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278 results for “Validated dataset”
Validation dataset for the ICASSP-2024 3D-CBCT challenge
<p>Validation Dataset for the ICASSP-2024 3D-CBCT challenge</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please dowload all files into one folder, and then merge them together with:</p> <pre><code class="language-bash">$ zip -s 0 validate.zip --out validate_unsplit.zip </code></pre> <p>This will create a new zip file, "validate_unsplit.zip" that then you can unzip with your favourite tool, e.g.</p> <pre><code class="language-bash">$ unzip validate_unsplit.zip</code></pre> <p> </p> <p>The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p> </p> <p> </p> <p>Remember that to be part of the challenge you need to register in the webpage above.</p> <p> </p>
Dataset for Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks
<p>Data set for the publication named: "Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks"</p>
MedFS EAS5 Validation Dataset
<p>Datasets used for the validation of the physical component of the Mediterranean Analysis and Forecast center of the EU Copernicus Marine Service for the period 2018-2020.</p><p>The observations used are: </p><ul><li>vertical profiles of temperature and salinity from Argo floats: <a href="https://data.marine.copernicus.eu/product/INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030">https://data.marine.copernicus.eu/product/INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030</a> ) after an internal quality check procedure</li><li>Satellite Sea Level along track data from Copernicus Marine SeaLevel-TAC product: <a href="https://data.marine.copernicus.eu/product/SEALEVEL_EUR_PHY_L3_NRT_OBSERVATIONS_008_059">https://data.marine.copernicus.eu/product/SEALEVEL_EUR_PHY_L3_NRT_OBSERVATIONS_008_059</a> ) after an internal quality check procedure</li><li>SST satellite data from Copernicus Marine SST-TAC product: <a href="https://data.marine.copernicus.eu/product/SST_MED_SST_L4_NRT_OBSERVATIONS_010_004/">https://data.marine.copernicus.eu/product/SST_MED_SST_L4_NRT_OBSERVATIONS_010_004/</a> after interpolation to the 1/24° horizontal resolution; <a href="https://data.marine.copernicus.eu/product/SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012">https://data.marine.copernicus.eu/product/SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012</a> after interpolation to the 1/24° horizontal resolution</li></ul><p>Figures in the paper are provided by using model misfits: model minus observations at the same time and position of the observation before it is assimilated.</p>
Validation dataset of 30 m resolution Landsat-8 fractional snow cover in the Asian Water Tower region (2013-2022)
<p>This dataset is the validation dataset for the article 'MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)', which is a 30m resolution Landsat-8 fractional snow cover dataset for the time period 2013-2022 in the Asian Water Tower Region. This dataset is based on 3046 scene Landsat-8 surface reflectance data and uses the MESMA-AGE algorithm to retrieve the fractional snow cover. Gaofen-2 images with higher resolution were used to evaluate the accuracy, and the results showed that the accuracy was better, with OA of 94.46% and RMSE of 0.094. The cloud cover of each image in this dataset is less than 10% and the snow cover is more than 30%, which can be used to validate medium- or coarse-scale snow products and to study the spatial distribution of snow at high spatial resolution.</p>
Diarrhea etiology prediction validation dataset - Bangladesh and Mali
Open the record for dataset details and reuse information.
Dataset used for PARASOL/GRASP aerosol products validation with AERONET and comparison with MODIS
<p><strong>Dataset used for PARASOL/GRASP aerosol products validation with AERONET and comparison with MODIS </strong></p> <p>1. PARASOL/GRASP vs. AERONET for 2005-2013</p> <ul> <li>AOD at 443, 490, 550, 565, 670, 865 and 1020 nm</li> <li>AE (440/870)</li> <li>AODF and AODC at 550 nm</li> <li>SSA at 440, 675, 870, and 1020 nm</li> <li>AAOD at 550 nm</li> </ul> <p>2. PARASOL/GRASP, PARASOL/Operational, MODIS (DT, DB and MAIAC) vs. AERONET for year 2008</p> <ul> <li>AOD 550 nm</li> <li>AE (440/670) and AE (440/870)</li> <li>AODF and AODC 550 nm</li> </ul> <p>3. Inter-comparison of daily 0.1 degree grided PARASOL and MODIS aerosol products for year 2008</p> <ul> <li>Daily 0.1 x 0.1 degree grided PARASOL and MODIS AOD 550 nm for 2008 <ul> <li>PARASOL/HP; PARASOL/Models; MODIS/DT; MODIS/DB; MODIS/MAIAC</li> </ul> </li> </ul> <p> </p> <p><strong><em>Please follow the data policy of each data source:</em></strong></p> <p>PARASOL/GRASP: GRASP-OPEN (<a href="https://www.grasp-open.com/products/">https://www.grasp-open.com/products/</a>)</p> <p>PARASOL/Operational: ICARE (<a href="http://www.icare.univ-lille1.fr">http://www.icare.univ-lille1.fr</a>)</p> <p>MODIS/DT and DB C6 MYD04_L2: ICARE (<a