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428 results for “image map”
Code and data associated with the paper 'Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes'
<p>Code and data associated with the paper 'Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes'.</p> <p>See Readme.pdf for data and code details.</p>
OMAP-19 Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Tonsil with IBEX
<p>Representative dataset acquired using the Iterative Bleaching Extends multi-pleXity (IBEX) imaging method described in:</p> <ol> <li>“IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues“, A. Radtke et al., <em>Proc. Natl. Acad. Sci. USA,</em> 2020, <a href="https://doi.org/10.1073/pnas.2018488117">doi.org/10.1073/pnas.2018488117</a>.</li> <li>"IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues", A. J. Radtke, et al., <em>Nat Protoc</em>, 2022, <a href="https://doi.org/10.1038/s41596-021-00644-9">doi.org/10.1038/s41596-021-00644-9</a>.</li> </ol> <p>This dataset accompanies the <a href="https://www.nature.com/articles/s41592-023-01846-7">Organ Mapping Antibody Panel (OMAP) effort</a> led by the <a href="https://commonfund.nih.gov/HuBMAP">Human BioMolecular Atlas Program</a> detailed <a href="https://humanatlas.io/omap">here</a>.</p> <p>OMAP-19 was designed for IBEX imaging of human FFPE tonsil samples sectioned onto glass slides. The panel consists of 6 cycles of 20 primary antibodies, 5 secondary antibodies, and the nuclear label Hoechst for image alignment and nuclear segmentation. This OMAP provides a spatial context for 10 anatomical structures and at least 16 cell types present in the <a href="https://cdn.humanatlas.io/hra-releases/v2.0/docs/asct-b/asct-b-vh-palatine-tonsil.copy.html">ASCT+B tonsil table</a>. OMAP-19 was designed to examine myeloid subsets (CD11b, CD11c, CD14, CD15, HLA-DR) and angiogenesis (VEGF-A and VEGF-C) in human ovarian cancer biopsies. The human tonsil was used as a positive control for antibody validation and panel development. Antigen retrieval was performed using a pressure cooker (Borg Decloaker BD1000, 110°C for 15 minutes) and a tris-based buffer (pH 9.5). Several custom antibodies were created using commercial labeling kits as indicated in the associated RRID entries for these antibodies. More details on the antigen retrieval protocol and step-by-step application of antibodies can be found on the <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/">IBEX Imaging Community knowlege-base</a> (current version) and on <a href="../records/7693279">Zenodo</a> (last official version).</p> <p>Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as a 405 nm laser. All images were captured at an 16-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.379 µm), y (0.379 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software.</p> <p>Image supplied as a .ims Imaris Format file and can be opened with the <strong>free</strong> <a href="https://imaris.oxinst.com/imaris-viewer">Imaris Viewer</a> software or <a href="https://imagej.net/software/fiji/">ImageJ/Fiji</a>. Image channel and antibody meta-data supplied as an xlsx file.</p>
FIGURE 3. Bittacus adult habitus images. a in The genus Bittacus Latreille (Insecta: Mecoptera) in Brazil: key to species, distribution maps, new synonym, and three new species
FIGURE 3. Bittacus adult habitus images. a) B. diversinervis; b) B. femoralis; c) B. latreillei. Scale bars = 1 mm.
FIGURE 1. Bittacus adult habitus images. a in The genus Bittacus Latreille (Insecta: Mecoptera) in Brazil: key to species, distribution maps, new synonym, and three new species
FIGURE 1. Bittacus adult habitus images. a) B. angrensis; b) B. brasiliensis, lectotype; c) B. flavescens, holotype; d) B. geniculatus, holotype. Scale bars = 1 mm. Type specimen photos provided by Michael Ohl (ZMHB).
FIGURE 2. Bittacus adult habitus images. a in The genus Bittacus Latreille (Insecta: Mecoptera) in Brazil: key to species, distribution maps, new synonym, and three new species
FIGURE 2. Bittacus adult habitus images. a) B. aripuanensis; b) B. blancheti; c) B. maculosus. Scale bars = 1 mm.
