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38,240 results for “image”
ELKI Multi-View Clustering Data Sets Based on the Amsterdam Library of Object Images (ALOI)
<p>These data sets were originally created for the following publications:</p> <p><em>M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek</em><br> <strong>Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?</strong><br> In Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM), Heidelberg, Germany, 2010.</p> <p><em>H.-P. Kriegel, E. Schubert, A. Zimek</em><br> <strong>Evaluation of Multiple Clustering Solutions</strong><br> In 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece, 2011.</p> <p>The outlier data set versions were introduced in:</p> <p><em>E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel</em><br> <strong>On Evaluation of Outlier Rankings and Outlier Scores</strong><br> In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.</p> <p> </p> <p>They are derived from the original image data available at <a href="https://aloi.science.uva.nl/">https://aloi.science.uva.nl/</a></p> <p>The image acquisition process is documented in the original ALOI work: <em>J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders</em>, <strong>The Amsterdam library of object images</strong>, Int. J. Comput. Vision, 61(1), 103-112, January, 2005</p> <p>Additional information is available at: <a href="https://elki-project.github.io/datasets/multi_view">https://elki-project.github.io/datasets/multi_view</a></p> <p>The following views are currently available:</p> <table> <tbody><tr> <th>Feature type</th> <th>Description</th> <th>Files</th> </tr> <tr> <td>Object number</td> <td>Sparse 1000 dimensional vectors that give the <em>true</em> object assignment</td> <td><a href="6355684/files/objs.arff.gz">objs.arff.gz</a></td> </tr> <tr> <td>RGB color histograms</td> <td>Standard RGB color histograms (uniform binning)</td> <td><a href="6355684/files/aloi-8d.csv.gz">aloi-8d.csv.gz</a> <a href="6355684/files/aloi-27d.csv.gz">aloi-27d.csv.gz</a> <a href="6355684/files/aloi-64d.csv.gz">aloi-64d.csv.gz</a> <a href="6355684/files/aloi-125d.csv.gz">aloi-125d.csv.gz</a> <a href="6355684/files/aloi-216d.csv.gz">aloi-216d.csv.gz</a> <a href="6355684/files/aloi-343d.csv.gz">aloi-343d.csv.gz</a> <a href="6355684/files/aloi-512d.csv.gz">aloi-512d.csv.gz</a> <a href="6355684/files/aloi-729d.csv.gz">aloi-729d.csv.gz</a> <a href="6355684/files/aloi-1000d.csv.gz">aloi-1000d.csv.gz</a></td> </tr> <tr> <td>HSV color histograms</td> <td>Standard HSV/HSB color histograms in various binnings</td> <td><a href="6355684/files/aloi-hsb-2x2x2.csv.gz">aloi-hsb-2x2x2.csv.gz</a> <a href="6355684/files/aloi-hsb-3x3x3.csv.gz">aloi-hsb-3x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-4x4x4.csv.gz">aloi-hsb-4x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-5x5x5.csv.gz">aloi-hsb-5x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-6x6x6.csv.gz">aloi-hsb-6x6x6.csv.gz</a> <a href="6355684/files/aloi-hsb-7x7x7.csv.gz">aloi-hsb-7x7x7.csv.gz</a> <a href="6355684/files/aloi-hsb-7x2x2.csv.gz">aloi-hsb-7x2x2.csv.gz</a> <a href="6355684/files/aloi-hsb-7x3x3.csv.gz">aloi-hsb-7x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-14x3x3.csv.gz">aloi-hsb-14x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-8x4x4.csv.gz">aloi-hsb-8x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-9x5x5.csv.gz">aloi-hsb-9x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-13x4x4.csv.gz">aloi-hsb-13x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-14x5x5.csv.gz">aloi-hsb-14x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-10x6x6.csv.gz">aloi-hsb-10x6x6.csv.gz</a> <a href="6355684/files/aloi-hsb-14x6x6.csv.gz">aloi-hsb-14x6x6.csv.gz</a></td> </tr> <tr> <td>Color similiarity</td> <td>Average similarity to 77 reference colors (not histograms) 18 colors x 2 sat x 2 bri + 5 grey values (incl. white, black)</td> <td><a href="6355684/files/aloi-colorsim77.arff.gz">aloi-colorsim77.arff.gz</a> (feature subsets are meaningful here, as these features are computed independently of each other)</td> </tr> <tr> <td>Haralick features</td> <td>First 13 Haralick features (radius 1 pixel)</td> <td><a href="6355684/files/aloi-haralick-1.csv.gz">aloi-haralick-1.csv.gz</a></td> </tr> <tr> <td>Front to back</td> <td>Vectors representing front face vs. back faces of individual objects</td> <td><a href="6355684/files/front.arff.gz">front.arff.gz</a></td> </tr> <tr> <td>Basic light</td> <td>Vectors indicating basic light situations</td> <td><a href="6355684/files/light.arff.gz">light.arff.gz</a></td> </tr> <tr> <td>Manual annotations</td> <td>Manually annotated object groups of semantically related objects such as cups</td> <td><a