href="http://www.icare.univ-lille1.fr">http://www.icare.univ-lille1.fr</a>)</p> <p>MODIS/MAIAC MAC19A2: NASA LAADS (<a href="https://ladsweb.modaps.eosdis.nasa.gov">https://ladsweb.modaps.eosdis.nasa.gov</a>)</p> <p>AERONET: <a href="http://www.aeronet.gsfc.nasa.gov">http://www.aeronet.gsfc.nasa.gov</a></p> <p> </p> <p>Details can be found in manuscript:</p> <p>Chen, C., O. Dubovik, D. Fuertes, P. Litvinov, T. Lapyonok, A. Lopatin, F. Ducos, Y. Derimian, M. Herman, D. Tanré, L. A. Remer, A. Lyapustin, A. M. Sayer, R. C. Levy, N. C. Hsu, J. Descloitres, L. Li, B. Torres, Y. Karol, M. Herrera, M. Herreras., M. Aspetsberger, M. Wanzenboeck, L. Bindreiter, D. Marth, A. Hangler, and Federspiel C., Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring, submitted to ESSD, <a href="https://essd.copernicus.org/preprints/essd-2020-224/">https://essd.copernicus.org/preprints/essd-2020-224/</a>, 2020. </p>
Reproducible Validation and Replication Studies in Nanoscale Physics (problem datasets for Rockstuhl et al. 2005 replication)
<p>Problem folders including all the input files necessary to reproduce the computations of the results related to Rockstuhl et al. 2005 on the paper: Reproducible Validation and Replication Studies in Nanoscale Physics</p>
Validity of two automatic artifact reduction software methods in ictal EEG interpretation. Dataset 1
<p>Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). "_out" files have been processed by AR2.</p>
Dataset for Verification and Validation Tests of the Spalart-Allmaras Model in Moltres
<p>This repository contains input files, raw output data, data analysis scripts, and plots for verification and validation tests of the Spalart-Allmaras turbulence model in Moltres. These V&V tests consist of numerical simulations of turbulent channel, pipe, and backward-facing step flows based on the works by Moser et al. (1999), Laufer (1954), and Driver & Seegmiller (1985), respectively.</p>
Replication Package for "VALIDATE: A Deep Dive into Vulnerability Prediction Datasets"
<p>Replication Package for "VALIDATE: A Deep Dive into Vulnerability Prediction Datasets"</p><ul><li>SDR Results</li><li>SDR Queries</li><li>VALIDATE User Guide</li><li>Original Studies References in BibTeX</li></ul>
INTELLIMAN_WP2_Application Requirements and Integration_T2.4_Fresh food handling use case analysis, integration and validation_Apple 6D pose estimation dataset_v0
<p>The dataset contains the data generated for the training of the 6D pose estimation neural network<br>DOPE related to the publication:<br>M. Costanzo, M. De Simone, S. Federico, C. Natale and S. Pirozzi, "Enhanced 6D Pose Estimation for<br>Robotic Fruit Picking," 2023 9th International Conference on Control, Decision and Information<br>Technologies (CoDIT), Rome, Italy, 2023, pp. 901-906, doi: 10.1109/CoDIT58514.2023.10284072.</p>
Dataset for "Validation of SSDE calculation in a modern CT scanner and correlation with effective dose"
<p>Size-Specific Dose Estimate (SSDE) is a size-adjusted dosimetric index that addresses the limitations of the Computed Tomography Dose Index (CTDIvol). This research aims to verify the SSDE generated by a modern Computed Tomography (CT) scanner and examine its relationship with effective dose (E). Sixty CT scans were performed on anthropomorphic phantoms, including models representing pediatric and obese patients, and then analyzed. SSDE values from the CT scanner were compared with those calculated independently using a Python-based method and Radimetrics, a dose monitoring software.</p> <p>The published dataset contains all the CT images and an Excel file with the main parameters given by the CT scanner, alsongside the ones calculated with Python and Radimetrics. </p>
Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology
<p><span>H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we provided deep convolutional neural networks, called MRH-Nets, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. </span><span> </span>The dataset contains multi-contrast MRI and histology used for the training and testing and the acquisition parameters. The datasets have been carefully registered to mouse brain images from the Allen Mouse Brain Atlas (https://mouse.brain-map.org). The source codes for MRH-Nets can be found at <a href="https://github.com/liangzifei/MRH-Net">https://github.com/liangzifei/MRH-Net</a>.</p>
Vasculature Segmentation Validation Dataset - Part IV - Biological Difference Dataset 2/2 (Development)
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications<br> </p> <p><strong>Dataset description</strong>:</p> <p><strong>Development</strong>: Example data of the head vasculature of developing zebrafish.</p> <p><br> <strong>Data acquisition</strong>:<br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><strong>Contact</strong>: kugler.elisabeth[at]gmail.com</p> <p><strong>Useful code links</strong>: </p> <ul> <li>https://github.com/ElisabethKugler/ZFVascularQuantification</li> <li>https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis<br> </li> </ul>
