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38,240 results for “Imaging”
Radiative Transfer Edge-on Protoplanetary Disk Images
<p>Dataset used to train a Convolutional Autoencoder model to generate synthetic images of edge-on protoplanetary disks. The work is described in "A machine learning framework to predict images of edge-on protoplanetary disks", Telkamps et al. 2022, submitted to AAS. This image dataset was created using the radiative transfer (RT) modeling code MCFOST (Pinte et al. 2006; Pinte et al. 2009). </p>
Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism
<p>Datasets related to the research article 'Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism'. <br> A readme with the necessary Matlab code to load the data is included.<br> Tested on Matlab2020b</p> <p>For the analytic rupture radiation simulation code please check the linked github repository.</p>
BioSR+: Dataset Extension of biological images for super-resolution microscopy
<p>BioSR+ dataset is an extension of our pre-published BioSR dataset of biological images for super-resolution microscopy, currently including image pairs of low-and-high resolution images of five biology structures (CCPs, ER, MTs, F-actin, Myosin-IIA) and 8 signal levels for each ROI. The BioSR+ dataset is related to our Nature Methods paper "Evaluation and development of deep neural networks for image super-resolution in optical microscopy" (DOI: 10.1038/s41592-020-01048-5) and Nature Biotechnology paper "Rationalized deep learning super-resolution <br> microscopy for sustained live imaging of rapid subcellular processes" (DOI:10.1038/s41587-022-01471-3). Both BioSR and BioSR+ are freely available and can be used for non-commercial purposes with proper citations of above two papers.</p>
Images_Mingei
<p>Documentation material from the Silk pilot of the Mingei project</p>
Historical_Images_complementory_Mingei
Documentation material from the Glass pilot of the Mingei project
Historical_Images_Mingei
Documentation material from the Glass pilot of the Mingei project
glass_carafe_images_Mingei
Documentation material from the Glass pilot of the Mingei project
glass_carafe_images_Mingei
Documentation material from the Glass pilot of the Mingei project
Segmentation of oocyte zona pellucida in transmitted light images (mouse and human)
<p>This dataset has been presented in our paper "An interpretable and versatile machine learning approach for oocyte phenotyping", in bioRxiv.</p> <p>It contains images acquired in transmitted light with different settings of mouse and human oocytes, with the corresponding ground-truth of the zona pellucida segmentation. Mouse oocyte images were taken before and during oocyte maturation (meiosis I). Some human oocyte images were taken during oocyte maturation (meiosis I), and some are M-II oocytes just after fertilization.</p>
Small-angle X-ray scattering datasets for imaging crossing fibers in mouse, pig, monkey, and human brain
<p>Small-angle X-ray scattering datasets for resolving crossing fibers (myelinated neuronal axon bundles), as described in</p> <p>"<strong><em>Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering</em></strong>"</p> <p>deposited in bioRxiv:</p> <p>https://doi.org/10.1101/2022.09.30.510198</p>
EconBiz Images for Text Extraction from Scholarly Figures
<p>Scholarly figures are data visualizations like bar charts, pie charts, line graphs, maps, scatter plots or similar figures. Text extraction from scholarly figures is useful in many application scenarios, since text in scholarly figures often contains information that is not present in the surrounding text. This dataset is a corpus of 121 scholarly figures from the economics domain evaluating text extraction tools. We randomly extracted these figures from a corpus of 288,000 open access publications from <a href="https://www.econbiz.de/">EconBiz</a>. The dataset resembles a wide variety of scholarly figures from bar charts to maps. We manually labeled the figures to create the gold standard.</p> <p>We adjusted the provided gold standard to have a uniform format for all datasets. Each figure is accompanied by a TSV file (tab-separated values) where each entry corresponds to a text line which has the following structure:</p> <ul> <li>X-coordinate of the center of the bounding box in pixel</li> <li>Y-coordinate of the center of the bounding box in pixel</li> <li>Width of the bounding box in pixel</li> <li>Height of the bounding box in pixel</li> <li>Rotation angle around its center in degree</li> <li>Text inside the bounding box</li> </ul> <p>In addition we provide the ground truth in JSON format. A schema file is included in each dataset as well. The dataset is accompanied with a ReadMe file with further information about the figures and their origin.</p> <p>If you use this dataset in your own work, please cite one of the papers in the references.</p>