href="6355684/files/manual1.arff.gz">manual1.arff.gz</a></td> </tr> </tbody></table> <p><strong>Outlier Detection Versions</strong></p> <p>Additionally, we generated a number of subsets for outlier detection:</p> <table> <tbody><tr> <th>Feature type</th> <th>Description</th> <th>Files</th> </tr> <tr> <td>RGB Histograms</td> <td>Downsampled to 100000 objects (553 outliers)</td> <td><a href="6355684/files/aloi-27d-100000-max10-tot553.csv.gz">aloi-27d-100000-max10-tot553.csv.gz</a> <a href="6355684/files/aloi-64d-100000-max10-tot553.csv.gz">aloi-64d-100000-max10-tot553.csv.gz</a></td> </tr> <tr> <td> </td> <td>Downsampled to 75000 objects (717 outliers)</td> <td><a href="6355684/files/aloi-27d-75000-max4-tot717.csv.gz">aloi-27d-75000-max4-tot717.csv.gz</a> <a href="6355684/files/aloi-64d-75000-max4-tot717.csv.gz">aloi-64d-75000-max4-tot717.csv.gz</a></td> </tr> <tr> <td> </td> <td>Downsampled to 50000 objects (1508 outliers)</td> <td><a href="6355684/files/aloi-27d-50000-max5-tot1508.csv.gz">aloi-27d-50000-max5-tot1508.csv.gz</a> <a href="6355684/files/aloi-64d-50000-max5-tot1508.csv.gz">aloi-64d-50000-max5-tot1508.csv.gz</a></td> </tr> </tbody></table>
Dataset for the manuscript "Event-triggered STED imaging"
<p>Dataset that supports the implementation of the event-triggered STED method and support the findings in the manuscript: "Event-triggered STED imaging" (Jonatan Alvelid, Martina Damenti, Chiara Sgattoni, Ilaria Testa, preprint: https://doi.org/10.1101/2021.10.26.465907). The data files are organized according to the various experiments performed for characterization or application of the method. References to the specific figures in the manuscript that uses the different data is provided in the info file.</p> <p>The scripts provided at https://github.com/jonatanalvelid/etSTEDanalysis (https://doi.org/10.5281/zenodo.6469723) have been used for image and data handling, and image analysis of the here provided dataset.</p>
Subjective human thresholds over computer generated images
<p>Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> <span class="math-tex">\(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\)</span></p> <p>where:</p> <ul> <li> <span class="math-tex">\(L_o(x, \omega_o)\)</span> is the luminance traveling from point <span class="math-tex">\(x\)</span> in direction <span class="math-tex">\(\omega_o\)</span>;</li> <li><span class="math-tex">\(L_e(x, \omega_o)\)</span> is point <span class="math-tex">\(x\)</span> emitted luminance (it is null if point x does not lie on a ligth source surface);</li> <li>the integral represents the set of luminances <span class="math-tex">\(L_i\)</span>incident in <span class="math-tex">\(x \)</span> from the hemisphere of the directions <span class="math-tex">\(\Omega\)</span> and reflected in the direction <span class="math-tex">\(\omega_o\)</span>. The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function <span class="math-tex">\(f_r(x, \omega_i \rightarrow \omega_o)\)</span>) and the cosinus of the incident angle.</li> </ul> <p>This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image.</p> <p>This proposed dataset is composed of 80 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 800 x 800 pixels in size. The most noisy image is of 20 samples and the reference one (the most converged image obtained) is of 10000 samples. The <a href="https://www.pbrt.org/index.html">pbrt</a> rendering engine (version 3) was used to generate these images.</p> <p>By exploiting these levels of samples obtained and therefore of noise perceptible in the images, average subjective human thresholds were collected. For this purpose, the images were divided into 16 areas of 200 x 200 pixels in size for each point of view.</p> <p>The proposed image database is composed of the following files:</p> <ul> <li><strong>human-thresholds.csv</strong> : the set of human subjective thresholds obtained on 40 points of view. A line is composed of the name of the point of view followed by all the thresholds obtained for each of the 16 zones;</li> <li><strong>SIN3D_dataset.tar.gz</strong> : is an archive containing all the images from 20 to 10000 samples in steps of 20 samples for each point of view (i.e. 500 images per point of view). Each folder in the archive corresponds to a point of view.</li> </ul> <p><em>This image database has been exploited in order to propose an objective model for noise detection in photo-realistic computer-generated images (article referenced to this image database).</em></p> <p><strong>Note:</strong> Some of the proposed scenes come from:</p> <ul> <li><a href="https://pbrt.org/scenes-v3">https://pbrt.org/scenes-v3</a></li> <li><a href="https://benedikt-bitterli.me/resources/">https://benedikt-bitterli.me/resources/</a></li> </ul> <p><strong>Funding:</strong> This research was funded by ANR support: project ANR-17-CE38-0009.</p> <p> </p>
