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38,240 results for “Imaging”
Dataset of Scanning Tunneling Microscopy (STM) images of model surfaces for elementary steps in catalytic reactions
<p>STM images presented in the dataset were recorded by the STRAS research group using a Omicron Variable Temperature STM (VT-STM) microscope, in the TASC laboratory of the CNR-IOM in Trieste.</p> <p>This work has been done within the NFFA-DI project funded by the European Union – NextGenerationEU - Missione 4, “Istruzione e Ricerca” – Componente 2, “Dalla ricerca all'impresa” – Linea di investimento 3.1,“Fondo per la realizzazione di un sistema integrato di infrastrutture di ricerca e innovazione” – Azione 3.1.1, “Creazione di nuove IR o potenziamento di quelle esistenti che concorrono agli obiettivi di Eccellenza Scientifica di Horizon Europe e costituzione di reti”.</p>
SH3-like domain from Penicillium virgatum muramidase: X-ray diffraction images
<p>This submission includes a zip archive of diffraction images recorded with the ADSC QUANTUM 315 CCD detector at the DIAMOND beamline I04 on 2017-06-27. The model of the crystal structure and associated information can be found in the Protein Data Bank entry 8B2G. This is a case of crystal twinning. The data are used in CCP4 Tutorials.</p>
Urban material ground truth data for the 2015 APEX hyperspectral image of Brussels
<p>This dataset entails a spectral library file (.sli file with matching .hdr text file) with 1350 georeferenced and labeled spectra derived from the 2m resolution airborne hyperspectral APEX image of Brussels (Belgium) that was acquired during the summer of 2015. The labeled spectra included in this dataset describe level 2A surface reflectance profiles ranging between 450 and 2431 nm. The original APEX image files can be downloaded via the <a href="https://belair.vito.be/en/belair-data" target="_blank" rel="noopener">Belair website</a>, and the preprocessing performed on this image data is explained in Sterckx et al. (2016) and Vreys et al. (2016). See the "Related works" section of this data publication.</p> <p>The main purpose of this dataset is to provide Ground Truth (GT) data for remote sensing-based mapping experiments with a generic urban spectral library, performed in the frame of the GENLIB research project. The content of this dataset hence focuses on the optical reflectance/absorption behaviour of urban surface materials and their variations.</p> <p>The spectra included in this dataset were manually sampled from the above mentioned APEX image and labeled using ancillary reference data (very high-resolution aerial imagery, Google Street View, LiDAR ...), 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 these spectra. These labels cover:</p> <ul> <li>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the <a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener">website of the EAGLE framework</a> for more information.</li> <li>Material Groups (MG).</li> <li>Artificial Material Types (AMT).</li> <li>Artificial Material Coating or Fabrication (AMCF).</li> <li>Artificial Material Forms (AMF).</li> <li>Latitude (degrees, WGS84).</li> <li>Longitude (degrees, WGS84).</li> </ul> <p>The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication.</p> <p>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.</p>
MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.
