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38,240 results for “image”
Synthetic images of cell nuclei in widefield microscopy
<p>The images were generated by <a href="http://www.cs.tut.fi/sgn/csb/simcep/tool.html">SIMCEP</a>, a widefield fluorescence microscopy biological images simulator.</p> <p>The dataset is used to demonstrate the execution of image analysis workflows with BIAFLOWS on a local machine from a jupyter notebook.</p>
Sky images recorded during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Scattered sunlight measurements can be strongly affected by clouds, mainly due to multiple scattering effects that give rise to large uncertainties in the light path retrieval. In order to estimate cloud cover, we recorded images of the sky every 5 minutes to evaluate a cloud index, from 0 (clear sky) to 10 (completely overcast).</p> <p>This dataset presents the sky images in PNG (Portable Network Graphics) format recorded on board the R/V Akademik Tryoshnikov during the austral summer of 2016/17 as part of the circumnavigation expedition (ACE). Data coverage is from December 2016 until April 2017.</p> <p>These images form a supporting dataset to optical spectroscopy data (Benavent et al., 2020; DOI 10.5281/zenodo.3827443).</p> <p>The ship’s position can be matched with these images using the corrected cruise track (Thomas and Pina Estany, 2019; DOI: 10.5281/zenodo.3483166) or the GPS data provided with the spectroscopy data set (Benavent et al., 2020; DOI 10.5281/zenodo.3827443).</p> <p><strong>Dataset contents</strong></p> <ul> <li>YYYY-MM-DD_hh.mm.ss.png, data file, portable network graphics</li> <li>README.txt, metadata, text</li> </ul> <p>where YYYY-MM-DD_hh.mm.ss is the date and time at which the file was saved in UTC.</p> <p><strong>Dataset license</strong></p> <p>This dataset of raw optical sky images from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Simulation of an imaging calorimeter to demonstrate GarNet on FPGA
<p>This data set is an output of a simulation of electrons and pions shot at a chunk of an imaging calorimeter. It is used in the case study for GarNet-on-FPGA, documented in <a href="https://arxiv.org/abs/2008.03601">arXiv:2008.03601</a>.</p> <p>Each HDF5 file contains the following arrays:</p> <p> Name | Shape | Description</p> <ul> <li>cluster | (10000, 128, 4) | Samples for training and inference. Outermost dimension is the event (cluster). Each cluster has maximum 128 hits, each of which has four features: x, y, z, and energy. The coordinates of the hits are in cm. The energy is in GeV. The x and y coordinates are relative to the seed hit, while the z coordinate is with respect to the calorimeter front face.</li> <li>size | (10000) | Number of hits in each cluster. The cluster array is zero-padded when the cluster size is below 128.</li> <li>truth_pid | (10000) | Identity of the primary particle (0: electron, 1: pion).</li> <li>truth_energy | (10000) | True energy of the primary particle.</li> <li>raw | (10000, 4375, 2) | Raw data (actual output of the simulation). For each event (outermost dimension), hit energy and primary fraction (innermost dimension indices 0 and 1) are given for each of the 4375 sensors. Energy is in MeV.</li> <li>coordinates | (4375, 3) | The x, y, and z coordinates of the 4375 sensors, to be used to interpret the raw data.</li> </ul> <p>See the paper for the details of the simulation.</p>
Synchrotron diffraction images for the 2.9 Å crystal structure of L-Selenomethionine labeled human GDAP1
<p>Dataset collected at DLS, I04 beamline 16.5.2019. L-SeMet substituted crystals collected with SAD-method.</p> <ul> <li>Flux: 1.32e+11</li> <li>Ω Start: 0.0°</li> <li>Ω Osc: 0.10°</li> <li>Ω Overlap: 0°</li> <li>No. Images: 3600</li> <li>Resolution: 2.90Å</li> <li>Wavelength: 0.9790Å</li> <li>Exposure: 0.040s</li> <li>Transmission: 100.00%</li> <li>Beam size: 63x50μm</li> <li>Type: SAD</li> <li>Comment: X,Y,Z (-561,302,301), Aperture: Large</li> </ul> <p> </p>
Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset
<p>This dataset contains data used in the research published by MLabs Optronics in the paper:</p> <p>Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018.</p> <p><br> The dataset is classified into 3 folders:</p> <p>- IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.<br> <br> - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS".</p> <p>- VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).</p> <p> </p> <p>Mlabs Optronics<br> PTA<br> Calle Pierre Laffitte, 8<br> 29590 Málaga (Spain)</p> <p>www.mlabsoptronics.com<br> info@mlabsoptronics.com</p>
Iterative Bleaching Extends multi-pleXity (IBEX) imaging method, mouse spleen
<p>This dataset was acquired using the Iterative Bleaching Extends multi-pleXity (IBEX) imaging method described in: “IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues“, A. Radtke et al., 2020, <a href="https://doi.org/10.1073/pnas.2018488117">doi:10.1073/pnas.2018488117</a>.</p> <p>It is comprised of a three cycle IBEX experiment performed on mouse spleen sections labeled with the nuclear marker JOJO-1 and membrane label CD4 AF594. 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 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 mm), y (0.284 mm), and z (1 mm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p> </p> <p>Markers per channel in each of the three cycles:</p> <ol> <li>spleen_panel1.nrrd (6 channels): B220 PE, CD8 BV421, IgD AF700, CD4 AF594, JOJO, Foxp3 eF660</li> <li> <p>spleen_panel2.nrrd (7 channels): CD169 PE, F480 BV421, MHCII AF700, CollIV AF488, JOJO, CD11c AF647, CD4 AF594</p> </li> <li> <p>spleen_panel3.nrrd (7 channels): CD31 PE, CD68 BV421, Ki67 AF700, CD45 AF488, CD4 AF594, JOJO, CD3 AF647</p> </li> </ol> <p>The panels can be registered using the code available on github: <a href="https://github.com/niaid/sitk-ibex">https://github.com/niaid/sitk-ibex</a></p> <p>To view these multi-channel images, in <a href="http://teem.sourceforge.net/nrrd/format.html">nrrd format</a>, use the <a href="https://imagej.net/Fiji">Fiji viewer</a>. The data is stored in XYZC order.</p>
Dataset of cracks on DIC images
<p>This dataset contains crack images and corresponding annotated ground truth masks. This data was used to train, validate, and test a deep convolutional neural network to detect crack pixels on images taken as input for the digital image correlation (DIC) method. </p> <p>For more information about the trained network, please refer to our publication at this <a href="https://www.sciencedirect.com/science/article/pii/S095006182032479X?via%3Dihub">link</a>. </p> <p>The source codes to reproduce the results are shared at this <a href="https://github.com/amirrezaie1415/Deep-DIC-Crack">link</a>.</p> <p>Please cite the following articles:</p> <blockquote> <p>[1] Rezaie, A., Achanta, R., Godio, M., & Beyer, K. (2020). Comparison of crack segmentation using digital image correlation measurements and deep learning. Construction and Building Materials, 261, 120474. doi:https://doi.org/10.1016/j.conbuildmat.2020.120474</p> <p>[2] Rezaie, A., Godio, M., & Beyer, K. (2021). Investigating the cracking of plastered stone masonry walls under shear–compression loading. Construction and Building Materials, 306, 124831.<br> doi:https://doi.org/10.1016/j.conbuildmat.2021.124831</p> </blockquote>
Umāmaheśvara image inscription from Tepe Skandar
<p>Umāmaheśvara image inscription from Tepe Skandar, from <em>Archaeological Survey of Kyoto University in Afganistan 1970</em>, Kyoto Daigaku Chūō Ajia Gakujutsu Chōsatai. / [Kyoto] Committee of Kyoto University Archaeological Mission to Central Asia (Kyoto 1972).</p>
Diffraction images used to solve the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target"
<p>Raw diffraction images used for generating the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target" (available <a href="https://doi.org/10.1073/pnas.2013620117">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries. Datasets 6ZJ1 and 6ZJ5 were cut anisotropically using STARANISO, other datasets were processed isotropically.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Data set and code supporting Marshall et al., "An inventory of online reptile images"