Madrid light impact impact maps using ISS images
<p>Environmental impact maps of Madrid based on the ISS image ISS030-e-082052 from https://eol.jsc.nasa.gov.</p> <p>Includes:</p> <p>Estimations of the corrections to all Lucas et al. 2013 pigments using the ratios BG and GR. Also, Aubé et al 2013 index.</p> <p>These files have been produced calibrating procedure Sánchez de Miguel 2015 and updated procedures and Sánchez de Miguel et al 2019a and early stages of Sánchez de Miguel et. al 2019b.</p>
Rapid mapping of flood inundation by deep learning-based image super-resolution
<div> <div># Rapid mapping of flood inundation by deep learning-based image super-resolution</div> <div># Developer: Wenke Song</div> <div># The University of Hong Kong</div> <div># Contact email: songwk@connect.hku.hk</div> <div># MIT License</div> <div># Copyright (c) 2024 songwk0924</div> <div> </div> <div>There are two folders in the compressed file: Trained_model and Test_cases:</div> <div>(1) Trained_model</div> <div> model_d_DenseUnet.pth, for predicting the maximum water depth;</div> <div> model_v_DenseUnet.pth, for predicting the maximum velocity.</div> <div> </div> <div>(2) Test_cases</div> <div> Test_d_r1.npy, Test_d_r2.npy, Test_d_r3.npy: Input features for predicting maximum water depth of rainfall events r1-r3;</div> <div> Test_v_r1.npy, Test_v_r2.npy, Test_v_r3.npy: Input features for predicting maximum velocity of rainfall events r1-r3; <div> </div> </div> <div> bathy_mat_5m_0p.csv: Elevation data to create mask layer;</div> <div> Fine_grid_flood_maps (2DSWEs):</div> <div> hmax_r1.asc, hmax_r2.asc, hmax_r3.asc, maximum water depth simulated by 2DSWEs of rainfall events r1-r3;</div> <div> velmax_r1.asc, velmax_r2.asc, velmax_r3.asc, maximum velocity simulated by 2DSWEs of rainfall events r1-r3;</div> <div> </div> <div><span>The aforementioned data will be used as input for model prediction (Prediction.py). </span></div> <div><a name="OLE_LINK902"></a><a name="OLE_LINK909"></a><a href="https://github.com/songwk0924/Flood-inundation-mapping"><span>https://github.com/songwk0924/Flood-inundation-mapping</span></a></div> </div>
Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images
<p>Supplementary materials v2 for the article Unagaev A, Korotkova I and Efremova N. "Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images using phenological, geographic and regional statistical information"<br> </p>
GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
<p>GDCLD : A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images</p> <p>Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., and Catani, F.: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-239, in review, 2024.</p> <p> </p> <p>Data description:</p> <p> </p> <p>The training dataset and the validation dataset are composed of UAV, PlanetScope, Gaofen-6 and Map World images of the 5 earthquake regions of Luding, Nippes, Hokkaido, Jiuzhaigou and Mainling. There is no overlapping area in each TIFF. The training dataset and the validation dataset are randomly divided at a ratio of approximately 0.75:0.25.</p> <p> </p> <p>train_dataset:</p> <p>train_data: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>train_label: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Validation_dataset</p> <p>val_data: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>val_label: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Test_dataset (Lushan, Sumatra, Mesetas and Palu dataset)</p> <p>This package contains the original files of remote sensing images from three sources: UAV, Map World, and PlaneScope belonging to the Lushan, Sumatra, Mesetas and Palu earthquake regiones, which are used to display the test area.<br><br>Future work:<br>The future work includes additional landslide data that the authors will continue to upload. In this 2.0 version update, we have added UAV imagery and interpreted data for loess landslides triggered by the December 2023 M6.2 earthquake in Gansu, China, with a resolution of 0.1 m. Due to authorization constraints, we can only provide PNG files without geographic coordinates. Additionally, this update includes PlanetScope imagery of landslides induced by heavy rainfall in Guangdong, China, in 2024, as well as PlanetScope imagery and landslide labels for events triggered by the Hualien earthquake in Taiwan.<br><br>Please note that this landslide dataset is publicly available exclusively for scientific research purposes and must not be used for commercial purposes.</p>
Stellar reddening map from DESI imaging and spectroscopy
<p>Data for reproducing figures in "Stellar reddening map from DESI imaging and spectroscopy" by Rongpu Zhou et al. 2024.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Mapping surface flaws on float glass through Fourier ptychographic quantitative phase imaging
<p>This dataset contains the 0.7 GP quantitative phase map described in the paper "Mapping surface flaws on float glass through Fourier ptychographic quantitative phase imaging". The phase map can be loaded and visualized in Matlab. Note that the file is large (>20 GB) and requires sufficient RAM to be loaded. The pixel size of the phase map is 542 nm.</p>
Deep and fast label-free Dynamic Organellar Mapping - Imaging data
