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38,240 results for “Imaging”
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE) - Raw images for the analysis of stomatal density and length 2021-2022
Stomatal density and length were measured on leaves of sugar maple (Acer sacharrum Marsh.) and yellow birch (Betula alleghaniensis Britton.) trees in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaves were collected in late July and early August in 2021 and 2022 from the tops of dominant and codominant trees using a shotgun. These measurements were made on 3 leaves from each tree. These data correspond with other foliar trait data collected from the same trees in 2021 and 2022. That EDI package is as follows: Hong, S.D., K.E. Gonzales, C.R. See, and R.D. Yanai. 2021. MELNHE: Foliar Chemistry 2008-2016 in Bartlett, Hubbard Brook, and Jeffers Brook (12 stands) ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/b23deb8e1ccf1c1413382bf911c6be19 This data package contains the raw images underlying the data reported in a separate data package on stomatal density and length: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=372 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Deep Image Reconstruction
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Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of tobacco seedlings
<p>Mass spectrometry imaging (MSI) data set in imzML format, of complete tobacco (<em>Nicotiana tabacum</em>) seedling using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to nicotine shows accumulation in the roots and at the borders of leaves.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of San Pedro cactus
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from San Pedro cactus (<em>Echinopsis pachanoi</em>) cross-section using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to mescaline shows a star-like distribution of this interesting alkaloid.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</p> <p> </p>
A set of allsky camera images from latitude 55 degrees North
<p>A set of R G and B images from an allsky camera situated in Copenhagen, Denmark. The images have been darksubtracted and split into these 16-bit FITS format images. Each image is the sum of something like 9 PNG images which each, originally, were 14 bit images from a ZWO camera cmos detector.</p>
Synthetic spherical tissue images
<p>A time-lapse sequence of synthetic cell membrane images along with their segmentations. Cell-lineages associating cell labels from consecutive time points are also provided.</p> <p> </p> <p><strong>File information:</strong></p> <p>Image files are in the .inr.gz format, which can be read for instance using the <strong>timagetk</strong> (<a href="https://gitlab.inria.fr/mosaic/timagetk">https://gitlab.inria.fr/mosaic/timagetk</a>) Python library. </p>
Artefact segmentation in digital pathology whole-slide images
<p>Dataset with examples of Artefacts in Digital Pathology.</p> <p>The dataset contains 22 Whole-Slide Images, with H&E or IHC staining, showing various types and levels of defect to the slides. Annotations were made by a biomedical engineer based on examples given by an expert.</p> <p>The dataset is split in different folders:</p> <ul> <li>train <ul> <li>18 whole-slide images (extracted at 1.25x & 2.5x magnification)</li> <li>All from the same Block (colorectal cancer tissue)</li> <li>1/2 with H&E & 1/2 with anti-pan-cytokeratin IHC staining.</li> </ul> </li> <li>validation <ul> <li>3 whole-slide images (1.25x + 2.5x mag)</li> <li>2 from the same Block as the training set (1 IHC, 1 H&E)</li> <li>1 from another Block (IHC anti-pan-cytokerating, gastroesophageal junction lesion)</li> </ul> </li> <li>validation_tiles <ul> <li>patches of varying sizes taken from the 3 validation whole-slide images @1.25x magnification.</li> <li>7 patches from each slide.</li> </ul> </li> <li>test <ul> <li>1 whole-slide image (1.25x + 2.5x mag)</li> <li>From another block: IHC staining (anti-NR2F2), mouth cancer</li> </ul> </li> </ul> <p>For the train, validation and test whole-slide images, each slide has:<br> - The RGB images @1.25x & 2.5x mag<br> - The corresponding background/tissue masks<br> - The corresponding annotation masks containing examples of artefacts (note that a majority of artefacts are not annotated. In total, 918 artefacts are in the train set)</p> <p>For the validation tiles, the following table gives the "patch-level" supervision:</p> <p>tile# Artefact(s)<br> 00 None/Few<br> 01 Tear&Fold<br> 02 Ink<br> 03 None/Few<br> 04 None/Few<br> 05 Tear&Fold<br> 06 Tear&Fold + Blur<br> 07 Knife damage<br> 08 Knife damage<br> 09 Ink<br> 10 None/Few<br> 11 Tear&Fold<br> 12 Tear&Fold<br> 13 None/Few<br> 14 None/Few<br> 15 Knife damage<br> 16 Tear&Fold<br> 17 None/Few<br> 18 None/Few<br> 19 Blur<br> 20 Knife damage</p>