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
German image spectral library of urban surface materials
<p>The German image spectral library consists of 5102 labelled image spectra of urban surface materials covering the spectral wavelength range between 455 nm and 2449 nm. The spectra have been extracted from high resolution imaging spectroscopy data (HyMap) acquired over the German cities of Dresden (18/05/1999, 01/08/2000, 20/07/2003), Potsdam (18/05/1999) and Munich (17/06/2007, 25/06/2007). This image data package ensures the collection of the most typical urban surface materials including their variations due to different illumination, alteration, observation conditions, regional specifications and data processing characteristics.</p> <p>The collection was done in two main steps: (1) manual collection of spectrally pure urban surface material pixels from the Dresden and Potsdam data sets including additional information, such as the results of field investigations, a field spectral library and color infrared aerial imagery (Heiden et al., 2007 ) and subsequent reduction for redundant pixel spectra; (2) spectral dissimilarity analysis to include and label meaningful unknow spectra from the Munich data set (Jilge et al. 2017 ). </p> <p>The image spectra are labelled based on three sets of spectra labels: one for EAGLE land cover (EAGLE_LCC, consult the “Explanatory Documentation of the EAGLE Concept” from the Copernicus Land website) , one for generalized material groupings (GENLIB_LCH_BuC_MG) and one for more detailed artificial material type (GENLIB_LCH_BuC_AMT).</p> <p>While every effort was made to ensure accurate information, this data set is presented "as is" without warranties of any kind. The authors accept no liability or responsibility to any person as a consequence of any reliance upon the data presented here. The user assumes all responsibility and risk for the use of this data.</p>
Urban material ground truth data for the 2007 HyMap hyperspectral image of Munich
<p><span>This dataset entails a spectral library file (.sli file with matching .hdr text file) with 12028 labeled spectra derived from the 4m resolution airborne hyperspectral HyMap image of Munich (Germany) that was acquired during the summer of 2007 (June 17 and 25 2007). The labeled image spectra are retrieved from pixels of the HyMap dataset that has been processed to level 2A surface reflectance in 119 bands ranging between the wavelengths of 455 nm and 2496 nm. The preprocessing performed on this image data is explained in Heldens et al. (2008) and Heiden et al. (2012). See the "Related works" section of this data publication.</span></p> <p><span>The ground truth (GT) data have been used in previous research (again, see the "Related works" section) and they were likewise used for the remote sensing-based mapping experiments with a generic urban spectral library performed in the frame of the GENLIB research project. The data set contains reflectance spectra of typical urban surface materials and their spectral variations.</span></p> <p><span>The spectra included in this dataset were sampled from the above mentioned HyMap image by (1) using the methodology described in Jilge et al. (2017) and (2) through the delineation of manually digitized regions of interest. The image spectra are <span> </span>labeled based on the method mentioned above and using ancillary reference data, already published urban spectral libraries, terrain knowledge and some field work. The header of the spectral library contains the various labels that were added to the image spectra. These labels cover:</span></p> <ul> <li><span>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the </span><span><a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener"><span>website of the EAGLE framework</span></a></span><span> for more information.</span></li> <li><span>Material Groups (MG).</span></li> <li><span>Artificial Material Types (AMT).</span></li> </ul> <p><span>The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication.</span></p> <p><span>While considerable efforts have been made to safeguard the accuracy of these data, they are published as is, without any warranty or support. Use at your own discretion.</span></p>
Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network
<p>Datasets acquired and generated for the manuscript "Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network". The datasets include test, training and time series datasets each containing the raw data and the predicted data where it applies. </p>
RCS-Image Dataset
