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250 results for “Synthetic Dataset”
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
<p><strong>Paper Accept by WACV 2024</strong></p> <p>[paper, supp] [<a href="https://arxiv.org/abs/2309.01907">arXiv</a>]</p> <p><strong>Overview</strong></p> <p>Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages.</p> <pre><strong>Description</strong> ------------ This dataset has been designed for land cover mapping and building change detection tasks. File Structure and Content: --------------------------- 1. **1024.zip**: - Contains images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `small-pre-images`: Images with a minor off-nadir angle difference compared to post-event images. - `big-pre-images`: Images with a large off-nadir angle difference compared to post-event images. - `cd_mask`: Ground truth for the building change detection task. 2. **512-1.zip**, **512-2.zip**, **512-3.zip**: - Contains images of size 512x512 with a GSD of 0.3-0.6m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `pre-event` folder: Images for the pre-event phase. - `cd-mask`: Ground truth for building change detection. Land Cover Mapping Class Grep Map: ---------------------------------- class_grey = { "Bareland": 1, "Rangeland": 2, "Developed Space": 3, "Road": 4, "Tree": 5, "Water": 6, "Agriculture land": 7, "Building": 8, }</pre> <p><strong>Reference</strong></p> <p>@misc{song2023syntheworld,<br> title={SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection}, <br> author={Jian Song and Hongruixuan Chen and Naoto Yokoya},<br> year={2023},<br> eprint={2309.01907},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> }</p> <p> </p>
Dataset for New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids
<p>Data set for the publication named: "New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids"</p>
Synthetic river datasets built for testing and development of the Surface Water and Ocean Topography mission discharge algorithms
<p><strong>1.Summary</strong></p> <p>Datasets used for testing the performance of discharge estimation algorithms built in support of the Surface Water and Ocean Topography satellite mission. The benchmarking manuscript entitled “Exploring the factors controlling the performance of the Surface Water and Ocean Topography mission discharge algorithms” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.</p> <p><strong>2.File description</strong></p> <p>The dataset is divided into four groups: 1-Ideal data, 2-Varying Temporal Sampling, 3-Measurement Uncertainty, and 4-SWOT Sampling and Uncertainty. Ideal data contains daily measurements with no observational uncertainty. Varying Temporal Sampling downsamples the ideal measurements considering different temporal frequencies with complete sets assuming: 1 measurement every 2 days, 3 days, 4 days, 5 days, 7 days, 10 days, and 21 days. The measurement uncertainty set adds errors to cross-sectional heights and widths, which are used to compute reach average height, width, and slope considering error corruption. The final set SWOT Sampling and Uncertainty accounts for SWOT temporal sampling and measurement uncertainty. Sets containing uncertainty have extra height, width, and slope attributes with the word true appended to the attribute name. Such attributes represent the uncorrupted measurements at the cross-section and reach scales. Height, width, and slopes for the SWOT sampling and Uncertainty dataset containing the value of negative 9999 denote points that are not observed at a particular location and time step.</p> <p>Data will be contained in one NetCDF file per river. The file contains the following groups and variables:</p> <p><strong>/River_Info/</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Reaches used in the study. Used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>/XS_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>Z: Bed elevation in meters. Dimension: Cross-section, time step.</p> <p>xs_rch: Reach number for each cross-section. Dimension: Cross-section,1.</p> <p>X: Flow distance measured from the most upstream end of the model to the cross-section (meters). Dimension: Cross-section, 1.