Dataset and images for "Instantaneous R calculation for COVID-19 epidemic in Brazil"
<p>This dataset was generated from raw data obtained at </p> <ul> <li>Ceará State - <a href="https://indicadores.integrasus.saude.ce.gov.br/api/casos-coronavirus/export-csv">https://indicadores.integrasus.saude.ce.gov.br/api/casos-coronavirus/export-csv</a></li> <li>São Paulo State - <a href="http://www.seade.gov.br/wp-content/uploads/2020/04/Dados-covid-19-estado.csv">http://www.seade.gov.br/wp-content/uploads/2020/04/Dados-covid-19-estado.csv</a></li> <li>Brazil - <a href="https://covid.saude.gov.br/">https://covid.saude.gov.br/</a></li> </ul> <p>Data was processed with R package EpiEstim (methodology in the associated preprint). Briefly, instantaneous R was estimated within a 5 day time window. Prior mean and standard deviation values for R were set at 3 and 1. Serial interval was estimated using a parametric distribution with uncertainty (offset gamma). We compared the results at two time points (day 7 and day 21 after the first case was registered at each region) from different brazillian states in order to make inferences about the epidemic dynamics.</p>
High Granularity Electromagnetic Shower Images
<p>This is a limited subset of the data used for training in <strong>arXiv:2005.05334</strong>. The network architectures and instructions to generate more data are available at <a href="https://github.com/FLC-QU-hep/getting_high">here. </a></p> <p>Electromagnetic calorimeter for the ILD consists of 30 active silicon layers in a tungsten absorber stack with 20 layers of 2.1 mm followed by 10 layers of 4.2 mm thickness respectively. We project the sensors onto a rectangular grid of 30×30×30 cells. Each cell in this grid corresponds to exactly one sensor, resulting in total of 27k channels.</p> <p>The file has the following structure:</p> <ul> <li> Group named <em>30x30</em> <ul> <li> <em>energy</em> : Dataset {1000, 1}</li> <li> <em> layers</em> : Dataset {1000, 30, 30, 30}</li> </ul> </li> </ul> <p>The <em>energy</em> specifies the true energy of the incoming photons in units of GeV, where <em>layers</em> represent the energy deposited (MeV) in 30 layers of the calorimeter in an image data format. This file contains approximately 24.000 showers.</p>
68 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file
<p>Data provided by Meitian Wang at PSI and master file revised May 2020 for full NXmx compliance.</p> <p>To create a new NeXus master file, assuming DIALS is installed in the folder $DIALS, use this command:</p> <p>libtbx.python $DIALS/modules/cctbx_project/xfel/swissfel/jf16m_cxigeom2nexus.py unassembled_file=lyso009a_0087.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.368479 detector_distance=97.830 mask_file=lyso009a_0087.JF07T32V01.mask.h5 nexus_details.start_time=2018-01-00T00:00:00.000 nexus_details.end_time=2018-01-00T00:00:02.720Z nexus_details.end_time_estimated=2018-01-00T00:00:02.720Z nexus_details.sample_name=Lysozyme nexus_details.total_flux=1000000000000</p> <p>Some notes about the parameters:<br> - Geometry file is in CrystFEL format but has been realigned to group the modules hierarchically into quadrants.<br> - Wavelength is a single wavelength for the whole dataset, but options exist to do 1 wavelength per image, or a whole spectrum per image.<br> - Start and end times are example timestamps for illustration. End times are estimated for 68 frames using a 25 Hz recording rate.<br> - Total flux of 1e12 photons is an estimate.</p> <p>View the data using DIALS: dials.image_viewer lyso009a_0087.JF07T32V01_master.h5</p> <p>Process the data using DIALS, treating the images as stills, assuming 64 cores available on the system:<br> dials.stills_process mp.nproc=64 lyso009a_0087.JF07T32V01_master.h5 dispersion.gain=10 known_symmetry.space_group=P43212 known_symmetry.unit_cell=77,77,37,90,90,90 refinement_protocol.d_min_start=2.5</p> <p>Download DIALS at dials.github.io.</p>
Data bundle for "Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning' </p> <p>The raw data is given as 'yprime.h5' - this contains patterns and metadata in the Bruker-exported format.</p> <p>Scripts for dataset decomposition into latent factors are given in 'Scripts'.</p> <p>Our spherical analysis code is included in 'SphericalAngleDF'.</p> <p>Outputs of our analysis code are contained in 'Analysis'.</p> <p>Figures for the paper are included in 'Figures'.</p> <p> </p>
Indor इंदौर (Guna District, Madhya Pradesh). Hero-stone dated VS 1177 with adjacent image of Hanumān.