<p>Note: There is a single zip file of the project available under the name "<a href="https://zenodo.org/api/files/30cdde03-6e96-4019-9a1a-bef54f3d2737/RCS%20Image%20Dataset.zip">RCS Image Dataset.zip </a>"</p> <p>A synthetic dataset for object detection, semantic segmentation and depth recognition was created using images from a virtual environment in Unreal Engine. This dataset that could be used to retrain neural networks on virtual aerial images, for object detection, segmentation and depth planning.</p> <p>There is ground truth for full pixel level semantic segmentation and object detction by way of bounding box coordinates in .exif files. The main labels present in the dataset are train truck and gas cylinders The size of the data is currently more than 10GB, with 1000 aerial images, from 100 different waypoints at 10 different heights. For each raw image, there is also an associated depth image, a segmented image, object ground truths with bounding box, and the metadata .exif file.</p> <p>If you found this dataset useful for your reserach, please cite as,</p> <pre>@article{smyth2018virtual, title={A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation}, author={Smyth, David L and Fennell, James and Abinesh, Sai and Karimi, Nazli B and Glavin, Frank G and Ullah, Ihsan and Drury, Brett and Madden, Michael G}, journal={arXiv preprint arXiv:1806.04497}, year={2018} }</pre> <p> </p>
102 hpf medaka embryos in 96 well plate (4 embryo/well) - brightfield - 2X magnification - ACQUIFER Imaging Machine
<p>Dataset originates from:</p> <p>Gierten, J., Pylatiuk, C., Hammouda, O. T., Schock, C., Stegmaier, J., Wittbrodt, J., Gehrig, J. and Loosli, F. (2020). <strong>Automated high-throughput heartbeat quantification in medaka and zebrafish embryos under physiological conditions</strong>. Sci Rep <em>10</em>, 2046, doi:<a href="https://doi.org/10.1038/s41598-020-58563-w">10.1038/s41598-020-58563-w</a>.</p> <p>Used as benchmark dataset for Multi-Template-Matching by Thomas and Gehrig </p> <p>See implementation in Fiji <a href="https://github.com/LauLauThom/MultipleTemplateMatching">https://github.com/LauLauThom/MultipleTemplateMatching</a></p> <p>and in KNIME <a href="https://github.com/LauLauThom/MultipleTemplateMatching-KNIME">https://github.com/LauLauThom/MultipleTemplateMatching-KNIME</a></p> <p>Contacts: j.gehrig(at)acquifer.de, l.thomas(at)acquifer.de, jakob.gierten(at)cos.uni-heidelberg.de</p>
Image data of co-localization of IgG and HEV ORF2 protein in a case of hepatitis E-associated kidney disease
<p><span>Image data for a co-localization study of IgG with HEV ORF2 protein in a </span><span>de novo immune complex-mediated glomerulonephritis (GN) case in</span><span> a kidney transplant recipient </span><span>with chronic hepatitis E (Leblond and Helmchen, et al. 2024).<span> </span>Immunofluorescence images are provided for 25 glomeruli at low magnification (20x, 0.227 micron/pixel) and for 16 glomeruli at high magnification (100x, 0.0454 micron/pixel). For each example glomeruli the green channel represents IgG antibody staining with FITC, and the magenta channel represent anti-HEV ORF2 staining using Alexa Fluor 546.</span></p> <p><span>Methods: </span></p> <p><span>Mouse monoclonal antibody clone 1E6 against the HEV ORF2 protein was incubated for 1h at a dilution of 1:125 followed by a mix of Alexa Fluor 546-conjugated goat anti-mouse antibody (Invitrogen BV, A11018) and FITC-conjugated Rabbit anti-Human IgG (Gamma chain, Diagnostic Biosystem, F008) for 1hat a dilution of 1:50. Following automated staining, the slides were hand -washed in distilled H<sub>2</sub>O. Tissue was covered with Vectashield® Antifade Mounting Medium with DAPI (VectorLaboratories, H-1200), covered with a coverslip and stored at 4°C until evaluation.</span></p> <p><span>Immunofluorescence images were acquired with an upright fluorescence microscope (AxioImager.Z2 controlled by ZEN Blue software; 89 North Photofluor LM-75 light source, and Axiocam 503 mono camera; Zeiss, Jena, Germany), equipped with the following objectives: 20x (NA 0.5, Plan-NEOFLUAR), 40x (NA 1.4 oil, Plan-APOCHROMAT), and 100x (NA 1.45 oil, Plan-APOCHROMAT) objectives. This setup provides an excellent spatial resolution (nominally about 200 nm lateral resolution in our study; pixel size was 45.4 nm for 100x objective). High resolution images were taken with the 100x objective using the ApoTome.2 module with deconvolution (grid 5 lp/mm; section thickness 0.7 µm). We used Vysis Abbott Chroma filter sets (Blue: excitation (ex) 335-383 nm, emission (em) 420-470; green: ex 481-507 nm; em 521-551 nm; red: ex 534-556 nm, em 574- 606 nm). Co-localization of IgG and HEV ORF2 staining was quantified using Fiji software (Schindelin et al., 2012) and the JACoP ImageJ plug-in. </span></p>
Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission by Roman et al.
