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68 results for “Image fusion”
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Imaging Flow Citometry Data
<p>Imaging flow citometry (IFC) datasets analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>The folders contain acquisitions of giant unilamellar vesicles (GUVs) for lipid exchange and content exchange controls, with file naming convention DATE_SAMPLE_REPLICATE.rif, content exchange is indicated by CE samples in the 20230228_CE.zip folder, lipid exchange by LE samples in the 20221222_LE.zip folder. 24 samples per set are included, triplicates of isolated P1 (DOPE Af488 0.6% in LE; Dex-Af488 40 uM for CE), P2 (DOPE Cy50.6% in LE; Dex-Af647 10 uM for CE), NC (P1 + P2 1:1), PC (DOPE Af488 0.3% + DOPE Cy5 0.3 in LE; Dex-Af488 20 uM + Dex-Af647 5 uM for CE), and M samples numbered 1 to 4, prepared by mixing P1, P2 and PC in different ratios (M1= 1:1:1; M2= 1:1:0.5; M3= 1:1:0.1; M4= 1:1:0.05).</p> <p>Only .rif files are provided, they have to be elaborated via compensation and application of an analysis template using the Amnis IDEAS software. Compensation matrices for lipid exchange (20230217_LEcom.ctm) and content exchange (20230217_CEcomp.ctm) are included, as well as the analysis template (Lipid_exchange_analysis_6.2.ast). Gating in the latter may have to be adjusted to analyse LE and CE experiments.</p> <p>10000 objects in the GUV population or 50000 objects in total were acquired in each file. The files were elaborated in batch mode, outputting the statistic reports (Statistics report CE.txt for CE; Statistics report LE.txt for LE) that were elaborated using an R scirpt (included, IFC_analysis.R) </p>
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Microscopy Data
<p>Microscopy dataset of multipoint-multichannel images of giant unilamellar vesicles (GUVs) suspensions analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>Three folders concerning different sections of the work are included. "preliminary analysis.zip" contians the raw files and analysis scripts for recall computation and imaging setup optimization as described in the paper. Timelapse data was excluded due to file size restrictions (available upon request at the corresponding authors of the work). "IFC comparison.zip" contains raw files and analysis scripts used to optimize colocalization computation in lipid exchange and content exchange experiments. "GUV fusion analysis" contains raw files and analysis scripts for the quantification of lipid and content exchange upon sodium chloride-induced aggregation.</p> <p>Further details on the analysis are provided in the paper. The R scripts require files saved upon analysis of the raw files by the ImageJ macro "CE_analysis_CPU.ijm" included here. The R environment of the complete analysis are included in each folder to provide easier access to the elaborated data.</p>
Photochromic fluorophores enable imaging of endogenous fusion constructs in Candida albicans
<p>10. Gcn5_Stat : Main dataset for figure 2 stationary phase of the manuscript.</p> <p>5., 6. and 7.Gcn5_Stat are additional datasets for figure 2 stationary phase.</p> <p>11. SC5314_Stat : main negative control dataset for figure 2 depicted in supplementary figure 1.</p> <p>14. Gcn5_Exp : Main dataset for figure 2 exponential phase of the manuscript.</p> <p>12., 13. Gcn5_Exp are additional datasets for figure 2 exponential phase.</p> <p>15. SC5314_Exp : main negative control dataset for figure 2 depicted in supplementary figure 1.</p> <p>___________________________________________________________________________________________________</p> <p>2. Erg11 and 7. Erg11 are main datasets for figure 3 of the manuscript.</p> <p>15. SC5314 (Erg11 control) : negative control dataset for figure 3 depicted in supplementary figure 3.</p>
X-ray tomography (CT) image data of tungsten fusion energy heat exchange components
<p>X-ray tomography (CT) image data of tungsten fusion energy heat exchange components.</p> <p>The dataset includes images of four samples:</p> <ul> <li>CCFE_MB_ROI (Culham Centre for Fusion Energy thermal break concept monoblock, region of interest sample)</li> <li>IPP_Wf-Cu (Max-Planck-Institut für Plasmaphysik tungsten fibre / copper matrix coolant pipe)</li> <li>ITER_HHFT_ROI (ITER reference monoblock which has undergone high heat flux testing, region of interest sample)</li> <li>ITER_MB_ROI (ITER reference monoblock, region of interest sample)</li> </ul> <p>This data was used originally for the following publication (please cite if re-using the data) where further details on the data may be obtained:</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Each of the sample directories include reconstructed slices in Tiff format. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). CCFE_MB_ROI also includes raw radiographs; scan & reconstruction parameter settings file.</p> <p>A Neutron CT version of this data is available for comparison: <a href="https://doi.org/10.5281/zenodo.3533418">https://doi.org/10.5281/zenodo.3533418</a></p> <p>Image-based simulation (IBSim) meshes were generated directly from these datasets: <a href="https://doi.org/10.5281/zenodo.3533422">https://doi.org/10.5281/zenodo.3533422</a></p>