<p>Indor इंदौर (Guna District, Madhya Pradesh). Hero-stone dated VS 1177 with adjacent image of Hanumān.</p>
PYU16 Socle of Kan Wet Khaung Mound Stone Buddha Image
<p><strong>PYU 16 Socle of Kan Wet Khaung Mound Stone Buddha Image</strong></p> <p>Support: stone Buddha image, socle</p> <p>Lines: A: 6, B: 5, C: 5, D: 5</p> <p>Dimensions (cm): h: 59, w: 57, d: 20</p> <p>Language: Sanskrit, Pyu</p> <p>Original locality: Kan Wet Khaung mound, Sriksetra</p> <p>Present locality: Sriksetra Museum, no. 2013/1/48</p> <p>References: ASI 1927–1928, 128, 145; Ray 1936: 19–20; Luce 1937: 243–244; <em>PR</em>, 41–43; <em>PPPB</em> I, 51, 57 n. 24, 65, 74 n. 22, 131–132; Guy 1997: 91; 2014: 91–92 (cat. 41); Tun Aung Chain 2003: 5–6; Sein Win 2016: 45–60.</p> <p> </p> <p>Data from: Arlo Griffiths, Marc Miyake, Bob Hudson, Julian Wheatley, "Studies in Pyu Epigraphy, I : State of the Field, Edition and Analysis of the Kan Wet Khaung Mound Inscription, and Inventory of the Corpus," <em>BEFEO</em> 103 (2017): 43-205. <a href="http://doi.org/10.5281/zenodo.1478504">http://doi.org/10.5281/zenodo.1478504</a></p>
DS_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_SEM images
<p>Scanning electron microscopy images showing P3HT pillar arrays with and without living cells on top</p>
Tutorial and dataset for gigapixel-like imaging strategies for dental anthropology
<p>This tutorial and image dataset to accompany the following publication: Willman JC, Lozano M, Hernando R, Vergès JM. Gigapixel-like imaging strategies for dental anthropology: Applications for scientific communication and training in digital image analysis. Quaternary International, <a href="https://doi.org/10.1016/j.quaint.2020.05.027">https://doi.org/10.1016/j.quaint.2020.05.027</a>. Part of the Special Issue: Not Only Use.</p> <p><strong>Contains: </strong>tutorial,<strong> </strong>183 images files for reconstructing three examples of gigapixel-like images, and one “READ ME” file describing the images.</p> <p>The tutorial is meant to be used as a guideline for the creation of gigapixel-like (GPL) images of dental surfaces based on our experience. The methodology can be extrapolated to other types of materials and surfaces, but you may need to augment these guidelines according to the specificity of your own research needs. We hope that the inclusion of this supplement will stimulate other researchers to include specific guidelines and step-by-step processes for how they created their own GPL images. While this study concentrates on scanning electron microscopy (SEM) images, there are many other ways to acquire two-dimensional images (e.g., digital photography, optical light microscopy, etc.) that can be used to create GPL images. Likewise, the number of software packages and their numerous built-in parameters for creating extended focus and mosaic images vary greatly. Therefore, more tutorials/guidelines will surely improve the transparency and accessibility of the GPL methodology in the archaeological sciences, biological anthropology, and allied fields.</p>
EU License Plates Images
<p>A collection of cropped vehicle license plates from across the EU (primarily Germany) for training automated license plate detection and extraction ML and OCR models. German plates are further sourced from a variety of states, allowing for sticker detection, extraction, and state classification models to be further developed.</p> <p>This dataset is used in the SODALITE Vehicle IoT use case for training automated license plate recognition models.</p>
Imaging spectroscopy and elemental mapping of Haughton impact melt rock: Datasets
<p>Description of archived data for manuscript Greenberger et al. (accepted, JGR Planets)<br> </p> <p>This archive contains the data underlying the results reported in the following paper:<br> Greenberger, R. N., Ehlmann, B. L., Osinski, G. R., Tornabene, L. L., & Green, R. O. Compositional Heterogeneity of Impact Melt Rocks at the Haughton Impact Structure, Canada: Implications for Planetary Processes and Remote Sensing. Journal of Geophysical Research: Planets, accepted.<br> 1. ImageList.txt: Contains information required to connect sample names from paper with images, which often contain multiple samples.<br> 2. FieldImages.tar.gz: Imaging spectroscopy files from images of outcrops in the field<br> 3. LabImages.tar.gz: Imaging spectroscopy files from samples imaged in the laboratory<br> 4. XRF_Data.tar.gz: Elemental mapping of cut samples via mapping x-ray fluorescence</p> <p>Imaging spectroscopy files (for all below, * is image name from ImageList.txt):<br> 1. *_SWIRcalib.img files: Laboratory images of samples processed to reflectance, including a dark current subtraction, line-by-line ratio to an image of Spectralon acquired with identical lighting, and correction for the reflectance properties of Spectralon. These files are stored with BIL interleave.