<p>Images of Neptune as used in <a href="https://arxiv.org/abs/2112.00033"><strong>Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission </strong></a>by Michael T. Roman, Leigh N. Fletcher, Glenn S. Orton, Thomas K. Greathouse, Julianne I. Moses, Naomi Rowe-Gurney, Patrick G. J. Irwin, Arrate Antunano, James Sinclair, Yasumasa Kasaba, Takuya Fujiyoshi, Imke de Pater, Heidi B. Hammel.</p> <p>Data are from various observatories/telescope instruments, including:</p> <ul> <li>The European Southern Observatory's Very Large Telescope, VISIR</li> <li>Keck Observatory, LWS</li> <li>Subaru Observatory, COMICS</li> <li>Gemini North, Michelle and TEXES</li> <li>Gemini South, T-ReCS</li> </ul> <p>Data were acquired from observatory archives and flux calibrated, when possible, by comparison to standard stars, with spectral radiances expressed in units of W/m<sup>2</sup>/sr/micron. North is up in the images. The first extension (ext=0) is the image in native spatial resolution. The second extension (ext=1) features the disk normalized in size to that of the finest data (i.e., to a disk with an equatorial width of 51.8 pixels, as imaged by VLT-VISIR on August 13, 2018).</p> <p>Image file names and times correspond to approximate mid-time of combined image sequences, and will differ from original file headers.</p> <p>Questions concerning these data should be directed towards Michael Roman, m.t.roman@le.ac.uk or michael.thomas.roman@gmail.com</p> <p> </p> <p> </p>
Invertebrates from the ANTARXXVII Leg1 expedition to the Bransfield Strait, Antarctica - images
<p>This upload contains photographs of Arthropoda, Nemertea, Mollusca, Annelida, Echinodermata and Nematoda samples from Admiralty bay, Bransfield Strait and Maxwell Bay taken by Louraine Salabao and Jolien Claes during the first leg of the ANTARXXVII campaign in the Southern Ocean aboard BAP Carrasco from December 24, 2019 to January 25, 2020.</p> <p>The occurrence dataset is available at https://ipt.biodiversity.aq/resource?r=antarxxvii-leg1, published by SCAR-AntOBIS under the license CC-BY 4.0. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via data-biodiversity-aq@naturalsciences.be.</p> <p>This dataset is part of the Refugia and Ecosystem Tolerance in the Southern Ocean (RECTO) project funded by Belgium Science Policy (BELSPO).</p>
Crowd4SDG - Crowdsourced image classification and damage assessment
<p>This data set contains crowdsourced classification and damage assessment of images of an earthquake extracted from social media. <br> <br> A data set of 907 images posted on Twitter related to the 2019 Albanian Earthquake, that are filtered and pre-classified using an automated technique is cross-validated for accuracy by two different crowds. One, digital humanitarian volunteers using the crowdsourcing platform <a href="http://www.crowd4ems.org">CROWD4EMS</a> and another, paid micro-taskers of the Amazon Mechanical Turk. In order to compare and evaluate the efficiency and accuracy of the volunteers and the paid micro taskers, ground truth is established with the help of a team of experts, who validated the same set of data. <br> <br> <strong>Parameters considered for volunteer contributions:</strong> The dataset was imported to the Crowd4EMS platform for Crowd contribution. In the forum, each volunteer will see the image to be validated along with the tweet text and the link to the original tweet. The user has to validate whether the given image is <em>relevant or</em> <em>irrelevant</em> to the disaster. In case of doubt, the user can refer to the tutorial explaining the relevance or skip the task. Once the image's relevance is validated, the user will be asked to label the <em>severity</em> of the impact, as seen in the image.</p> <p>The Automated algorithm has pre-classified the images as <em>severe </em>and <em>minimal </em>damage. The Crowd4EMS platform lets the volunteer label them as '<em>severe damage</em>,' <em>moderate damage'</em>,' <em>minimal damage', </em>and' <em>no damage'.</em> Each task has to be answered <em>at least three times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Parameters considered for micro-taskers contribution:</strong> The dataset was imported to the <em>Amazon Mechanical Turk</em> platform for Crowd contribution. In the platform, each worker will see only the image that is to be categorised as follows: The user has to validate whether the given image depicts <em>severe damage, moderate damage, minimal damage, no damage </em>or <em>irrelevant</em> to the disaster. Each task has to be answered <em>at least ten times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Acknowledgements:</strong> We want to thank Muhammad Imran of Qatar Computing Research Institute for sharing their pre-filtered social media imagery dataset on the Albanian earthquake from the Artificial Intelligence for Disaster Response (AIDR) Platform. We would also like to extend our gratitude to the volunteers for their contribution on the Crowd4EMS Platform.<br> </p>
