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38,240 results for “Imaging”
Diffraction images of crystals of the Calx-beta domain of integrin beta4 (PDB code 3FQ4)
<p>Diffraction images of native crystals of the Calx-β domain<br> of the human integrin β4 subunit. Images correspond to the dataset used to solve and refine the pdb entry 3FQ4 (http://www.rcsb.org/pdb/explore/explore.do?structureId=3FQ4).</p> <p>Data was collected using a rotating anode generator (MicrostarH, Bruker AXS) and a mar345dtb image plate detector (marXperts GmbH). The dataset consists of 360 images (1 degree oscillation per image). Data extend to 1.48 Å resolution.</p>
Atomic Force Microscopy Images of Various Specimens
<p>This data set consists of ten atomic force microscopy images in MI format as well as corresponding previews in PNG format.</p> <p>The microscopy images are of various materials and have been scanned with AFM equipment from Keysight Technologies. Details on the individual images:</p> <ul> <li>image_7.mi - calibration grid with 5 µm pitch size</li> <li>image_8.mi - Celgard (a polymer membrane used in batteries)</li> <li>image_9.mi - Titanium-Tungsten film</li> <li>image_10.mi - AFM image</li> <li>image_11.mi - self-assembled monolayer of lipids on gold</li> <li>image_12.mi - capacity calibration sample for Scanning Microwave Microscopy imaging</li> <li>image_13.mi - capacity calibration sample for Scanning Microwave Microscopy imaging</li> <li>image_14.mi - PS-LDPE-12M (a polymer blend of Polystyrene and Polyolefin Elastomere)</li> <li>image_15.mi - AFM Calibration grid</li> <li>image_16.mi - AFM Calibration grid</li> </ul> <p>The images can be opened using, e.g. Gwyddion: http://gwyddion.net/<br /> The Python package Magni can be used to load the images into Python (using the magni.afm.io module): https://github.com/SIP-AAU/Magni</p> <p>The images are provided as-is without warranty of any kind.</p>
Skyrmion image gallery
<p>The figures were created in the frame of the MAGicSky consortium. They are available in the report "D1.1: report on imaging of individual skyrmions of MML systems made by different techniques" (arXiv ID: 1609.08415).</p>
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
X-ray diffraction images used for refinement of cytochrome cL from Methylobacterium extorquens.
<p>X-ray diffraction images for cytchrome cL from <em>M. extorquens</em> extending to 1.6 Angstroms resolution that were collected at ID14-2 at the ESRF (Grenoble) in April 2001. This dataset was used for high resolution refinement of the structure. </p>
X-ray diffraction images for L-threonine dehydrogenase from Trypanosoma brucei with NAD and pyruvate bound.
<p>X-ray diffraction images which were collected at ESRF (Grenoble) using an ADSC 315r CCD detector on beamline ID29 on 11th November 2009. More details are given in the uploaded notes. </p>
1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.
<p>Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron.</p> <p>Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.</p>
X-ray diffraction images for an MDM2/Nutlin-3a complex
<p>This submission includes a zip archive of diffraction images recorded with the MARMOSAIC 225 mm CCD detector at the ESRF beam line ID23-2. Relevant meta data can be found in the headers of those diffraction images or in the Protein Data Bank entry 4HG7.</p>
Diffraction images of a crystal of the spectrin repeats 7, 8, and 9 (SR7-SR9) of the plakin domain of human plectin (PDB code 5J1I)
<p>Diffraction images of crystals of a fragment of the plakin domain of human plectin that includes the spectrin repeats 7 to 9 (SR7-SR9).</p> <p>Images correspond to the dataset used to solve and refine the pdb entry 5J1I (http://www.rcsb.org/pdb/explore/explore.do?structureId=5J1I).</p> <p>Data were collected on a single crystal at the beamline 14.2 of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using radiation of 0.9330 Å wavelength and an ADSC Q4 CCD detector. The dataset consists of 360 images (1 degree oscillation per image).</p> <p>Diffraction data is highly anisotropic. Based on analysis with the STARANISO server (http://staraniso.globalphasing.org/) data extend approximately to 5.0, 3.8, and 2.6 Å resolution along the three principal directions of anisotropy, which are 0.555 a*+ 0.832 c*, b*, and -0.361 a* + 0.932 c*, respectively.</p>
Icchāwar, Uttar Pradesh. Buddha image inscription.