Neutron tomography (CT) image data of tungsten fusion energy heat exchange components
<p>Neutron tomography (CT) image data of tungsten fusion energy heat exchange components.</p> <p>The dataset includes three sets of images:</p> <ul> <li>ITER_171T-WA-0002_MB (ITER reference monoblock)</li> <li>CCFE_ThBr_MB (Culham Centre for Fusion Energy thermal break concept monoblock)</li> <li>ROIsamples_Stack (A stack of four region of interest samples*)</li> </ul> <p>The region of interest samples within the stack are as below:</p> <ul> <li>CCFE_ThBr_ROI (Culham Centre for Fusion Energy thermal break concept monoblock)</li> <li>IPP_Wf-Cu_p5_s1 (Max-Planck-Institut für Plasmaphysik tungsten fibre / copper matrix coolant pipe)</li> <li>ITER_HHFT_ROI (ITER reference monoblock which has undergone high heat flux testing)</li> <li>ITER_17IT-WA-0002_ROI (ITER reference monoblock)</li> </ul> <p>This data was used originally for the following publication (please cite if re-using the data) where further details on the data may be obtained:</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Each of the sample directories include reconstructed slices in Tiff format. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads).</p> <p>CCFE_ThBr_ROI and ROIsamples_Stack include raw radiographs; dark and flat field images; scan & reconstruction parameter settings file.</p> <p>ITER_171T-WA-0002_MB includes data relating to the modulation transfer function (MTF) measurement.</p> <p>An X-Ray CT version of the ROI data is available for comparison: <a href="https://doi.org/10.5281/zenodo.3533420">https://doi.org/10.5281/zenodo.3533420</a></p> <p>Image-based simulation (IBSim) meshes were generated directly from these datasets: <a href="https://doi.org/10.5281/zenodo.3533422">https://doi.org/10.5281/zenodo.3533422</a></p>
Image-based deformation measurements for validation of fusion divertor armour under high heat flux loading
<p>This data set contains all the images, DIC data and python scripts and FE input files that allows the reproduction of all results in the paper "Image-based deformation measurements for validation of fusion divertor armour under high heat flux loading" that this data is linked to.</p>
Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'
<p><strong>Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>.<br> The licences valid for the elements of this repository are discussed under point "3. Licenses" below.</p> <p><strong>1. Structure</strong></p> <p>The repository contains the following items:</p> <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>We now focus on the structure of the file CODE_AND_RESULTS.zip.<br> It contains the following items:</p> <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>Inside the folder "new_methods," the following sub-folders are provided:</p> <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> <p>In our experiments, we used two datasets:</p> <p>"WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.<br> The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>].</p> <p><strong>2. References</strong></p> <p>[<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, <em>IEEE Access</em> 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>. <br> [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>.<br> [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em> 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>. <br> [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</em> (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>. <br> [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: <em>Proceedings of the IEEE/CVF International Conference on Computer Vision</em> (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>.</p> <p><strong>3. Licenses</strong></p> <p>The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip":</p> <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><strong>4. Contact</strong></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn<br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn</p> <p>Institute of Applied Optimization, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China<br> </p>
Supplementary data: Bayesian multi-exposure image fusion for robust high dynamic range ptychography
<p>Accompanying supplementary data for the paper. To download this data automatically and use the software, please refer to the details in the README of the linked github repository. </p> <p><strong>Github URL: </strong><a href="https://github.com/microscopic-image-analysis/bayes-mef"><strong>https://github.com/microscopic-image-analysis/bayes-mef</strong></a></p>
Intraoperative Rapid Diagnosis of Glioma Based on Fusion of Magnetic Resonance and Ultrasound Imaging
ClinicalTrials.gov study NCT05656053. IPD Sharing: NO. Countries: 1. Publications: 1.