Data from the behavioural and Magnetic resonance imaging of the Ts66Yah and Ts65Dn male model of Down syndrome
<p>Please find enclosed the behavioural and Magnetic Resonnance Imaging (MRI) variables used for comparing the Ts66Yah DS models with the parental line Ts65Dn. The raw data are found as two CVS files</p> <p>- Behavioural phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>- MRI phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>while the processed data used for the GDAPHEN analysis (https://github.com/YaH44/GDAPHEN/releases/tag/Public) are available as Excel docs.</p> <p> </p> <p>The processing has been done with a low level of imputation for missing data detailed in the Formating_decision_phenoParameters_Ts65Dn_Ts66Yah. ...</p>
Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum - data
<p>Data set pertaining to the article "Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum", published in <em>Struct. Dyn.</em> 10, 034901 (2023), <a href="https://doi.org/10.1063/4.0000188" target="_blank" rel="noopener">https://doi.org/10.1063/4.0000188 </a>.</p> <p>The following data are provided:</p> <table> <tbody> <tr> <td>(zip-)file/Folder</td> <td>Description</td> <td>Format</td> <td>Extension</td> </tr> <tr> <td>IR_images/calibration_data/vacuum</td> <td> <p>Snapshots from a thermographic movie of our flat jet running in vacuum, at thirty different background temperature. (A snapshot shown in Fig. 3a, rhs.)</p> </td> <td> <p>temperature values per camera pixel (°C), 640 row * 480 columns, semicolon-separated ascii data</p> </td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/1atm</td> <td>As above, for our flat jet running in atmosphere. (Three snapshots shown in Fig. 2a.)</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/chipnozzle</td> <td>As above, for a flat jet produced from a chip nozzle, and running in atmosphere.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/raw_data</td> <td>As above, for various conditions of the flat jet environment as detailed in table exp_settings.csv.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_video</td> <td>Two thermographic movies recorded of our flat jet at varied conditions of the jet environment detailed in table chamber_pressure.pdf.</td> <td>Radiographic image stream, suitable for opening with free software Optris Pix Connect.</td> <td>.ravi</td> </tr> <tr> <td>FJ_cooling_2D.mph</td> <td>Input file for 2D finite element simulation of our flat jet.</td> <td>Input file suitable for Comsol software, proprietary format.</td> <td>.mph</td> </tr> <tr> <td>Y_Z_Temp_Comsol.txt</td> <td>Ascii representation of our simulated temperature profile (Fig. S7 (SI)).</td> <td>List of (y,z,T) tupels, with (y,z) in m and T in °C.</td> <td>.txt</td> </tr> </tbody> </table> <p> </p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Large SEM-BSE images of hydrated alite of ages from 1 day up to 1 year
<p>This dataset contains 8-Bit SEM-BSE images of commercially available tricalcium silicate (C<sub>3</sub>S; alite, MIII polymorph; Vustah, Czech Republic). The alite was mixed with a water/binder ratio of 0.5. The paste was the cast in small sealed containers, which were submersed with water. The specimens were stored at 22 ± 2°C.</p> <p>After the desired hydration times (1, 7, 14, 28, 84, 365 days) the hydration was stopped by immersing the prisms in isopropanol and drying them at 60°C for 12 hours. The dried prisms were then embedded in low viscosity epoxy resin and mechanically polished using diamond paste with a grain size down to 0.25 µm. Finally, the specimens were coatet with a thin layer of carbon to avoid charging.</p> <p>The images were acquired at 10 kV (7 days, smaller image), 12 kV (7 - 365 days) and 15 kV (1 days) using a CBS (concentric backscatter, 14-365 days) and a ABS (1 and 7 days) detector within a Thermofischer Helios G4 UX.</p> <p><strong>Table 1</strong>: Basic information like resolution, size and phase composition of the images.