IN02005 Trivikrama Image Inscription of Tilaganga
<p>Gnoli, Raniero. Nepalese Inscriptions in Gupta Characters. Roma: Is. M. E. O. 1956. Plate 5, Inscription 3, p. 6.</p>
Mars orbital image (HiRISE) labeled data set
<p>This data set contains 3820 landmarks that were extracted from 168 HiRISE images. The landmarks were detected in HiRISE browse images. For each landmark, we cropped a square bounding box the included the full extent of the landmark plus a 30-pixel margin to left, right, top, and bottom. Each cropped image was then resized to 227x227 pixels.</p> <p><strong>Contents</strong>:</p> <ul> <li>map-proj/: Directory containing individual cropped landmark images</li> <li>labels-map-proj.txt: Class labels (ids) for each landmark image</li> <li>landmark_mp.py: Python dictionary that maps class ids to semantic names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.1048301</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p> <p> </p>
Mars surface image (Curiosity rover) labeled data set
<p>This data set consists of 6691 images spanning 24 classes that were collected by the Mars Science Laboratory (MSL, Curosity) rover by three instruments (Mastcam Right eye, Mastcam Left eye, and MAHLI). These images are the "browse" version of each original data product, not full resolution. They are roughly 256x256 pixels each.</p> <p>We divided the MSL images into train, validation, and test data sets according to their sol (Martian day) of acquisition. This strategy was chosen to model how the system will be used operationally with an image archive that grows over time. The images were collected from sols 3 to 1060 (August 2012 to July 2015). The exact train/validation/test splits are given in individual files. Full-size images can be obtained from the PDS at https://pds-imaging.jpl.nasa.gov/search/ .</p> <p><strong>Contents</strong>:</p> <ul> <li>calibrated/: Directory containing calibrated MSL images</li> <li>train-calibrated-shuffled.txt: Training labels (images in shuffled order)</li> <li>val-calibrated-shuffled.txt: Validation labels</li> <li>test-calibrated-shuffled.txt: Test labels</li> <li>msl_synset_words-indexed.txt: Mapping from class IDs to class names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:</p> <p>10.5281/zenodo.1049137</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p>
TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image
<p>Unseparated Pulse Energy Spectrogram</p> <p>Processed audio data.</p> <p>Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it.</p> <p>I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification.</p> <p>For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied.</p> <p>I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning.</p> <p>Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.</p>
Data supporting 3D Super-resolution Optical Fluctuation Imaging with Temporal Focusing with two-photon excitation
<p>Data to support the publication combining temporal focusing two photon excitation with super-resolution optical fluctuation imaging.</p> <div>This research was funded by National Centre of Science, grant number: 2022/47/B/ST7/03465. For the purpose of Open Access, the author has applied a</div> <div>CC-BY public copyright licence to any author Accepted Manuscript (AAM) version arising from this submission</div>
Dataset of imaged commercial and custom-made printing filament materials for Computed Tomography imaging of organ body phantoms
<p>The dataset includes a total of 29 filament materials 7 custom-made materials and the selection of 22 commercially available materials.</p> <p>All the materials were printed with a Longer LK4 Pro printer into cubes with dimensions 20 mm x 20 mm x 10 mm.</p> <p>A part of each filament was grinded into pellets, placed into metallic cylinder container and then were heated up to their melting points to receive a homogeneous cylindrical sample of this material.</p> <p>The cubes and the cylindrical samples were scanned at a clinical CT scanner at three anode voltages (kV) and a slice thickness of 0.6 mm.</p>