<p><a href="https://siddham.network/inscription/in00539/">IN00539</a> Dhanesar Kherā is near the village of Icchāwar or Nicchāwar in Jaspura Tahsil, Banda District, Uttar Pradesh, located approximately at 25.28°N 80.57°E. The bronze statue was discovered with two others and published by William Hooey and Vincent Arthur Smith in 1895. Since 1969 it has been kept in the British Museum. The other two statues from the hoard are in the National Museum, Bangkok, and the Nelson Atkins Museum of Art, Kansas City.</p>
Diffraction images of crystals of the spectrin repeats 7 and 8 (SR7-SR8) of the plakin domain of human plectin (PDB code 5J1G): native and Hg-derivative datasets for phasing by SIRAS
<p>Diffraction images of crystals of a fragment of the plakin domain of human plectin that includes the spectrin repeats 7 to 8 (SR7-SR8).</p> <p>Images correspond to the dataset used to solve and refine the pdb entry <strong>5J1G</strong> (http://www.rcsb.org/pdb/explore/explore.do?structureId=5J1G).</p> <p> </p> <p>The structure was phase by single isomorphous replacement with anomalous scattering (SIRAS) using two datasets: one from a native crystal and another one from a crystal derivatized with the mercurial compound ethylmercurithiosalicylate (EMTS).</p> <p> </p> <p>The <strong>Native dataset</strong> was collected on a single crystal at the beamline XALOC of the ALBA Synchrotron (Barcelona, Spain) using radiation of 0.9792 Å wavelength and a PILATUS 6M detector. The dataset consists of 4 wedges of 450 images each (0.2º oscillation per image). Each wedge was collected at a different position of the same crystal. The crystals belong to the space group P2<sub>1</sub> with approximate cell dimensions <em>a</em>=45.7 Å, <em>b</em>=115.9 Å, <em>c</em>=64.8 Å, beta=97.6 º.</p> <p> </p> <p>The data from a <strong>mercurial derivative</strong> (EMTS) was collected in house using a rotating anode X-ray generator (wavelength 1.54179 Å) and a mar345 image plate detector. The dataset consists of 360 images (1º oscillation per image). The crystal was isomorphic to the native crystal.</p> <p> </p> <p>In addition to the diffraction images the following files are included:</p> <p>a) Files for indexing with the program XDS and the HKL files containing the integrated intensities.</p> <p>b) Files for scaling using the program xscale (directory XSCALE_5J1G_Native_EMTS).<br> c) The directory “phasing_shelx” contains hkl files of the intensities of the native and EMTS datasets in a format suitable for analysis with Shelx. This directory also contains the files of the phasing by SIRAS using Shelx C/D/E.</p>
Supporting data for 'Simultaneous maximum a posteriori longitudinal PET image reconstruction'
<p>This dataset contains the data used to produce the paper: <em>'Simultaneous maximum </em>a posteriori l<em>ongitudinal PET image reconstruction' </em>by Ellis and Reader, Physics in Medicine and Biology (2017). DOI: http://dx.doi.org/10.1088/1361-6560/aa7b49. Please see the article for a full description of methodology used to obtain this data. </p> <p>The dataset comprises a number of MATLAB data files (.mat), MATLAB scripts (.m), and plain text files (.txt), corresponding to each figure in the article. Running the .m script in MATLAB for each figure will reproduce that figure approximately as it appears in the article. Furthermore, the .txt files describe the contents of the .mat data files in order to allow independent exploration of the data. Note that the function plotSparseMarker is required to be able to run fig5.m.</p> <p>This work was funded by the King’s College London & Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging (grant number EP/L015226/1) and supported by the EPSRC grant number EP/M020142/1. This data has been made available in accordance with the EPSRC's policy framework on research data.</p>
Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images
<p><strong>Foundational Codebook and Data: </strong></p> <p>Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.</p> <p>The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.</p> <p><strong>Obstructed Class Variation: </strong></p> <p>There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.</p> <p>This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.</p>
A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images