</p> <p>longitude: Cross-section longitude in decimal degrees. Dimension: Cross-section,1.</p> <p>latitude: Cross-section latitude in decimal degrees. Dimension: Cross-section,1.</p> <p>W: River width in meters. Dimension: Cross-section, time step.</p> <p>Wtrue: River width in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>Q: Discharge (m<sup>3</sup>/s). Dimension: Cross-section, time step.</p> <p>H: Water surface elevation in meters. Dimension: Cross-section, time step.</p> <p>Htrue: Water surface elevation in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>A: Cross-sectional area of flow in m<sup>2</sup>. Dimension: Cross-section, time step.</p> <p>P: Wetted perimeter in meters. Dimension: Cross-section, time step.</p> <p>n: Manning’s roughness. Dimension: Cross-section, time step.</p> <p><strong>/Reach_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>W: Reach averaged river width in meters. Dimension: Reach, time step.</p> <p>Wtrue: Reach averaged river width in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the width value with no uncertainty.</p> <p>Q: Reach averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>H: Reach averaged water surface elevation in meters. Dimension: Reach, time step.</p> <p>Htrue: Reach averaged water surface elevation in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>S: Reach averaged water surface slope in meters per meter. Reach, time step.</p> <p>Strue: Reach averaged water surface slope in meters per meter. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the slope value with no uncertainty.</p> <p>A: Reach averaged area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p><strong>References</strong></p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: <a href="https://doi.org/10.1016/j.gloplacha.2014.01.011">https://doi.org/10.1016/j.gloplacha.2014.01.011</a>.</p> <p> </p>
Synthetic and real datasets for "Seismic source tracking with six degree-of-freedom ground motion observations"
<p>Synthetic datasets for the 2D and 3D rupture tracking and real datasets for the traffic noise tracking used in the manuscript "Seismic source tracking with six degree-of-freedom ground motion observations". The README file describes the data structures.</p>
Dataset in R format, containing labor force data for a synthetic population
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6DOF pose estimation - synthetically generated dataset using BlenderProc
<p>Accurate and robust 6DOF (Six Degrees of Freedom) pose estimation is a critical task in various fields, including computer vision, robotics, and augmented reality. This research paper presents a novel approach to enhance the accuracy and reliability of 6DOF pose estimation by introducing a robust method for generating synthetic data and leveraging the ease of multi-class training using the generated dataset. The proposed method tackles the challenge of insufficient real-world annotated data by creating a large and diverse synthetic dataset that accurately mimics real-world scenarios. The proposed method only requires a CAD model of the object and there is no limit to the number of unique data that can be generated. Furthermore, a multi-class training strategy that harnesses the synthetic dataset's diversity is proposed and presented. This approach mitigates class imbalance issues and significantly boosts accuracy across varied object classes and poses. Experimental results underscore the method's effectiveness in challenging conditions, highlighting its potential for advancing 6DOF pose estimation across diverse applications. Our approach only uses a single RGB frame and is real-time.</p>
Synthetic Car Dataset