Vasculature Segmentation Validation Dataset - Part III - Biological Difference Dataset 1/2 (Exsanguination)
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications</p> <p><strong>Dataset description</strong></p> <p><strong>Exsanguination</strong>: Example data of the head vasculature of 4dpf of zebrafish. Data acquisition before and after exsanguination by opening of the heart cavity with forceps in Tg(kdrl:HRAS-mCherry)s916 (4dpf; n=16 embryos from 2 experimental repeats).</p> <p><br> <strong>Data acquisition</strong>:<br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><strong>Contact</strong>: kugler.elisabeth[at]gmail.com</p> <p><strong>Useful code links</strong>: </p> <p>https://github.com/ElisabethKugler/ZFVascularQuantification<br> https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis</p>
Vasculature Segmentation Validation Dataset - Part II - Decreasing Contrast-To-Noise Ratio
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications</p> <p><strong>Dataset description:</strong></p> <ul> <li><em><strong>LaserForNoise</strong></em>: Example data of the head vasculature of 4dpf of zebrafish. The data were consecutively acquired with 1.2%, 0.8%, and 0.4% laser power (LP), respectively. This allowed us to produce data with decreasing contrast-to-noise ratio, and thus study robustness over a range of signal distributions.</li> </ul> <p><em><strong>Data acquisition</strong></em>:<br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl: HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide-semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><strong>Contact</strong>: kugler.elisabeth[at]gmail.com</p> <p><strong>Useful code links</strong>: </p> <ul> <li>https://github.com/ElisabethKugler/ZFVascularQuantification</li> <li>https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis</li> </ul>
Vasculature Segmentation Validation Dataset - Part I - Simulated Tubes and Filter Responses
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications</p> <p><strong>Dataset description:</strong></p> <ul> <li><em><strong>SimulatedTubes</strong></em>: Computationally produced tubes (3 different sizes) that are hollow, filled, or filled with Gaussian distribution. To study filter responses, artificial noise and blurring were added.</li> <li><em><strong>DataExFilterResponse</strong></em>: Example data of the head vasculature of 4dpf of zebrafish, where the response to different filters is tested (i.e. GF - general filtering and TF - tubular filtering / Sato enhancement; for TF also different scale sizes are examined; see also: https://www.mdpi.com/2313-433X/5/1/14).</li> </ul> <p><em><strong>Data acquisition:</strong></em><br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><em><strong>Contact</strong></em>: kugler.elisabeth[at]gmail.com</p> <p><em><strong>Useful code links</strong></em>: </p> <ul> <li>https://github.com/ElisabethKugler/ZFVascularQuantification</li> <li>https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis</li> </ul>
Dynamic deformation calculation of articular cartilage and cells using resonance-driven laser scanning microscopy - Deformable Registration Validation Dataset
<p>This dataset includes the supporting input files, scripts, and output files for validation tests of lsmgridtrack v0.3 applied to resonance scanned images. </p>
Training and validation datasets for "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network"
<p>This is training and validation datasets used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". In this manuscript, we propose an efficient deep learning method using a Convolutional Neural Network (CNN) to predict a scalar field from sparse structural data associated with multiple distinct stratigraphic layers and faults. The CNN architecture is beneficial for the flexible incorporation of empirical geological knowledge when trained with numerous and realistic structural models that are automatically generated from a data simulation workflow. It also presents an expressive characteristic of integrating various types of structural constraints by optimally minimizing a hybrid loss function to compare predicted and reference structural models, opening new opportunities for further improving geological modeling. </p>
Dataset for the validation and clinimetric test of the Danish Flourish Index and Secure Flourish Index in a test-retest setup.
<p>This is a dataset used in the initial clinimetric evaluation of the Flourish Index (FI) and Secure Flourish Index (SFI). See the paper published in: for more information.</p> <p>The datasets have been stripped of demographic and personal data for anonymization purposes.</p> <p>The sample is a convenience sample of (healthy) adult Danes with a large body of university students in the sample. The mean age of the sample is 43,9 (sd: 16,3) and 73% were women</p> <p>// Dr. T. K. Stripp</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.