</p> <table> <tbody> <tr> <td><strong>file</strong></td> <td><strong>age</strong></td> <td><strong>size</strong></td> <td><strong>size</strong></td> <td><strong>area</strong></td> <td><strong>pores</strong></td> <td><strong>hydrates</strong></td> <td><strong>clinker</strong></td> </tr> <tr> <td> </td> <td>in days</td> <td>in px</td> <td>in µm</td> <td>in mm²</td> <td>area-%</td> <td>area-%</td> <td>area-%</td> </tr> <tr> <td>C3S 1d.tif</td> <td>1</td> <td>21179 x 21495</td> <td>749.9 x 749.9</td> <td>0.56</td> <td>38.5</td> <td>38</td> <td>23.9</td> </tr> <tr> <td>C3S 7d.tif</td> <td>7</td> <td>19433 x 19320</td> <td>390.9 x 390.9</td> <td>0.15</td> <td>32.2</td> <td>48.5</td> <td>19.4</td> </tr> <tr> <td>C3S 7d_2.tif</td> <td>7</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>28.5</td> <td>52.8</td> <td>19.1</td> </tr> <tr> <td>C3S 14d.tif</td> <td>14</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>22.2</td> <td>62.7</td> <td>15.4</td> </tr> <tr> <td>C3S 28d.tif</td> <td>28</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>16.9</td> <td>74.9</td> <td>8.3</td> </tr> <tr> <td>C3S 84d.tif</td> <td>84</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>21.7</td> <td>72.7</td> <td>5.7</td> </tr> <tr> <td>C3S 365d.tif</td> <td>365</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>14.8</td> <td>83.2</td> <td>2.0</td> </tr> </tbody> </table> <p>The proportions of pores, hydrates and unhydrated clinker shown in Table 1 are the result of manual thresholding of denoised versions of these images and may therefore differ to own measurements.</p> <p>The scaling is backed into the file and can be read using ImageJ/Fiji.</p> <p>The unstitched files are provded as 7z archives. The sub-images were arranged in a 10 x 10 grid, with the exception of the 7 days image, which was arranged in a 9x9 grid. The pixel scaling of these files is the same as in the larger files. The unstitched files for the 1 day specimen can be provided on request.</p> <p><strong>Internal note</strong></p> <p>These files are included in the following MAPS datasets:</p> <ul> <li>2019_04_15 FK C3S 1d</li> <li>2019_04_23 C3S 7d 15 BIB</li> <li>2023_05_24 C32-C2S 14-84 d</li> <li>2023_06_08 C2S-C3S 28d-1year</li> <li>2023_07_18 C3S 7d, C2S 1d, 7d, 3C3S-1C2S 7d</li> </ul> <p><strong>Changelog</strong></p> <ul> <li>2023-08-03, V1.1 Added new dataset (C3S 7d_2.tif).</li> <li>2024-02-07, V1.1 modified description (error in hydrate/C<sub>3</sub>S measurement for the 14 days dataset)</li> </ul>
Estimation of Abundance and Distribution of Salt Marsh Plants from Images Using Deep Learning
Recent advances in computer vision and machine learning, most notably deep convolutional neural networks (CNNs), are exploited to identify and localize various plant species in salt marsh images. Three different approaches are explored that provide estimations of abundance and spatial distribution at varying levels of granularity in terms of spatial resolution. In the coarsest-grained approach, CNNs are tasked with identifying which of six plant species are present/absent in large patches within the salt marsh images. CNNs with diverse topological properties and attention mechanisms are shown capable of providing accurate estimations with > 90% precision and recall in the case of the more abundant plant species whereas the performance of the CNNs is observed to decline in the case of less common plant species. Estimation of percent cover of each plant species is performed at a finer spatial resolution, where smaller image patches are extracted and the CNNs tasked with identifying the plant species or substrate at the center of the image patch. In an ecological setting, several image patches (~100) are extracted and classified using this approach to estimate the percent cover of the various plant species in the image. For the percent cover estimation task, the CNNs are observed to exhibit a performance profile similar to that for the presence/absence estimation task, but with an ~ 5–10% reduction in precision and recall. Finally, estimation of the spatial distribution of the various plant species is performed via semantic segmentation of the input images at the finest level of granularity in terms of spatial resolution. The Deeplab-V3 semantic segmentation architecture is observed to provide very accurate estimations for abundant plant species; however, a significant degradation in performance is observed in the case of less abundant plant species and, in extreme cases, rare plant classes are seen to be ignored entirely. Overall, a clear trade-off is observed between
Daily phenocam image data and derived timeseries for global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains daily data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data included here comes from phenocams installed in two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and this dataset includes color values extracted from shrub and grass regions in these images. Further analyses, including daily values of calculated greenness (green chromatic coordinate), precipitation, and temperature, for all the plots included in the study are in EDI dataset knb-lter-jrn.210574002. This study is ongoing.