<p>Vehicle detection is a very important aspect of computer vision application to aerial and satellite imagery, facilitating activities such as instance counting, velocity estimation, traffic predictions, etc. The feasibility of accurate vehicle detection often depends on limited training datasets, requiring a lot of manual work in collection and annotation tasks. Furthermore, there are no known publicly available datasets. Our aim was to construct a pipeline for synthetic dataset generation from aerial imagery and 3D models in Blender software. The dataset generation pipeline consists of seven steps and results in a wished number of images with bounding boxes in YOLO and coco formats. This synthetic dataset has been produced following the steps described in this pipeline. It consists of 5000 2048x2048 images with cars inserted into the roads and highways at the images without cars from all over the world. We believe that this dataset and the respective pipeline might be of great importance for vehicle detection, facilitating the customizability of the models to specific needs and context.</p>
Synthetic datasets with controlled structures at first and second order
<p>These are the synthetic datasets analyzed in "Covariance based information processing in reservoir computing systems" (https://doi.org/10.1101/2021.04.30.441789)</p>
Datasets for the integration of MT data with magnetic inversion: proof-of-concept using synthetic data and field application
<p>This dataset is a companion dataset to the manuscript "<strong>Utilisation of probabilistic MT inversions to constrain magnetic data inversion: proof-of-concept and field application</strong>", by Jérémie Giraud, Hoël Seillé, Mark D. Lindsay, Gerhard Visser, Vitaliy Ogarko, and Mark W. Jessel. <br><br>It contains models and data shown in the paper.<br><br>The document was submitted for publication in Solid Earth: <br>https://se.copernicus.org/preprints/se-2021-124/se-2021-124-manuscript-version2.pdf<br><br>The folder organisation is as follows, where <strong>bold</strong> refers to folders and subfolders, and text in <i>italic</i> corresponds to a succinct description of the contents.<br><br>|-- <strong>Dataset_synth</strong> > <i>synthetic dataset and results</i><br>| |-- <strong>Mag </strong>> <i>magnetic data and models: inversion and results </i><br>| | |-- <strong>domains </strong>> <i>contains the files used to define domains for inversion</i><br>| | |-- <strong>inversion results </strong>> <i> contains subfolders with inversion results for different cases </i><br>| | | |--<strong> case a </strong><br>| | | |--<strong> case b</strong><br>| | | |--<strong> case c</strong><br>| | | |-- <strong>case d</strong><br>| | | |-- <strong>case e</strong><br>| | | |-- <strong>case f </strong><br>| | |-- <strong>membership values</strong> > <i>contain files with membership values for the cases (a)-(f)</i><br>| | |-- <strong>responses </strong>> <i>simulated data with and without noise</i><br>| | |-- <strong>true model </strong>> <i>true model used for simulation</i><br>| |-- <strong>MT </strong>> <i>MT probabilities, sites information and models</i><br>| | |-- <strong>probabilities </strong>> <i>MT-derived probabilities</i><br>| | |-- <strong>responses </strong>> <i>simulated MT data</i><br>| | | |-- <strong>edi_noise_5p </strong>> <i> 5% noise-contaminated data (*.edi files)</i><br>| | | |--<strong> </strong>MansfieldMT_fwd.dat > <i> uncontaminated (ModEM format *.dat file)</i><br>| | |-- <strong>model </strong>> <i>model in ModEM format (*.mod) and WinGLink format (.out) formats</i><br>| | |-- coordinates.txt ><i> location of MT sites</i><br>| |-- <strong>Rock units </strong>> <i>contains the file with indices of the rock unit model, in 3D, of the modified Mansfield model. The indices are stored as a column vector. </i><br><br>|-- <strong>Dataset_field</strong> > <i>field dataset</i><br>| |-- <strong>Mag </strong>> <i>magnetic data and models: inversion and results</i><br>| | |-- <strong>admm constraints </strong>> <i> file with bound constraints used in cases 3, 4, and adjusted case 4.</i><br>| | |-- <strong>data </strong>> <i> magnetic data for inversion, x, y, z, data column format.</i><br>| | |-- <strong>inverted models </strong>> <i> folders containing inversion results for the different cases tested</i><br>| | | |-- <strong>case 1</strong> <br>| | | |-- <strong>case 2</strong><br>| | | |-- <strong>case 3</strong><br>| | | |-- <strong>case 4</strong><br>| | | |-- <strong>case 4 adjusted</strong><br>| |-- <strong>MT </strong>> <i>MT probabilities and sites information</i><br>| | |-- <strong>probabilities </strong>> <i>MT probabilities of interface and rock units (*.txt files)</i><br>| | |-- coord_L26_sites > <i>File containing the location of sites along ligne L26</i></p>