Fusion of Single and Integral Multispectral Aerial Images
<p>Abstract: <span>An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occluded features visible. We present a first and hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images with the ones from integral aerial images that are the result of synthetic aperture sensing for removing occlusion. It combines the environment’s spatial references with features of unoccluded targets that would normally be hidden by dense vegetation. Our method outperforms state-of-the-art two-channel and multi-channel fusion approaches visually and quantitatively in common metrics, such as mutual information, visual information fidelity, and peak signal-to-noise ratio. The proposed model does not require manually tuned parameters, can be extended to an arbitrary number and arbitrary combinations of spectral channels, and is reconfigurable for addressing different use cases. We demonstrate examples for search and rescue, wildfire detection, and wildlife observation.</span> <strong><br></strong></p>
Arabidopsis guard cell vacuoles imaged after the induction of vacuole fusion
<p>Wild type Arabidopsis thaliana plants from the Columbia-0 ecotype were grown in soil at 22C with a 16 h photoperiod. Leaves from 4-week old plants were cut in the morning and immediately processed to generate epidermal peels. Briefly, a small leaf fragment was applied abaxially to a coverslip coated with medical adhesive and all but the bottommost layers of abaxial cells containing the stomata were scrapped away with a razor. 1 mm thick silicone isolators (GraceBio #664170) were used to create wells around the adhesive for incubations. Epidermal peels were immediately incubated in stomata buffer (MES pH 6.1). To induce stomatal closure, peels were incubated in closing buffer (10 mM MES pH 6.1, 40 mM malate, 5 mM CaCl2, 10 µM BCECF, 50 mM ABA (Sigma Aldrich A1049) at 22C in the dark for two hours. To induce stomatal opening or vacuole fusion, ABA-treated peels were incubated in opening buffer (10 mM MES pH 6.1, 50 mM KCl) supplemented with 10 µM BCECF (Fisher Scientific B1170) and either 3 µM fusicoccin (Sigma F0537) or 33 µM wortmannin (Sigma W3144). Wortmannin-treated peels were kept in the dark, while fusicoccin-treated peels were exposed to the light for up to 1 h. All concentrated stocks were first dissolved in DMSO. Images of leaf epidermal peels were acquired after ABA incubation and after 20, 40 and 60 min of fusicoccin or wortmannin treatments. <br><br>Confocal laser scanning microscopy was carried out in a Zeiss LSM 980 confocal microscope. Images were taken with a 40× FCS water objective (1.1 N.A.). Acquisition of BCECF fluorescence was accomplished with 405 nm laser excitation and 495-550 nm emission filter set. Images were acquired with an Airyscan detector with a pinhole size of 2.5 airy units.</p>
DAVE - Image and Pointcloud data for Fusion
<p>The dataset was collected during a measurement run on the river Trave in Lübeck, Germany. The data consists of two main components: images and pointclouds from a LiDAR sensor.</p> <p>Pointcloud data was recorded using an RoboSense RS-LIDAR-M1.</p> <p>Image data was recorded using a Hik Vision DS-2CD2T47G2-LSU/SL camera.</p> <p> </p> <div> <div> <div> <p>This publication is a result of the research of the Center of Excellence CoSA and funded by the Federal Ministry for Digital and Transport of the Federal Republic of Germany (Id 19F2225C, DAVE).</p> <p> </p> <p>Project website: https://www.th-luebeck.de/cosa/projekt/dave/</p> </div> </div> </div>
The features of Tissues and Patches for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model"
<p>This repository contains features and corresponding coordinates of patches and tissues extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into The histology images were classified as “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) according to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer”, which corresponds to the division of the training and test sets in the article.<br><br></p> <p>Patches were extracted at 40x objective magnification and 20x objective magnification, respectively, and the corresponding features were extracted by pre-training resnet48, respectively</p> <p>The features of Tissues are thumbnails obtained at 2.5x objective magnification and further extracted by MedSAM after extracting the masks of the tissues.</p>
Cell features for "Datasets for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model""
<p>This repository contains features and corresponding coordinates of cells extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) and corresponded to the article dividing the training and test sets.<br><br></p> <p>The features of all cells were extracted by Hovernet and Transnuseg at 40x objective magnification for extraction masking and further feature extraction</p>
Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics
<p>This is the data set for experiments of satellite images of Oroville dam site in paper: Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics. Inside the zip file, there are 3 folders: 'HD-IMG-Database-Landsat-8', 'HD-IMG-Database-Landsat-8-Qest' and 'MODIS_250'.</p>
Vacuole fusion in fluorescent images training data
<p>Fluorescent image training data for classification tasks associated with the paper ‘<strong><em>Automated quantification of vacuole fusion and lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning</em></strong>’ https://doi.org/10.1101/2023.02.27.530171</p> <p>All images contain a ROI with FM4-64 and BODIPY 493/503 channels. Images are placed in folders corresponding to their respective classes.</p>
Localization files for "Imaging proteins sensitive to direct fusions using transient peptide-peptide interactions"
<p>Localization csv files for super-resolution images taken and analysed for "Imaging proteins sensitive to direct fusions using transient peptide-peptide interactions". Single localizations were processed using the Peak Fit function of the Fiji (Java 8 2017 release) GDSC SMLM plugin, using a signal strength threshold of 30, a minimum photon threshold of 100, and a precision threshold of 15-30 nm (precision threshold used is indicated in the file name). </p>
Study of Automatic Image Fusion of a CT Volume With Ultrasound During Percutaneous Ablation Treatment of Hepatic Tumors
ClinicalTrials.gov study NCT04420026. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Fusion Imaging in Endovascular Thrombectomy for Acute Ischemic Stroke
ClinicalTrials.gov study NCT06975696. IPD Sharing: NO. Countries: 2. Publications: 10.
Suprasacral Parallel Shift - Ultrasound/MR Image Fusion Guided Lumbosacral Plexus Block
ClinicalTrials.gov study NCT02593370. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.