<p>This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as:</p> <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> <p>From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
Infection Inspection: Classifications and images of ciprofloxacin-treated Escherichia coli clinical isolates
<p>This dataset includes a .csv file with the image metadata and a folder of RGB images of <i>E. coli</i> grown from clinical isolates with varying concentrations of the antibiotic ciprofloxacin and varying minimum inhibitory concentrations. The <i>E. coli</i> cell membranes are stained with Nile Red and the DNA is stained with DAPI. The details of the image data collection are included in: https://doi.org/10.1038/s42003-023-05524-4. The classification data come from a Zooniverse citizen science project, Infection Inspection. (https://www.zooniverse.org/projects/conor-feehily/infection-inspection) Volunteers learned how to interpret ciprofloxacin response phenotypes as antibiotic-sensitive or antibiotic-resistant, and their classifications are included in the Metadata.csv file.</p><p>This dataset could be used for further analysis into the volunteer classifications, or the image data could be used for further image feature analysis of the ciprofloxacin response phenotypes.</p>
Cross-sectional images from x-ray computed tomography (XCT) of conserved archaeological samples
<p>The repository contains cross-sections of 83 wood samples derived from X-ray computed tomography (CT) data. The samples are a part of the LEIZA reference collection, which were created within the framework of the project "Mass Finds in Archaeological Collections", which was funded by the "Kulturstiftung des Bundes" and the "Kulturstiftung der Länder" from 15.04.2008 to 31.12.2011 as part of the "Program for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur).</p> <p>Around 10 years later, during the CuTAWAY project (ConservaTion And Wod AnalYses), the wood samples were digitized using an in-house laboratory X-ray CT system (Diondo d2, Germany) at HSLU with a nominal voxel size between 27 and 44 μm in order to analyse the structure of the interior. You can download the cross-sectional images of the data here. The 3D data acquisition was carried out during November 2019 - April 2021.</p> <p>The CuTAWAY project was funded by the German Research Association (DFG) and the Swiss National Science Foundation (SNSF) from 2019 to 2023 (CuTAWAY - Conservation and Wood Analyses, DFG - 416877131 and SNSF - 200021E_183684).</p>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions
<p>This data set corresponds to the paper: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions [1] (Experiment: Comprehensive reporting).</p> <p>The key research questions corresponding to this data set were:</p> <p>RQ1: What is the role of challenges for the field of biomedical image analysis (e.g. How many challenges conducted to date? In which fields? For which algorithm categories? Based on which modalities?)</p> <p>RQ2: What is common practice related to challenge design (e.g. choice of metric(s) and ranking methods, number of training/test images, annotation practice etc.)? Are there common standards?</p> <p>RQ3: Does common practice related to challenge reporting allow for reproducibility and adequate interpretation of results?</p> <p>To address these research questions, we aimed to capture all biomedical image analysis challenges that have been conducted up to 2016. To acquire the data, we analyzed the websites hosting/representing biomedical image analysis challenges, namely grand-challenge.org, dreamchallenges.org and kaggle.com as well as websites of main conferences in the field of biomedical image analysis, namely Medical Image Computing and Computer Assisted Intervention (MICCAI), International Symposium on Biomedical Imaging (ISBI), International Society for Optics and Photonics (SPIE) Medical Imaging, Cross Language Evaluation Forum (CLEF), International Conference on Pattern Recognition (ICPR), The American Association of Physicists in Medicine (AAPM), the Single Molecule Localization Microscopy Symposium (SMLMS) and the BioImage Informatics Conference (BII). This yielded a list of 150 challenges with 549 tasks.</p> <p>Next, a tool for instantiating the challenge parameter list introduced in [1] was used by some of the authors (engineers and medical student) to formalize all challenges that met our inclusion criteria as follows: (1) Initially, each challenge was independently formalized by two different observers. (2) The formalization results were automatically compared. In ambiguous cases, when the observers could not agree on the instantiation of a parameter - a third observer was consulted, and a decision was made. When refinements to the parameter list were made, the process was repeated for missing values. Based on the formalized challenge data set, a descriptive statistical analysis was performed to characterize common practice related to challenge design and reporting.</p> <p>[1] Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A. P., Carass, A., Feldmann, C., Frangi, A. F., Full, P. M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B. A., März, K., Maier, O., Maier-Hein, K., Menze, B. H., Müller, H., Neher, P. F., Niessen, W., Rajpoot, N., Sharp, G. C., Sirinukunwattana, K., Speidel, S., Stock, C., Stoyanov, D., Aziz Taha, A., van der Sommen, F., Wang, C.-W., Weber, M.-A., Zheng, G., Jannin, P., Kopp-Schneider, A.: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions. arXiv preprint arXiv:1806.02051 (2018).</p>