UTHealth - Fundus and Synthetic OCT-A Dataset (UT-FSOCTA)
<p><strong>Introduction</strong></p> <p>Vessel segmentation in fundus images is essential in the diagnosis and prognosis of retinal diseases and the identification of image-based biomarkers. However, creating a vessel segmentation map can be a tedious and time consuming process, requiring careful delineation of the vasculature, which is especially hard for microcapillary plexi in fundus images. Optical coherence tomography angiography (OCT-A) is a relatively novel modality visualizing blood flow and microcapillary plexi not clearly observed in fundus photography. Unfortunately, current commercial OCT-A cameras have various limitations due to their complex optics making them more expensive, less portable, and with a reduced field of view (FOV) compared to fundus cameras. Moreover, the vast majority of population health data collection efforts do not include OCT-A data.</p> <p>We believe that strategies able to map fundus images to en-face OCT-A can create precise vascular vessel segmentation with less effort.</p> <p>In this dataset, called UTHealth - Fundus and Synthetic OCT-A Dataset (UT-FSOCTA), we include fundus images and en-face OCT-A images for 112 subjects. The two modalities have been manually aligned to allow for training of medical imaging machine learning pipelines. This dataset is accompanied by a manuscript that describes an approach to generate fundus vessel segmentations using OCT-A for training (Coronado et al., 2022). We refer to this approach as "Synthetic OCT-A".</p> <p><strong>Fundus Imaging</strong></p> <p>We include 45 degree macula centered fundus images that cover both macula and optic disc. All images were acquired using a OptoVue iVue fundus camera without pupil dilation.</p> <p>The full images are available at the <code>fov45/fundus</code> directory. In addition, we extracted the FOVs corresponding to the en-face OCT-A images collected in <code>cropped/fundus/disc</code> and <code>cropped/fundus/macula</code>.</p> <p><strong>Enface OCT-A</strong></p> <p>We include the en-face OCT-A images of the superficial capillary plexus. All images were acquired using an OptoVue Avanti OCT camera with OCT-A reconstruction software (AngioVue). Low quality images with errors in the retina layer segmentations were not included.</p> <p>En-face OCTA images are located in <code>cropped/octa/disc</code> and <code>cropped/octa/macula</code>. In addition, we include a denoised version of these images where only vessels are included. This has been performed automatically using the ROSE algorithm (Ma et al. 2021). These can be found in <code>cropped/GT_OCT_net/noThresh</code> and <code>cropped/GT_OCT_net/Thresh</code>, the former contains the probabilities of the ROSE algorithm the latter a binary map.</p> <p><strong>Synthetic OCT-A</strong></p> <p>We train a custom conditional generative adversarial network (cGAN) to map a fundus image to an en face OCT-A image. Our model consists of a generator synthesizing en face OCT-A images from corresponding areas in fundus photographs and a discriminator judging the resemblance of the synthesized images to the real en face OCT-A samples. This allows us to avoid the use of manual vessel segmentation maps altogether.</p> <p>The full images are available at the <code>fov45/synthetic_octa</code> directory. Then, we extracted the FOVs corresponding to the en-face OCT-A images collected in <code>cropped/synthetic_octa/disc</code> and <code>cropped/synthetic_octa/macula</code>. In addition, we performed the same denoising ROSE algorithm (Ma et al. 2021) used for the original enface OCT-A images, the results are available in <code>cropped/denoised_synthetic_octa/noThresh</code> and <code>cropped/denoised_synthetic_octa/Thresh</code>, the former contains the probabilities of the ROSE algorithm the latter a binary map.</p> <p><strong>Other Fundus Vessel Segmentations Included</strong></p> <p>In this dataset, we have also included the output of two recent vessel segmentation algorithms trained on external datasets with manual vessel segmentations. SA-Unet (Li et. al, 2020) and IterNet (Guo et. al, 2021).