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167 results for “x-ray imaging”
Damage Localisation in Fresh Cement Mortar Observed via In Situ (Timelapse) X-ray uCT imaging.
<p>This is dataset to paper: Damage Localisation in Fresh Cement Mortar Observed via In Situ (Timelapse) X-ray uCT imaging.</p>
Alpha-Galactosaminidase family GH114 protein from Fusarium solani: X-ray diffraction images
<p>This submission includes h5-files with diffraction images recorded using the Dectris EIGER X 16M detector at the DIAMOND beamline I04. The model of the crystal structure and associated information can be found in the Protein Data Bank entry 9EP6. The model has P 31 2 1 symmetry and three molecules per asymmetric unit. This is a case of crystal pathology – partial disorder. There is electron density for the fourth molecule which could be modelled with occupancy 1/2 and would overlap with a symmetry-related molecule.</p>
LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models
<p>The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named <strong>LigPCDS</strong>. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.</p> <p> </p> <p>The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries.</p> <p>The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% <span lang="EN-GB">[-19.4,20.</span><span lang="EN-GB">2]</span> and 77.4% <span lang="EN-GB">[-11.7,12.1]</span> in terms of Intersection over Union (mIoU) metric and between 62.4% <span lang="EN-GB">[-18.8,19.</span><span lang="EN-GB">7]</span> and 87.0% <span lang="EN-GB">[-8.4,8.8]</span> in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record.</p> <p>The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines. </p> <p>The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand</p> <p>This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.</p>
Annotations to direct and indirect image rotation estimation methods of orthopedic X-ray images
<p>The annotation file contains labels for AP wrist images of the MURA dataset on the center line of the radius bone. The annotations are stored in json format. For each annotated image file of the MURA dataset an entry is provided with the coordinates of the start and end point of the radius' center line.</p>
In situ Bragg Coherent X-ray Diffraction Imaging of Corrosion in a Co-Fe alloy microcrystal
<p>Here we present the final crystal reconstructions and analysis scripts for the paper titled "<em>In situ</em> Bragg coherent X-ray diffraction imaging of corrosion in a Co–Fe alloy microcrystal" published in CrystEngComm, 24(7), 1334-1343, on 18/01/2021. </p> <p><a href="https://doi.org/10.1107/S1600577520016264">https://doi.org/10.1107/S1600577520016264</a></p>
Refinements for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation
<p>Here we present the final crystal reconstructions and analysis scripts for the paper titled "Refinement for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation" published in Journal of Applied Crystallography, 55, 2022. Please see the README file for more information.</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>
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>
X-ray diffraction images of bovine trypsin crystals recorded at the FemtoMAX beamline of Max IV synchrotron facility
<p>The deposition concerns bovine trypsin diffraction images in two wedges. Each image is recorded on a still crystal and separated by 0.1 deg rotation. The x4.tar.gz archive contains summed intensities from individual snapshots at the same orientation, whereas x4_single.tar.gz archive contains single snapshots/orientation. </p>
X-ray diffraction images for PDB 6Z5G: The RSL - sulfonato-calix[8]arene complex, I23 form, citrate pH 4.0, solved by S-SAD
<p>Anomalous diffraction data collected at 5.975 KeV at Swiss Light Source beam line X06DA using a Pilatus 2M-F detector. </p> <p> </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>
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>
ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset
<p>ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations. </p> <p>ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. </p> <p>The dataset structure is as follows: top-level directories "syntax" and "stenosis" contain files for the two dataset objectives, namely: i) vessel branch classification according to the SYNTAX methodology; and ii) stenosis detection. Inside both directories, there are 3 subsets of the dataset, such as "train", "val", and "test". Inside each of those folders, there are 2 lower-level directories - "images", and "annotations". Inside the "images" folder there are images in ".png" format, extracted from DICOM recordings. The "annotations" folders contain single ".JSON" files, which are named in correspondence to the objective, i.e. "train.JSON", "val.JSON", and "test.JSON".</p> <p>The structure of ".JSON" contains three top-level fields: "images", "categories", and "annotations". The "images" field contains the unique "id" of the image in the dataset, its "width" and "height" in pixels, and the "file_name" sub-field, which contains specific information about the image. The "categories" field contains a unique "id" from 1 to 26, and a "name", relating it to the SYNTAX descriptions. The "annotations" field contains a unique "id" of the annotation, "image_id" value, relating it to the specific image from the "images" field, and a "category_id" relating it to the specific category from the "categories" field. The "segmentation" sub-field contains coordinates of mask edge points in "XYXY" format. Bounding box coordinates are given in the "bbox" field in the "XYWH" format, where the first 2 values represent the x and y coordinates of the left-most and top-most points in the segmentation mask. The height and width of the bounding box are determined by the difference between the right-most and bottom-most points and the first two values. Finally, the "area" field provides the total area of the bounding box, calculated as the area of a rectangle.</p> <p> </p> <p>The corresponding Dataset Article will be provided later. </p> <p>[1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions</p>
Alpha-Galactosaminidase family GH191 protein from Environmental sample (99.2% identity to Myxococcus fulvus enzyme): X-ray diffraction images
<p><span>This submission includes a zip archive of diffraction images recorded with the Dectris EIGER X 9M detector at the DIAMOND beamline I04-1. The model of the crystal structure and associated information can be found in the Protein Data Bank entry 9EP5. This is a case of crystal pathology – partial disorder. The model has C 2 2 21 symmetry and two molecules per asymmetric unit with occupancies 1 and 1/3. The molecule with partial occupancy overlaps with a symmetry related molecule.</span></p>
Multi-resolution X-Ray micro-CT images of Bentheimer Sandstones
<p>This dataset consists of multi-resolution X-Ray micro-tomography images of two Bentheimer sandstone rock cores. The rock cores were first used experimentally in [1] with further modelling in [2]. This new dataset is used directly in the publication [3] - preprint available at https://arxiv.org/abs/2111.01270. </p> <p>The original dataset from [1] (of the same rock cores) is hosted on the BGS National Geoscience Data Centre, ID #130625 at dx.doi.org/10.5285/5f899de8-4085-4370-a45e-e613f27e8f1d and there is also a subvolume image dataset, for easier download available on the Digital Rocks Portal, project 229, DOI:10.17612/KT0B-SZ28 at digitalrocksportal.org/projects/229. </p> <p>The images provided herein are from two distinct Bentheimer rock cores -- core 1 and core 2. The cores have diameter, 12.35mm, lengths 73.2mm and 64.7mm, core-averaged porosities of 0.203 and 0.223 and permeabilities of 1.636D and 0.681D for core 1 and 2, respectively. Core 2 has a clear low permeability lamination occurring at 2/3 of the total core length, whereas core 1 has a general fining towards the outlet of the core creating a reduction in porosity [1].</p> <p>The images were acquired with a Zeiss Versa 510 X-Ray CT scanner. We acquired images of two sub volumes from each core, at locations 1/3rd (subvolume 1) and 2/3rds (subvolume 2) of the way along the core length, at resolutions of 2, 6 and 18 microns. We refer to the 2 micron images as high-resolution (HR), the 6 micron images as low-resolution (LR) and the 18 micron images as very-low-resolution (VLR). There are also super-resolution (SR) images created at 2 micron resolution from the LR images, using a deep-learning algorithm. There are also cubic interpolation images created from the LR image - these are labels bicubic. These have a resolution of 2 microns, and size equal to the HR and SR images. Details of the SR and LR Bicubic generation are found in [3]. The following scanning protocols were used for the direct imaging:</p> <p>2 micron images:<br> --We use a 4x microscope objective, an exposure time of 8s, 2x averaged binning, 9001 projections, a scan voltage of 80kV and a power of 7W. Each scan takes approximately 24 hours.</p> <p>6 micron images:<br> --We use a flat panel detector, an exposure time of 0.7s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 14.46 degrees and the fan angle is 22.2 degrees. Each scan takes approximately 1 hour.