<p>Data set and code supporting: MARSHALL, B.M., FREED, P., VITT, L.J., BERNARDO, P., VOGEL, G., LOTZKAT, S., FRANZEN, M., HALLERMANN, J., SAGE, R.D., BUSH, B. and DUARTE, M.R., 2020. An inventory of online reptile images. <em>Zootaxa</em>, <em>4896</em>(2), pp.251-264. DOI:<a href="https://doi.org/10.11646/zootaxa.4896.2.6">10.11646/zootaxa.4896.2.6</a></p> <p>Data includes: </p> <ul> <li>Supplementary Table 1. List of all species and the number of photos in each of the 6 repositories: "SuppData1_Species_Photo_Count_Table_2020-08-04_no_syn.csv"</li> <li>Supplementary Table 2. List of species without photo in any of the 6 repositories: "SuppData2_Species_no_photos.csv"</li> <li>Supplementary Table 3. Per country summary data of number of species present and number with images: "SuppData3_Country_species_counts.csv"</li> <li>Reptile Database species checklist: "reptile_checklist_2020_04.csv"</li> <li>Reptile Database species synonyms used in second Wikimedia search: "reptile names 2019 syno.csv"</li> </ul> <p>Code includes:</p> <ul> <li>R code used to retrieve Flickr photograph metadata: "SuppCode1_Flickr_search.R"</li> <li>R code used to retrieve Wikimedia photograph metadata: "SuppCode2_Wikimedia_query.R"</li> <li>R code used to retrieve HerpMapper photograph metadata: "SuppCode3_HerpMapper_search.R"</li> <li>R code used to generate figures: "SuppCode4_Figure Generation.R"</li> </ul> <p>Also includes Zootaxa supplementary table.</p> <p> </p>
Diffraction images used to solve the structures published in the article "An Epoxide Intermediate in Glycosidase Catalysis"
<p>Raw diffraction images used for generating the structures published in the article "An Epoxide Intermediate in Glycosidase Catalysis" (available <a href="https://doi.org/10.1021/acscentsci.0c00111">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images used to solve the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-alpha-1,2-Mannanases."
<p>Raw diffraction images used for generating the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-a-1,2-Mannanases" (available <a href="https://doi.org/10.1002/chem.201800435">here</a>). Full single-crystal datasets are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images used to solve the structures published in the article "Contribution of Shape and Charge to the Inhibition of a Family GH99 endo-α-1,2-Mannanase"
<p>Raw diffraction images used for generating the structures published in the article "Contribution of Shape and Charge to the Inhibition of a Family GH99 endo-α-1,2-Mannanase" (available <a href="https://doi.org/10.1021/jacs.6b10075">here</a>). Full single-crystal datasets are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images used to solve the structures published in the article "A Family of Dual-Activity Glycosyltransferase-Phosphorylases Mediates Mannogen Turnover and Virulence in Leishmania Parasites"
<p>Raw diffraction images used for generating the structures published in the article A Family of Dual-Activity Glycosyltransferase-Phosphorylases Mediates Mannogen Turnover and Virulence in Leishmania Parasites" (available <a href="https://doi.org/10.1016/j.chom.2019.08.009">here</a>). The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images used to solve the structures published in the article "From 1,4-Disaccharide to 1,3-Glycosyl Carbasugar: Synthesis of a Bespoke Inhibitor of Family GH99 Endo-α-mannosidase"
<p>Raw diffraction images used for generating the structures published in the article "From 1,4-Disaccharide to 1,3-Glycosyl Carbasugar: Synthesis of a Bespoke Inhibitor of Family GH99 Endo-α-mannosidase" (available <a href="https://doi.org/10.1021/acs.orglett.8b03260">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries. An additional 720 degree dataset is provided, which has been collected from the same crystal as PDB 6HMH. This dataset has not been used to solve the structure presented in the paper. It works very well as an example of sulfur SAD phasing.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images of crystals of the first and second spectrin repeats (mutant C420A/C435A) of human plectin (PDB code 2ODV): 2-wavelength SeMet MAD dataset