</p> <ul> <li> <p>SA-Unet. The full images are available at the <code>fov45/SA_Unet</code> directory. Then, we extracted the FOVs corresponding to the en-face OCT-A images collected in <code>cropped/SA_Unet/disc</code> and <code>cropped/SA_Unet/macula</code>.</p> </li> <li> <p>IterNet. The full images are available at the <code>fov45/Iternet</code> directory. Then, we extracted the FOVs corresponding to the en-face OCT-A images collected in <code>cropped/Iternet/disc</code> and <code>cropped/Iternet/macula</code>.</p> </li> </ul> <p><strong>Train/Validation/Test Replication</strong></p> <p>In order to replicate or compare your model to the results of our paper, we report below the data split used.</p> <ul> <li> <p>Training subjects IDs: 1 - 25</p> </li> <li> <p>Validation subjects IDs: 26 - 30</p> </li> <li> <p>Testing subjects IDs: 31 - 112</p> </li> </ul> <p><strong>Data Acquisition</strong></p> <p>This dataset was acquired at the Texas Medical Center - Memorial Hermann Hospital in accordance with the guidelines from the Helsinki Declaration and it was approved by the UTHealth IRB with protocol HSC-MS-19-0352.</p> <p><strong>User Agreement</strong></p> <p>The UT-FSOCTA dataset is free to use for non-commercial scientific research only. In case of any publication the following paper needs to be cited</p> <pre><code> Coronado I, Pachade S, Trucco E, Abdelkhaleq R, Yan J, Salazar-Marioni S, Jagolino-Cole A, Bahrainian M, Channa R, Sheth SA, Giancardo L. Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks. Sci Rep 2023;13:15325. https://doi.org/10.1038/s41598-023-42062-9. </code></pre> <p><strong>Funding</strong></p> <p>This work is supported by the Translational Research Institute for Space Health through NASA Cooperative Agreement NNX16AO69A.</p> <p><strong>Research Team and Acknowledgements</strong></p> <p>Here are the people behind this data acquisition effort:</p> <p>Ivan Coronado, Samiksha Pachade, Rania Abdelkhaleq, Juntao Yan, Sergio Salazar-Marioni, Amanda Jagolino, Mozhdeh Bahrainian, Roomasa Channa, Sunil Sheth, Luca Giancardo</p> <p>We would also like to acknowledge for their support: the Institute for Stroke and Cerebrovascular Diseases at UTHealth, the VAMPIRE team at University of Dundee, UK and Memorial Hermann Hospital System.</p> <p><strong>References</strong></p> <pre><code>Coronado I, Pachade S, Trucco E, Abdelkhaleq R, Yan J, Salazar-Marioni S, Jagolino-Cole A, Bahrainian M, Channa R, Sheth SA, Giancardo L. Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks. Sci Rep 2023;13:15325. https://doi.org/10.1038/s41598-023-42062-9. C. Guo, M. Szemenyei, Y. Yi, W. Wang, B. Chen, and C. Fan, "SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation," in 2020 25th International Conference on Pattern Recognition (ICPR), Jan. 2021, pp. 1236–1242. doi: 10.1109/ICPR48806.2021.9413346. L. Li, M. Verma, Y. Nakashima, H. Nagahara, and R. Kawasaki, "IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks," 2020 IEEE Winter Conf. Appl. Comput. Vis. WACV, 2020, doi: 10.1109/WACV45572.2020.9093621. Y. Ma et al., "ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model," IEEE Trans. Med. Imaging, vol. 40, no. 3, pp. 928–939, Mar. 2021, doi: 10.1109/TMI.2020.3042802. </code></pre>
Synthetic Dataset for Nano Trees
<p>This dataset contains synthetic data used to test Nano Trees algorithm.</p>
Synthetic dataset S1
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Synthetic Smart Home Dataset
<p>This dataset is derived from the van Kasteren dataset (van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp08), 2008) using a Markov model. The dataset has been validated with a conformance test, yielding positive results.</p> <p>The dataset comprises three columns:</p> <ol> <li><strong>Day</strong>: Represents the day number, with each row corresponding to a specific day.</li> <li><strong>Activities</strong>: Represents the sequence of activities performed on a given day. The activities are listed in the order they occurred. Each activity is denoted by a number corresponding to a specific action: <ul> <li>1: 'leave house'</li> <li>4: 'use toilet'</li> <li>5: 'take shower'</li> <li>10: 'go to bed'</li> <li>13: 'prepare breakfast'</li> <li>15: 'prepare dinner'</li> <li>17: 'get drink'</li> </ul> </li> <li><strong>Durations</strong>: Represents the duration (in minutes) for each corresponding activity in the "Activities" column.</li> </ol> <p>For example, on Day 1, the sequence of activities is recorded as <code>[4, 4, 10, 4, 1, 4, 4, 10, 4]</code>, with corresponding durations <code>[1, 1, 23.8, 1, 202.03, 1.81, 1, 25.86, 1.29]</code>. This indicates that on Day 1, the individual started by spending 1 minute using the toilet, followed by another 1 minute in the toilet, then 23.8 minutes in bed, and so on.</p>