Images of article "Sexy ways: the methodical approaches to study plant sex chromosomes"
<p><strong>Figure 1. </strong>Schematic diagram of sex chromosome evolution in dioecious plants. Species are shown according to their level of sex chromosome differentiation and Y chromosome asynapsis. In<em> S. oleracea, A. officinalis</em> and <em>C. papaya</em>, the sex chromosomes are mostly homomorphic with recently formed non-recombining regions (region with suppressed recombination). The non recombining region is largely extended almost to entire chromosomal length in species with heteromorphic sex chromosomes, namely in <em>S. latifolia, R. hastatulus</em> (XY cytotype), <em>R. acetosa, H. lupulus, H. japonicus </em>and<em> M. polymorpha</em>. The position of the centromere, the PAR length and the ratio between X and Y is illustrative. </p> <p><strong>Figure 2.</strong> Laser microdissection as a tool to reduce genome complexity. Sex chromosomes in metaphase are isolated from plant cells (mostly pollen mother cells or root tips) and subsequently spread on a special microscopic slide covered with the membrane. After microdissection, chromosomes are transferred into a tube and processed to other applications. In case of chromosome sorting, the chromosome suspension is stained with a DNA-specific dye and introduced into a flow chamber. Within this chamber, individual chromosomes interact with a laser beam, and the scattered light and emitted fluorescence are measured. Through this process, a histogram of fluorescence intensity (known as a flow karyotype) is generated. Sorting is accomplished by breaking the liquid stream into droplets and electrically charging the droplets containing the chromosomes of interest.</p> <p><strong>Figure 3.</strong> Cytogenetic tools to study sex chromosome origin and evolution. Cytogenetics nowadays combine genomic tools to study repeat fraction including TEs and satellites (a), design unique barcodes to distinguish particular chromosome or chromosomal domain using chromosome oligo-painting probe design (b), and bioinformatic tools to dissect single chromosomes or genome parts (c). The combination of above methods helps to understand sex chromosome evolution regarding their autosomal origin, chromosomal rearrangements, and Y(W) chromosome differentiation. Arrows represent evolutionary steps during sex chromosome divergence (d). The sex chromosome barcoding allows understanding of meiotic pairing which in turn supports chromosomal fusions and inversion/translocations. To chromosomes belong to species with references, from the top to the bottom as follows: <em>S. latifolia </em>Ogre retroelement (Kubat et al., 2014), <em>R. hastatulus</em> XY cytotype satellite Cl135 (Sacchi et al., 2023, Preprint), <em>S. latifolia</em> PAR oligo-painting probe with the subtelomeric satellite X43.1 and centromeric satellite STAR-C (Bačovský et al., 2020), and the same DNA probes on chromosomes in metaphase I in <em>S. latifolia </em>(Bernasconi et al., 2009; Bačovský et al., 2022). </p> <p><strong>Figure 4.</strong> Methodical strategies to assess the function of sex chromosomes in plants. Experimental assays with polyploids (alternatively aneuploids) represent the classical way to determine the role of individual sex chromosomes (a). These assays with plants of various ploidy levels were usually supported by analyses of deletion lines (plants carrying short-chromosomal <br>deletions or microdeletions) (b) that allowed researchers to identify sex-linked regions involved in sex determination and floral development. Modern assays using reverse genetics, such as CRISPR/Cas9, virus-induced gene silencing (VIGS) or peptide treatment of shoot apical meristem (c) provide direct evidence of the gene function and its contribution to the development <br>of reproductive organs. Parasite infected (d) or chemically induced (e) hermaphrodites from either female or male individuals, e.g. in <em>Silene</em> or kaki, let to the identification of key mechanisms and genes that regulate sexual phenotypes, and to understand the regulatory networks leading to separate sexes. </p>
Diffusion weighted MR imaging of post-mortem rat brain to allow reconstruction of the cortical connectome