<p>Diffraction images of SeMet labeled crystals of a fragment of human plectin that includes the first and second spectrin repeats (SR1-SR2) of the plakin domain. The two Cys in the wild type sequence were replaced by Ala.</p> <p>This Se-Met MAD dataset was used for the <em>de novo</em> phasing of the pdb entry 2ODV (http://www.rcsb.org/pdb/explore/explore.do?structureId=2ODV).</p> <p> </p> <p>Data was collected at the BM14 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using a Mar CCD detector. Data from the same crystal were collected at two wavelengths :</p> <ul> <li>Remote wavelength (0.9185 Å): 180 images (1 degree oscillation per image).</li> <li>Peak wavelength ( 0.9785 Å): 360 images (1 degree oscillation per image).</li> </ul>
Pythia Generated Jet Images with Alternative Rotation Scheme for Location Aware Generative Adversarial Network Training
<p>Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927].</p> <p><strong>Format</strong>:</p> <p>HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (300000, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., <em>Jet-Images -- Deep Learning Edition </em>[arXiv:1511.05190]</li> <li>scikit-image==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Bodhgayā Sanctum Image Inscription of Pīṭhīpati Jayasena
<p>Facsimiles of the Bodhgayā Sanctum Image Inscription of Pīṭhīpati Jayasena. The inscription is arranged in four blocks on the pedestal of a larger-than-life sculpture of the Buddha in bhūmisparśa mudrā, presently housed in the sanctum of the Mahābodhi temple at Bodhgayā. Blocks A, B, C and D shown from archive photograph, C’ and D’ from archive photograph of an inked rubbing. Both photographs originally belonged to (and were presumably made) by Alexander Cunningham and are presently at the British Museum. Cropped, enhanced and rearranged by author. Courtesy of the Trustees of the British Museum.</p> <p>The inscription is re-edited from these facsimiles by Dániel Balogh in <em>Precious Treasures from the Diamond Throne</em> (British Museum Press, London, 2021, p. 43; ISBN 978 9780861592289)</p> <p>See also Tsukamoto, Keisho. 1996. A Comprehensive Study of the Indian Buddhist Inscriptions. Part I. Text, Notes and Japanese Translation. Kyoto: Heirakuji Shoten, pp. 146–47 (I. Bodh-Gayā 29).</p>
Pre-trained models for segmentation and tracking of Coronal Bright Fronts from SDO AIA Base Difference images
<p>Here we present pretrained U-NET-based models followed by SDO AIA Base Difference(BD) validation set after intensity tresholding [-50;150] with predicted feature masks samples. <br>We provide a command-line Python utility for image segmentation using our CNNs designed to process images of solar eruptive phenomena. The https://gitlab.com/iahelio/helios_cnn repository includes regularly updated and newly published models. </p> <p>First model we present is designed to predict the likelihood of each pixel belonging to a certain class or feature in the solar image. A probabilistic output allows for a more nuanced interpretation of ambiguous region. The output can be converted into binary masks through thresholding. The range of values also gives insights into the model's confidence</p> <p>We also present sample segmentation results and the second model designed to produce binary masks.</p>
Sidescan Sonar Substrate, Depth, and Shadow Image-Label-Pairs
<p>Substrate, depth, and shadow image-label pairs used to train side scan sonar segmentation models v1.0 implemented in PINGMapper v2.0.</p><p> </p><p>Images were labeled with <a href="https://github.com/Doodleverse/dash_doodler">Doodler</a> and <a href="https://www.makesense.ai/">Make Sense.</a></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.