3D Tomography Images of wheat grains for several development stages
<p>Images of wheat grains acquired by 3D tomography at various stages of the early development of the grain. This data set serves as companion for the article "Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development" submitted to the "Plant Methods" journal.</p> <p><strong>Grain samples</strong></p> <p>A total of 41grains obtained at height different stages was imaged. The files correspond to the collections of grains at each stage:</p> <ul> <li>060 degree-days: 5 grains</li> <li>080 degree-days: 5 grains</li> <li>100 degree-days: 5 grains</li> <li>120 degree-days: 5 grains</li> <li>180 degree-days: 6 grains</li> <li>210 degree-days: 5 grains</li> <li>270 degree-days: 5 grains</li> <li>310 degree-days: 5 grains</li> </ul> <p>A more detailed description is provided in the file "<a href="https://zenodo.org/api/files/923a7508-b325-4264-8553-6a62092bb84d/wheatGrainTomoDataset.pdf">wheatGrainTomoDataset.pdf</a>".</p> <p><strong>Image format</strong></p> <p>All images are in TIFF format.</p> <p>Two kinds of images are provided: the volumes of the whole grains after conversion tu 256 gray levels, and the results of the segmentation of the grains as described in the manuscript. </p>
Galaxy Zoo 2: Images from Original Sample
<p>The Galaxy Zoo team regularly receives requests for subject images for various versions of Galaxy Zoo, in order to facilitate other investigations, e.g. machine learning projects. This repository is an updated attempt to provide those in a way that is useful to the wider community.</p> <p>The images here are meant to be used with the data tables available at <a href="http://data.galaxyzoo.org">data.galaxyzoo.org</a>. They are the "original" sample of subject images in Galaxy Zoo 2 (Willett et al. 2013, MNRAS, 435, 2835, DOI: <a href="https://doi.org/10.1093/mnras/stt1458">10.1093/mnras/stt1458</a>) as identified in Table 1 of Willett et al. and also in Hart et al. (2016, MNRAS, 461, 3663, DOI: <a href="https://doi.org/10.1093/mnras/stw1588">10.1093/mnras/stw1588</a>). The original GZ2 subjects also gave the option to view an inverted version of the subject image; these inverted images are not provided but are easily reproducible from the included subject images. </p> <p><strong>If you use this dataset, please cite</strong> Willett et al. (2013) as the general data release and <em>also</em> cite the DOI for this dataset; if you use the updated debiased tables from Hart et al. (2016) please cite that as well.</p> <p>There are 243,434 images in total. This is off by about 0.08% from the total count in the tables - it's not clear what the cause of the discrepancy is, but we don't think the missing images have any particular sampling bias, so this sample should be useful for research.</p> <p>The images are available in a single zip file (<strong>images_gz2.zip</strong>).</p> <p>The most recent and reliable source for morphology measurements is "GZ2 - Table 1 - Normal-depth sample with new debiasing method – CSV" (from Hart et al. 2016), which is available at <a href="https://data.galaxyzoo.org">data.galaxyzoo.org</a>. To cross-reference the images with Table 1, this sample includes another CSV table (<strong>gz2_filename_mapping.csv</strong>) which contains three columns and 355,990 rows. The columns are:</p> <ul> <li><strong>objid</strong>: the Data Release 7 (DR7) object ID for each galaxy. This should match the first column in Table 1.</li> <li><strong>sample</strong>: string indicating the subsampling of the galaxy. </li> <li><strong>asset_id</strong>: an integer that corresponds to the filename of the image in the zipped file linked above.</li> </ul> <p>As an example row:</p> <p>587722981742084144,original,16</p> <p>The galaxy is 587722981741363294, which is in Table 1 and was identified by GZ2 volunteers as a barred spiral galaxy with a mild bulge and two tightly-wound arms (morphology='Sc2t'). It is in the original GZ2 sample, and can be found in the zipped file as 16.jpg. </p> <p>The overlap between the set of images, the attached table, and Table 1 is not 100%; there are a few rows in the tables that don't have a corresponding image. Again, it's not clear what the exact reason is for this, but we suggest just dropping any missing rows/images from your analysis unless you have a need for analyzing specific subjects. If you do need a 100% complete sample, you can obtain the missing images directly from SDSS. </p> <p>Based on spot checks the mappings between asset ID and DR7 object ID appear correct, but we strongly suggest that you pick some random images and verify on your own that the image seems to match the label/classifications that are listed in Table 1. </p> <p>If you have any issues using this dataset, please contact the Galaxy Zoo team, in particular Brooke Simmons (b.simmons@lancaster.ac.uk). Should Dr Simmons be unavailable, try contacting Karen Masters or Chris Lintott.</p> <p>- the GZ team, 5 Dec 2019<br> </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.