<h2>Brief description</h2> <p> </p> <p>These data accompany the article by Sinke et al. (Sinke et al., 2018). It contains the dMRI image volumes of 10 rats, a subset of these data was used for the tractography procedures described in the article. In addition high-resolution 3D balanced SSFP data are provided with high contrast between grey and white matter and CBF. The data are also accompanied by T<sub>1</sub> weighted 3D spoiled gradient echo volumes at three different echo times (5,10 and 15 ms) which can be used for T<sub>2</sub>* measurements.</p> <h2>Animals</h2> <p> </p> <p>All animal procedures were approved by the Animal Experiments Committee of the University Medical Center Utrecht and Utrecht University. Experiments were performed in accordance with the guidelines of the European Communities Council Directive. Ten healthy adult (12–13 weeks old) male Wistar rats have been used and are described in the RCR_table.csv file. Animals were sacrificed and their brains were fixed with transcardial perfusion-fixation. Brains were extracted scanned.</p> <p> </p> <h2>MR acquisition</h2> <p> </p> <p>MRI was performed on a 9.4 T horizontal bore MR system (Varian, Palo Alto, CA, USA) equipped with a 6 cm ID gradient insert with gradients up to 1 T/m. A custom made solenoid coil with an internal diameter of 2.6 cm was used for excitation and reception of the MR signal. The perfusion-fixed brains were inserted with the skulls intact in a custom-made holder and immersed in non-magnetic oil (Fomblin, Solvay Solexis). Diffusion MR used a 3D diffusion-weighted spin-echo sequence with an isotropic spatial resolution of 150 mm, where the read- and phase- encode direction were acquired using 8-shot EPI encoding and the second phase direction was linearly phase-encoded (TR/TE 500/32.4 ms, 220*128*108 matrix, FOV 33*19.2*16 mm<sup>3</sup>, D/d 15/4 ms, b 1031,2078,3994,6038,7756 s/mm<sup>2</sup>, 60 diffusion-weighted images in non-collinear directions and 24 images without diffusion weighting (b=0), number of averages 1, total number of images 325). Four 3D BSSFP images were acquired with an isotropic spatial resolution of 100 mm (TR/TE 15.4/7.7 ms, flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm<sup>3</sup>, 6 averages, pulse angle shift 0°, 90°, 180° and 270°). The four images were added as complex images to obtain a single BSSFP image with reduced banding artifacts in the brain. If scanning time allowed, three spoiled gradient-echo acquisitions were also performed with varying echotimes of 15, 10 and 5 ms respectively and TR 20 ms (flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm<sup>3</sup>, 24 averages, pulse angle shift 117°).</p> <h2>Data structure</h2> <p> </p> <p>The repository contains the following data:</p> <p>- READ_ME.txt: this file</p> <p>- RCR_table.csv : Table containing acquisition dates and numbers for the scanned animals.</p> <p>- rawdata.zip : Zipped data directory ‘rawdata’ containing acquired images in NIfTI data format per animal. Data can be unzipped using the ‘unzip’ command. Directory rawdata contains subdirectories RCR01 to RCR10 (individual rat directories). Each rat directory contains the following NIfTI files:</p> <p>o bal.nii.gz and balsumcom.nii.gz : The separate acquisitions of the BSSFP experiment and the complex summation of the data respectively.</p> <p>o dtitot.nii.gz : The diffusion weighted volumes in the order that they were acquired.</p> <p>o bvals and bvecs : Text files containing the b-values and b-vectors in the order that they were acquired, so this corresponds with the dtitot.nii.gz file.</p> <p>o zerob: Text file containing the image numbers where images with no diffusion weighting were acquired.</p> <p>o ubal1.nii.gz, ubal2.nii.gz and ubal3.nii.gz : The three 3D spoiled gradient acquisitions with TE 15,10, and 5 ms respectively.</p> <p>- derivatives.zip : Zipped data directory ‘derivatives’ containing calculated images of the diffusion parameters after application of FMRIB’s diffusion toolbox DTIfit. In addition it contains a file dti3D_b0.nii.gz which is a summation of all the b0-images and a file mask.nii.gz containing the ‘brain’ mask used for application of DTIfit.</p> <p>- Sinke_BrainStructureFunction2018.pdf : The article based on (part) of these data.</p>
IODP Expedition 379 Core composite images
A digital composite image (PNG) is made for each core comprising core sections scanned using a line-scan camera. The composite layout is equivalent to traditional core table photos. Top left is top of core; color and meter rule references are included.
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.