Dataset for "Ammonia water as a biobased fertiliser: Evaluating agronomic and environmental performance for Lactuca sativa compared to synthetic fertilisers"
<p>This datasent was uploaded as an addition to the works published in "Ammonia water as a biobased fertiliser: Evaluating agronomic and environmental performance for <em>Lactuca sativa</em> compared to synthetic fertilisers". DOI: https://doi.org/10.1016/j.bcab.2023.102907</p> <p>1) The 1st attached file contains the raw and calculated dataset for the greenhouse gas emission monitering from the lettuce pot trials for the different bio-based fertilisers under Belgian pilot plant.</p> <p>2) The 2nd attached file contains the raw and calculated dataset for biomass, nitrogen uptake, nitrogen fertiliser replacement value, nitrogen use efficiency and other agronomic data related to the lettuce pot trials from the different bio-based fertilisers under Belgian pilot plant.</p> <p> </p>
A hyperspectral and multi-angular synthetic dataset of optical properties for waters with varying trophic levels and optical complexity
<p>This synthetic dataset encompasses inherent and apparent optical properties (IOPs-AOPs) alongside associated bio-geochemical constituents. The bio-optical modeling framework benefited from insights and data accumulated over the past three decades, resulting on a comprehensive dataset of in situ IOPs, including diverse water typologies, and enabling the imposition of rigorous quality standards.</p> <p>Employing the Hydrolight scalar radiative transfer equation solver, we generated above-surface and submarine light fields across the specified spectral range at a “true” hyperspectral resolution (1 nm), covering the ultraviolet down to 350 nm, therefore facilitating algorithm development and assessment for present and forthcoming hyperspectral satellite missions. A condensed version of the dataset tailored to twelve Sentinel-3 OLCI bands (400 nm to 753 nm) was crafted. Derived AOPs encompass an array of above- and below-surface reflectances, diffuse attenuation coefficients, and average cosines,</p> <p>A unique feature of our dataset lies in the calculation of AOPs across the complete range of solar and viewing zenith and azimuthal angles as per the Hydrolight default quadrants. This comprehensive directional coverage caters to studies investigating signal directionality, previously lacking sufficient reference data. The dataset is thus the result of 6500000 Hydrolight simulations, distributed in 5000 files, each file encapsulating a specific IOP scenario, ensuring sufficient data volume for each water type represented. In addition, angularly resolved AOPs are incorporated.</p>
A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM): Datasets
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Energy Consumption Synthetic Dataset Generation Through Parametric Analysis for Residential Buildings in Hot Climates
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[Dataset and Software] Altitude-dependent plasma parameter variations of synthetic EISCAT UHF and VHF incoherent scatter spectra calculated from TIE-GCM results
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FIRO_synthetic-ensemble-forecasts dataset v0
<p>Dataset to support initial submission of WRR manuscript 'Synthetic forecast ensembles for evaluating Forecast Informed Reservoir Operations (FIRO)'</p> <p>Code and workflow description are in public GitHub repository: https://github.com/zpb4/FIRO_synthetic-ensemble-forecasts</p>
Synthetic Financial Datasets
<p>Synthetic Financial Datasets</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.