Data from Automated plankton image analysis using convolutional neural networks
<p>Datasets and code from Luo et al., "Automated plankton image analysis using convolutional neural networks." Limnology and Oceanography Methods.</p> <p>Data include:</p> <p>1) 42,564 item training library, sorted in 108 classes,</p> <p>2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering.<br> CSV file: Luo_etal_FT_images_pred.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification. <br> CSV file: Luo_etal_confusionmatrix_images.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p> </p> <p>Scripts and programs:</p> <p>1) Segmentation.zip contains the scripts and executables for the segmentation program.</p> <p>2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript (current version available at: https://github.com/btgraham/SparseConvNet or https://github.com/facebookresearch/SparseConvNet)<br> Note that google-sparsehash is necessary for running SparseConvNet.<br> Also, plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want to replicate the classifications.</p>
Third harmonic generation images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>Data set for 11 samples in 3 groups of Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains THG images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Confocal fluorescence microscopy images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>This data set provides complementary measurements to a separate THG data set of the same study: doi: 10.5281/zenodo.1475906</p> <p>Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains confocal fluorescence microscopy images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Rakhtera (रखतेरा Guna, Madhya Pradesh). Inscription above foot prints near a large rock-cut image of Ādinātha
<p><a href="https://siddham.network/inscription/vs1555/">INIG1555</a> Rakhetra or Rakhtera (रखतेरा Ashoknagar, Madhya Pradesh). Inscription above foot prints near a large rock-cut image of Ādinātha.</p>
MADIA_732678_SCRIBA_Bonding images and protocol_01
<p>ONLY METADATA</p> <p>Collection of protocol and images on bonding procedure of:</p> <ul> <li>PDMS on several substrates (glass, silicon wafer, SU-8);</li> <li>a polystyrene transparent top layer on sensors substrate.</li> </ul> <p>Data produced between from April 2017 to November 2017.</p>
Raw diffraction images of pyruvate phosphate dikinase (PPDK), PDB 5JVN
<p>Raw diffration images and processing input files/logs of pyruvate phosphate dikinase (PPDK) from the C<sub>3</sub> plant <em>Flaveria pringlei</em>. The data was used for PDB entry <a href="https://www.ebi.ac.uk/pdbe/entry/pdb/5jvn">5JVN</a> (<a href="https://www.doi.org/10.1038/srep45389">Minges et al. 2017</a>). Data was collected in two helical scans from the same crystal, each consisting of 3600 images (360°/scan, 0.1°/image). Data were cut according to accumulated radiation damage at 3445 and 3400 images respectively. All data was collected from loop-harvested crystals at beamline ID29 at the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using a wavelength of 0.976252 Å and a Pilatus 6M (Dectris, Baden, Switzerland) detector.</p> <p>The crystal belonged to the spacegroup P622 with unit cell constants a, b ~ 250 Å, c ~ 84 Å, α, β, γ ~ 90°.</p> <p>.</p>
ALICE multi camera image set
<p>Images of pinned insects from multiple camera views with associated calibration grid images. This image set is used for label identification and segmentation in the ALICE project. https://doi.org/10.31219/osf.io/s2p73</p>
ALICE machine learning image set
<p>Pinned insect images and corresponding label outlines in JSON format. This image set is used for machine learning of label identification and segmentation for the ALICE project. https://doi.org/10.31219/osf.io/s2p73</p> <p> </p>
Training data for: CoastSat image classification
<p><strong>CoastSat image classification training data </strong></p> <p>CoastSat is an open-source global shoreline mapping toolbox, available at https://github.com/kvos/CoastSat, which enables users to extract time-series of shoreline change from 30+ years of publicly available satellite imagery (Landsat 5, 7, 8 and Sentinel-2).</p> <p>The automated shoreline extraction relies on a classifier (Multilayer Perceptron from scikit-learn) which labels each pixels on the images with one of four classes: sand, water, white-water and other land features.</p> <p>The data used to train the classifier is stored here, the README.md file provides information on the data organisation and content of each file.</p>
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
Human Bony Labyrinth: Co-Registered CT and micro-CT Images, Surface Models and Anatomical Landmarks
<p>This data set consists of 23 specimens of the human bony labyrinth. For each specimen clinical CT (0.15×0.15×0.2 mm3, voxel size) and co-registered microCT (0.06 mm isotropic voxel size) images are available. Image labels for the bony labyrinth are provided for the same image coordinates. From the image labels, 3D surface models were generated. In addition, each specimen has a descriptor file containing the coordinates of anatomical landmarks as well as a cochlear coordinate system. The data set can be used to study the morphology of the inner ear or to evaluate (semi-)automated segmentation algorithms (e.g., for the preoperative planning of surgical procedures such as cochlear implantation).</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.