</p> <p>18 micron images:<br> --We use a 0.4x microscope objective, an exposure time of 1s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 12.65 degrees and the fan angle is 12.65 degrees. Each scan takes approximately 2 hours.</p> <p>We present 4 sets of the images with different levels of processing. All images are mutual registered to each other. Each image filename has a Core#_Subvol#_resolution identifier, either with the actual resolution (e.g. 6) or the short form (e.g. LR). The following name endings are used</p> <p>(1) - '_16bit_LE.raw'. These are the .raw images of little-endian format. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(2) - '_16bit_LE_normalised.raw'. These are the .raw images of little-endian format with normalised greyscale values following the procedure in [1]. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(3) - 'Core1_Subvol1_HR' etc. These are the .tiff images of (2) above, which have been converted to 8 bit. Includes bicubic interpolation images and SR images, but no 16 micron images, since these were not used in the analysis of [3]. 16 images in total. </p> <p>(4) - 'Core1_Subvol1_HR_filtered' etc. These are the .tiff images from (3) above, which have filtered using non-local means filtering. More details are found in [3]. Note there are no SR images here since they are already essentially filtered, and included in (3) above. 12 images in total.</p> <p><br> <strong>References</strong><br> <br> [1] Jackson, S.J., Lin, Q. and Krevor, S. 2020. Representative Elementary Volumes, Hysteresis, and Heterogeneity in Multiphase Flow from the Pore to Continuum Scale. Water Resources Research, 56(6), e2019WR026396</p> <p>[2] Zahasky, C., Jackson, S.J., Lin, Q., and Krevor, S. 2020. Pore network model predictions of Darcy‐scale multiphase flow heterogeneity validated by experiments. Water Resources Research, 56(6), e e2019WR026708.</p> <p>[3] Jackson, S.J, Niu, Y., Manoorkar, S., Mostaghimi, P. and Armstrong, R.T. 2021. Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling. Under review, preprint available at https://arxiv.org/abs/2111.01270 </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>
RefleX: X-ray diffraction images dataset
<p>Image dataset prepared for the RefleX study, described in <em>"Detecting anomalies in X-ray diffraction images using Convolutional Neural Networks"</em><em>.</em> The dataset contains 6311 X-ray diffraction images in 1024x1024 png format (reflex_img_1024_inter_nearest.zip). The repository also contains a file mapping each image to a set of labels (labels.csv) and files describing the assignment of each image to training, validation, and testing sets (labels_train.csv, labels_val.csv, labels_test.csv).</p> <p>The dataset can be used for multi-label classification. Each diffraction image can exhibit any combination of seven classes: Ice ring, Diffuse Scattering, Background Ring, Non-uniform Detector, Loop Scattering, Strong Background, and Artifact.</p>
X-ray diffraction images of Anti-CD20 crystals
<p>Original X-ray diffraction images from Pilatus detector taken at Diamond Light Source Synchrotron (I04 beamline).</p> <p>Images can be read by AXDV (or similar) software.</p> <p>These datasets were used for diffraction and crystallographic analyses reported in Yang et al., Crystals 2019, 9, 230.</p> <p> </p>
Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets
<p>This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++. The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets. The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well. This work is a companion to the paper : "Segmenting root systems in X-ray computed tomography images using level sets" (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 . The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906.</p> <p>Format of the data:</p> <p>Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality. The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset. The pre-processing set is CassavaSlices. The output set for Soybean is SoybeanResultsJul11. The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C. _B is the largest, and only contains the results overwritten on the original X-Ray images. Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.</p> <p> </p> <p> </p><p> </p><p> </p> <p></p> <p></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
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Annotated Behaviour and Observability Dataset (ABODe)
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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.