<br> 2. *_SWIRcalib.hdr files: Header files for (1).<br> 3. *_SWIRcalib_atmcorr.img: Images of outcrops acquired in the field processed to reflectance, including instrument level corrections (dark current subtraction and flat field correction) and atmospheric correction (dark object subtraction and correction to in-scene Spectralon calibration target).<br> 4. *_SWIRcalib_atmcorr.hdr: Header files for (3).<br> 5. mask# and mask#.hdr: Masks and associated header files where the sample or region of interest has a value of 1 and outside of the sample or region of interest has a value of 0. Imaging spectroscopy measurements of multiple samples were sometimes acquired within the same image, and each sample has its own mask. ImageList.txt shows conversions from image and mask names to sample numbers. For files with no # after mask, only one sample or region of interest is present within the image.<br> 6. *_SWIRcalibmask#_MAP, *_SWIRcalib_atmcorrmask_MAP, and corresponding .hdr files: These are image files with 12 bands, one for each lithologic classification in the paper, and associated header files. Values of 1 indicate that the lithology is present, and values of 0 indicate that it is absent. The mask files (5) were used to ignore areas outside of the sample or outcrop. The band named "Mixed 2.2 and 2.3 micron features" is Mixed Carbonate + Si-OH, and "Illite-y" is Illite-like. These files are stored with BSQ interleave.</p> <p>X-ray fluorescence (XRF) data:<br> Each folder corresponds with measurements of a single sample. These are measurements of the same surfaces of some cut samples analyzed by imaging spectroscopy. ImageList.txt gives the corresponding image cubes. All exported elements are .tsv files and are in quantized relative counts as exported by the instrument software. Data for Mg are unreliable due to its low atomic number, as is typical for XRF.</p>
X-ray diffraction images of bovine trypsin crystals recorded at the FemtoMAX beamline of Max IV synchrotron facility
<p>The deposition concerns bovine trypsin diffraction images in two wedges. Each image is recorded on a still crystal and separated by 0.1 deg rotation. The x4.tar.gz archive contains summed intensities from individual snapshots at the same orientation, whereas x4_single.tar.gz archive contains single snapshots/orientation. </p>
Extreme to phenomenal storm wave impacts on a steep rocky coast, north Mayo, Ireland: video data, image analysis, runup and flow velocity calculations for waves of storms Fionn and Gareth.
<p>The primary data are video (.mp4) files of extreme storm wave impacts on the sites of high elevation (>=20m above high water mark) coastal boulder deposits, recorded during storms Fionn (16/01/2018) and Gareth (12/03/2019), at (54.320355, -9.569633) on the north Mayo coast of Ireland, while the significant wave height was in the range [11m,14m]. There are also .png and .jpg files derived from frames of some of the videos, relating to the analysis of the impacting wave kinematics (runup/landward propagation and flow velocities), together with physical measurements for scale determination and runup/velocity/measurement uncertainty calculations in Excel. The files EventX.mp4 are the primary data for the wave impacts EventX. The files EventX_Frame_Y.jpg are frames sampled from EventX.mp4 at constant time intervals in the temporal vicinity of the impact. The files EventX_Edges_Y.png are the edges derived from the frames with the Canny edge detector. The files EventX_Registration_Y.jpg are the impacting wavefront edges with topographical edges registered on the file ReferenceImage.jpg The files EventX.jpg are the stacked registrations for all Y, from which the impact kinematics are derived. The file Scale_Registration_Position_Velocity_Measurements_AndUncertainty.xlsx contains physical measurements for scale determination, measurements of registration error, and the calculations of impact runup/landward displacement and flow velocities, with their uncertainties. The files JetX_Leacht_a_Chúil.mp4/g are videos of large jet-producing impacts at another site.</p> <p>The files DSCN0066.MP4-DSC0085.MP4 are the raw video observations of Storm Gareth, recorded from 15:35-18:41 UT on 12 March 2019 with a Nikon Coolpix W100, while the significant wave height increased from 12m to in excess of 14m (the timestamp of these videos in Properties->Details->Media Created is one hour later than the UT of creation, because the camera's clock was set to Irish Summer Time). The file GPO15366.MP4 is an example of the GoPro (Hero 5) videos recorded simultaneously.</p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 17:09 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 17:09 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_417-419 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.