook showing how to access the phase grating file and plot the data.</li> <li><em>slice_00001_thick_1.9525_nm_blocksz100.raw</em>: Simulated 4D STEM dataset as a raw binary file. Shape 100 x 100 x 596 x 596, dtype float32.</li> <li><em>ssb-example.ipynb</em>: Jupyter notebook showing first moment analysis and ptychography with the dataset.</li> </ul> <p><strong>Simulation parameters</strong></p> <ul> <li>Scan points: 100x100</li> <li>Field of view: 1.9525nm</li> <li>Convergence angle: 23mrad, 136 px</li> <li>Acceleration voltage: 300 kV</li> <li>Center: (297, 297)</li> <li>Rotation angle: 0°</li> </ul>
synthetic dataset covid-19 PER
<p>Synthetic dataset for outbreaks col-per 2021</p>
Dataset for manuscript entitled "The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm"
<p>The following datasets were used for the 16s rRNA analysis in the manuscript entitled " The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm". BZ2 files were obtained from next generation sequencing with the Illumina Mi-Seq. Mothur was used to analyze the BZ2 files, creating the listed excel documents.</p>
Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders: Synthetic dataset for validation of trace gas retrieval algorithms
<p>This data set is described in detail in a paper submitted to AMTD:</p> <p><strong>Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders - Part I: Synthetic dataset for validation of trace gas retrieval algorithms</strong></p> <p>by Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and<br> Bernhard Mayer</p> <p> </p> <p>The subdirectory <em>boxcloud</em> includes synthetic reflectances for clearsky, 1D cloud and box cloud.</p> <p>The subdirectory <em>les_cloud</em> includes synthetic reflectances for the LES cloud scenario for low earth orbit (<em>leo</em>) and geostationary orbit (<em>geo</em>).</p> <p>All data are provided in <em>netcdf</em> format.</p> <p> </p>
Synthetic ground motions to support the Fennoscandian GMPEs. Supporting information – Response spectra dataset in excel format
<p>This dataset has been created in the NKS project: ”Synthetic ground motions to support the Fennoscandian GMPEs”, contract: NKS-R(18)126/5. The data is RotD50 (Boore, 2010, doi: <a href="https://doi.org/10.1785/0120090400">10.1785/0120090400</a>), pseudo-acceleration response spectra calculated from synthetic ground motions generated using physics-based modeling of Fennoscandian earthquakes. The earthquake magnitude range is 4.3-5.6; the rupture distance range 2-30km and the hypocenter depth range 2-20km. The response spectra is in mm/s2 and should be used up to 25Hz.</p> <p>Cite the data as part of the research report: Fülöp, L., Jussila, V., Fälth, B., Voss, P., Lund, B. 2019. Synthetic ground motions to support the Fennoscandian GMPEs. NKS - Nordic Nuclear Safety Research NKS-424, ISBN: ISBN 978-87-7893-514-4</p> <p>Methods used to generate the data are described in:</p> <p>Fülöp, L., Jussila, V., Lund, B., Fälth, B., Voss, P., Puttonen, J., Saari, J., and Heikkinen, P. 2017. Modelling as a Tool to Augment Ground Motion Data in Regions of Diffuse Seismicity – Final report. NKS - Nordic Nuclear Safety Research NKS-394, ISBN: 978-87-7893-482-6</p> <p>Fülöp, L., Jussila, V., Lund, B., Fälth, B., Voss, P., Puttonen, J., Saari, J., 2016. Modelling as a tool to augment ground motion data in regions of diffuse seismicity - Progress 2015. NKS Nordic Nuclear Safety Research, ISBN: 978-87-7893-448-2</p>
Dataset for: Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks
<p>Datasets for the paper Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks, published in BMC Biology.</p>
Synthetic vehicle trajectory dataset for the metropolitan city of Los Angeles using DDTG
<p>The analysis of trajectory datasets has numerous applications ranging from urban planning to human mobility understanding, but to protect the privacy of individuals trajectory datasets are rarely released to researchers. And even when they are, they are limited in size and spatio-temporal coverage. To address these issues a number of methods for generating synthetic yet realistic trajectory datasets have been proposed. These existing methods either require a lot of complex parameters to be calibrated (simulators) or rely on existing trajectory datasets (generative models). We use our proposed, and recently published at IEEE BigData 2022 conference, Data-Driven Trajectory Generator, dubbed DDTG, to generate a synthetic vehicle trajectory dataset in the metropolitan city of Los Angeles. The dataset consists of 1.5 million trajectories spanning the first two weeks of December 2019.</p>
Supplementary Dataset for "Synthetic Witherite for Standardization of Clumped Isotope (Delta 47) Analyses"
<p>This supplementary dataset contains raw clumped isotopic data used to generate figures in the article "Synthetic Witherite for Standardization of Clumped Isotope (Delta 47) Analyses" by Kong et al. </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.