<p><strong>CGHD</strong></p> <p>This dataset contains images of hand-drawn electrical circuit diagrams as well as accompanying annotation and segmentation ground-truth files. It is intended to train (e.g. ANN) models for extracting electrical graphs from raster graphics.</p> <p><strong>Content</strong></p> <ul> <li><strong>3.269</strong> Annotated Raw Images<br> <ul> <li>31 Main Drafters <ul> <li>12 Circuits per Drafter</li> <li>2 Drawings per Circuit</li> <li>4 Photos per Drawing</li> </ul> </li> <li>Additional Circuit Images provided by TU Dresden (from Real-World Examinations, Drafter 0)</li> <li>Additional Circuit Images provided by RPTU Kaiserslautern-Landau (Drafter -1)</li> <li><strong>248.020 </strong>Bounding Box Annotations</li> <li><strong>40.711</strong> Rotation Annotations</li> <li><strong>1.437</strong> Mirror Annotations</li> <li><strong>85.417</strong> Text String Annotations (equals <strong>93.74%</strong> completeness)<br> <ul> <li><strong>289.850</strong> Text Characters</li> <li><strong>98</strong> Character Types (Upper/Lower Case Latin, Numbers, Special Characters)</li> </ul> </li> </ul> </li> <li><strong>320</strong> Binary Segmentation Maps<br> <ul> <li>Strokes vs. Background</li> <li>Accompanying Polygon Annotation Files</li> <li><strong>22.929</strong> Polygon Annotations</li> </ul> </li> <li><strong>59 </strong>Object Classes</li> <li><strong>Scripts</strong> for Data Loading, Statistics, Consistency Check and Training Preparation</li> </ul>
Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer
<p>This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the <span>Laboratoire de Glaciologie (GLACIOL)</span> at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf). </p>
Image Repository Decision Tree - Where do I deposit my imaging data
<p>Depositing data in quality data repositories is one crucial step towards FAIR (Findable, Accessible, Interoperable, and Reusable) data. Accordingly, Euro-BioImaging strongly encourages sharing scientific imaging data in established, thematic repositories. </p> <p>To guide you in the selection of appropriate repositories, we have created an overview of available repositories for different types of image data, including their scope and requirements. This decision tree guides you through questions about your data and directs you to the correct repository, and/or provides instructions for further processing to meet the critera of the repositories. </p> <p>Three seperate trees are provided for different classes of imaging data: open bioimage data, preclinical data, and human imaging data. These versions with three trees can be used for web-view. Update: also the editable versions in powerpoint format (.pptx) are now provided. Please be aware that opening the versions with another program might lead to shifted formatting.</p> <p>Update: we now also provide ready-to-print versions designed to be printed on A3 format. One page shows the open bioimaging data tree and one page combines the preclinical and human imaging data trees. Also the editable versions of these are provided.</p>
MultiCaRe: An open-source clinical case dataset for medical image classification and multimodal AI applications
<p>The dataset contains multi-modal data from over 70,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.</p> <p>More than 90,000 patients and 280,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.</p> <p>Refer to the examples showcased in this <a href="https://github.com/mauro-nievoff/MultiCaRe_Dataset">GitHub repository</a> to understand how to optimize the use of this dataset.<br><br>The license of the dataset as a whole is CC BY-NC-SA. However, its individual contents may have less restrictive license types (CC BY, CC BY-NC, CC0). For instance, regarding image filess, 66K of them are CC BY, 32K are CC BY-NC-SA, 32K are CC BY-NC, and 20 of them are CC0.</p>
Underwater images collected by Scuba diving in Ifaty, Madagascar - 2023-05-07
<i>This dataset was collected by Scuba diving in Ifaty, Madagascar - 2023-05-07.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 2.66 GB of MP4 files, which were trimmed into 1056 frames (at 2997/1000 fps). <br> The frames are not georeferenced. <br> 93.75% of these extracted images are useful and 6.25% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> No GPS. <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30
<i>This dataset was collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 1.5 GB of JPG files, which were trimmed into 267 frames (at 4 fps). <br> The frames are georeferenced. <br> 99.63% of these extracted images are useful and 0.37% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> No GPS. <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
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.