<p>Imaging data from the article "Deep and fast label-free Dynamic Organellar Mapping", published in Nature Communications by Schessner et al., from Fig. 6, 7 and Supp. Fig. 6c.</p> <p>Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X).</p> <p>Fig6_GLG1_TGOLN2_GALNT2_SuppFig6c_LC3B: Widefield imaging of wild-type HeLa cells cultured for 1h in either: 1) full growth medium (Control); 2) EBSS to starve the cells (Starve); 3) full growth medium plus 100 nM BafA (Control + BafA); or 4) EBSS plus 100 nM BafA (Starve + BafA). Cells were labelled with anti-GALNT2 (Alexa Fluor 488), in combination with either anti-GLG1 (Alexa Fluor 568) or anti-TGOLN2 (Alexa Fluor 680), as shown in Fig. 6, or were labelled with anti-LC3B (Alexa Fluor 488), as shown in Supp. Fig. 6c. In all images, cells were stained with DAPI to label nuclei.</p> <p>Fig7_GALNT2_GLG1_TM9SF2_TGOLN2_GOLIM4_SDF4: Widefield imaging of HeLa cells left untreated in full growth medium (0h) or cultured in the presence of 100 nM BafA for 0.5, 1, 2, 4, 6 or 8 hours, before fixation. Cells were labelled with anti-GALNT2 (Alexa Fluor 647) in combination with either anti-GLG1, anti-TM9SF2, anti-TGOLN2, anti-GOLIM4 or anti-SDF4 (Alexa Fluor 555). In all images, cells were stained with DAPI and phalloidin-488 to label nuclei and cytoplasm, respectively.</p>
Survey2Survey: A deep learning generative model approach for cross-survey image mapping
<p>During the last decade, there has been an explosive growth in survey data and deep learning techniques, both of which have enabled great advances for astronomy. The amount of data from various surveys from multiple epochs with a wide range of wavelengths, albeit with varying brightness and quality, is overwhelming, and leveraging information from overlapping observations from different surveys has limitless potential in understanding galaxy formation and evolution. Synthetic galaxy image generation using physical models has been an important tool for survey data analysis, while deep learning generative models show great promise. In this paper, we present a novel approach for robustly expanding and improving survey data through cross survey feature translation. We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES). This map was used to generate false DES representations of SDSS images, increasing the brightness and S/N while retaining important morphological information. We substantiate the robustness of our method by generating DES representations of SDSS images from outside the overlapping region, showing that the brightness and quality are improved even when the source images are of lower quality than the training images. Finally, we highlight several images in which the reconstruction process appears to have removed large artifacts from SDSS images. While only an initial application, our method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.</p><p>------------</p><p>This repository contains the image files from Survey2Survey: a deep learning generative model approach for cross-survey image mapping. Please cite https://arxiv.org/abs/2011.07124 if you use this data in a publication. For more information, contact Brandon Buncher at buncher2(at)illinois.edu</p><p><strong>--- Directory structure ---</strong></p><p>tutorial.ipynb demonstrates how to load the image files (uploaded here as tarballs). Images were obtained from the SDSS DR16 cutout server (https://skyserver.sdss.org/dr16/en/help/docs/api.aspx) and DES DR1 cutout server (https://des.ncsa.illinois.edu/desaccess/</p><ul><li>./sdss_train/ and ./des_train/ contain the original SDSS and DES images used to train the neural network (Stripe82)</li><li>./sdss_test/ and ./des_test/ contain the original SDSS and DES images used for the validation dataset (Stripe82)</li><li>./sdss_ext/ contain images from the external SDSS dataset (SDSS images without a DES counterpart, outside Stripe82)</li><li>./cae and ./cyclegan contain images generated by the CAE and CycleGAN, respectively. train_decoded/ and test_decoded/ contain the reconstructions of the images from the training dataset and test dataset, respectively. external_decoded/ contain the DES-like image reconstructions of SDSS objects from the external dataset (outside Stripe82).</li></ul>
Native T1 Mapping by Cardiovascular Magnetic Resonance Imaging in Rare Diseases
ClinicalTrials.gov study NCT03199001. IPD Sharing: NO. Countries: 2. Publications: 5.
T1-mapping by Cardiovascular Magnetic Resonance Imaging to Assess Non-Alcoholic Fatty Liver Disease
ClinicalTrials.gov study NCT04220450. IPD Sharing: NO. Countries: 1. Publications: 1.
Assessment of Magnetic Resonance Imaging Neurography and Tractography for Preoperative Mapping of Pelvic Nerves
ClinicalTrials.gov study NCT02582346. IPD Sharing: Not stated. Countries: 1. Publications: 12.
Deep Learning for Fluorescein Angiography and Optical Coherence Tomography Macular Thickness Map Image Translation
ClinicalTrials.gov study NCT05105620. IPD Sharing: NO. Countries: 1. Publications: 1.
Parametric Mapping in Paediatric Magnetic Resonance Imaging
ClinicalTrials.gov study NCT04068987. IPD Sharing: NO. Countries: 1. Publications: 4.
Lymphatic Mapping for Image Guided Radiotherapy in Patients With LACC - a Feasibility Study
ClinicalTrials.gov study NCT05746156. IPD Sharing: NO. 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.