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8 results for “Nanotomography”
Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"
<p>Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"</p>
Supplementary dataset for "Magnetic recording fidelity of basalts through 3D nanotomography, 2024"
<p>This repository contains raw data and scripts needed to reproduce the results presented in "Magnetic recording fidelity of basalts through 3D nanotomography, 2024". These include slice-and-view image stacks (<a href="../api/records/11369780/draft/files/VesuviusSnVTiffStack.tif/content" target="_blank" rel="noopener noreferrer">VesuviusSnVTiffStack.tif</a> for the Vesuvius dataset and <a href="../api/records/11369780/draft/files/HeklaSnVTiffStack.tif/content" target="_blank" rel="noopener noreferrer">HeklaSnVTiffStack.tif</a> for the Hekla Volume) for the samples discussed in the manuscript. These were used to generate 3D meshes of magnetite grains in the volume using the methodology described in the manuscript. <a href="../api/records/11369780/draft/files/Individual%20Meshes%20Vesuvius.7z/content" target="_blank" rel="noopener noreferrer">Individual Meshes Vesuvius.7z</a> and <a href="../api/records/11369780/draft/files/Individual%20Meshes%20Hekla.7z/content" target="_blank" rel="noopener noreferrer">Individual Meshes Hekla.7z</a> contain the individual 3D mesh .pat files, while <a href="../api/records/11369780/draft/files/Hekla%20Full%20Volume.stl/content" target="_blank" rel="noopener noreferrer">Hekla Full Volume.stl</a> and <a href="../api/records/11369780/draft/files/Vesuvius%20Full%20Volume.stl/content" target="_blank" rel="noopener noreferrer">Vesuvius Full Volume.stl</a> show full 3D representations of the studied volumes. The individual mesh files can be used as geometry inputs for micromagnetic simulations using the MERRILL suite . Example MERRILL scripts are also included, with <a href="../api/records/11369780/draft/files/LEM_StateMerrilScript.merrill/content" target="_blank" rel="noopener noreferrer">LEM_StateMerrilScript.merrill</a> showing an example of a script used to determine the local energy minimum (LEM) state of a magnetic grain and <a href="../api/records/11369780/draft/files/NEB_Merril_Script.merrill/content" target="_blank" rel="noopener noreferrer">NEB_Merril_Script.merrill</a> showing a script to determine the energy barriers between LEM states used in the calculation of relaxation times. Finally ".csv" files containing grain metrics are also included for both of the studied samples ( <a href="../api/records/11369780/draft/files/HeklaGrainMetrics.csv/content" target="_blank" rel="noopener noreferrer">HeklaGrainMetrics.csv</a> and <a href="../api/records/11369780/draft/files/VesuviusGrainMetrics.csv/content" target="_blank" rel="noopener noreferrer">VesuviusGrainMetrics.csv</a> ). These contain information on the size of the individual particles, their morphology, the LEM states they support and the energy barries from the NEB calculation.</p>
Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software
<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: <strong>- Dynamic_PEFC_data.h5</strong> # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z <strong>- liquid_simulation.h5</strong> # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). <strong>- phasetran_simulation.h5</strong> # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: <strong>- astra_nonrigid.zip</strong> # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. <strong>- reconstruction_PEFC.py</strong> # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * <strong>!!!</strong> Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. <strong>- reconstruction_simulation.py</strong> # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>
Original reconstructions for the research in "X-ray nanotomography of individual pulp fibre bonds reveals the effect of wall thickness on contact area"
<p>The following data set contains the original cropped and straightened reconstruction stacks of all the cellulose fibre bond samples imaged for the study outlined in the article<strong> “</strong>X-ray nanotomography of individual pulp fibre bonds reveals the effect of wall thickness on contact area” by T. Sormunen, A. Ketola, A. Miettinen, J. Parkkonen and E. Retulainen. The article is currently (23.11.2018) in decision phase.</p> <p>In addition, the algorithm for reconstruction stack processing conducted in ImageJ and the MATLAB function for contact area and pixelwise correlation calculations are included.</p>
Three‐dimensional reconstruction of porous polymer films from FIB‐SEM nanotomography data using random forests
<p>Dataset and code used in M. Röding, et al, "Three-dimensional reconstruction of porous polymer films from FIB-SEM nanotomography data using random forests", published in Journal of Microscopy, 2020. In this work, we develop a segmentation method for focused ion beam scanning electron microscopy (FIB-SEM) data acquired by volumetric imaging of porous polymer films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. This type of polymer films are used for controlled release applications. Based on manual segmentation of a fraction of the data, a random forest classifier is trained and applied to the full data set. Here, raw data, manual segmentations, and the Matlab code used for all steps in the analysis are supplied.</p>
Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization
<p>This is the raw data for the manuscript:</p><p>Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization.</p><p>A readme file containing all descriptions can be found in the main folder. All data are sorted in a separate subfolder each according to the three different types of datasets used in the main manuscript. Further, the Python scripts used for tomographic reconstruction are to be found in the zip file.</p><p>Abstract:</p><p>Compressed sensing is an image reconstruction technique to achieve high-quality results from limited amount of data. In order to achieve this, it utilizes prior knowledge about the samples that shall be reconstructed. Focusing on image reconstruction in nanotomography, this work proposes enhancements by including additional problem-specific knowledge. In more detail, we propose further classes of algebraic inequalities that are added to the compressed sensing model. The first consists in a valid<br>upper bound on the pixel brightness. It only exploits general information about the projections and is thus applicable to a broad range of reconstruction problems. The second class is applicable whenever the sample material is of roughly homogeneous composition. The model favors a constant density and penalizes deviations from it. The resulting mathematical optimization models are algorithmically tractable and can be solved to global optimality by state-of-the-art available implementations of interior point methods. In order to evaluate the novel models, obtained results are compared to existing image reconstruction methods, tested on simulated and experimental data sets. The experimental data comprise one 360° electron tomography tilt series of a macroporous zeolite particle and one absorption contrast nano X-ray computed tomography (nano-CT) data set of a copper microlattice structure. The enriched models are optimized quickly and show improved reconstruction quality, outperforming the existing models. Promisingly, our approach yields superior reconstruction results, particularly when information about the samples is available for a small number of tilt angles only.</p>
Alignment methods for nanotomography with deep sub-pixel accuracy
<p>This repository contains codes for alignment of projections for tomography and laminography. Additionally we include artificial, i.e. simulated, and experimental examples.</p> <p><strong>Here you will find:</strong></p> <p>1) <em>cSAXS_matlab_tomo_shared.zip</em> - Codes and routines, as introduced in [1], for deep sub-pixel alignment and reconstruction of tomography and laminography datasets.</p> <p>2) <em>example_data.mat</em> - artificial tomography dataset that serves as an example for the alignment codes.</p> <p><br> <strong>Citation and acknowledgements</strong></p> <p>If you use the codes from this repository here is where to find more information and the expected citation.</p> <p>For use or further development of the tomography and laminography alignment and reconstruction codes please cite:<br> [1] M. Odstrcil, M. Holler, J. Holler, M. Guizar-Sicairos,"Alignment methods for nanotomography with deep sub-pixel accuracy", Opt. Express, (2019).</p> <p><br> <strong>Code requirements:</strong></p> <p>Reconstruction scripts were tested for Matlab2018a with parallel toolkit, CUDA 9.0. NVIDIA, RHEL 7.6<br> GPU is required for the tomographic reconstruction.</p> <p><br> <strong>Instalation and reconstruction:</strong></p> <p> 1) Download example datasets and code from https://doi.org/10.5281/zenodo.3539550 or a measured ptychotomography dataset from https://doi.org/10.5281/zenodo.3539513<br> 2) Make sure that the downloaded datasets can be loaded by matlab<br> 3) Choose one of the provided datasets in the "run_simple_example.m" template<br> 4) Run script "run_simple_example.m". The final results will be stored in folder defined in par.output_folder</p> <p><br> <strong>Example dataset</strong> contains following two variables:</p> <p><em>stack_object</em> - complex valued unaligned projection with dimensions [Npix_vertical , Npix_horizontal, number_of_angles]</p> <p> <em>theta</em> - vector of corresponding projection angles in degrees</p> <p> </p> <p>This code and subroutines are part of a continuous development. There is no liability on PSI or cSAXS. License for the codes can be found in the individual scripts.</p>
Calcium aluminate cement conversion analysed by ptychographic nanotomography
<p><strong>Raw data for: </strong><strong><span>Calcium aluminate cement conversion analysed by ptychographic nanotomography</span></strong></p> <p><strong><span>doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.cemconres.2020.106201" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.cemconres.2020.106201</span></a></span></strong></p> <p><strong>Calcium aluminate cements are used for special applications but are nowadays banned for general structural purposes. This banning is due to the calcium aluminate hydrate conversion, that has led, for concretes fabricated with high water contents, to many building collapses. The stoichiometries of these conversion chemical reactions are relatively well established but the consequences in porosity, key to predict and ensure durability, were unknown. Here, we have used hard X-ray ptychographic nanotomography to study the hydration of CaAl<sub>2</sub>O<sub>4</sub> at different temperatures and chiefly, at 4ºC and then at 50ºC to provoke conversion similar to field conditions. T</strong><strong>he mass densities of the resulting Al(OH)<sub>3</sub> gels were 1.94, 1.98 and 2.23 g<sup>.</sup>cm<sup>-3</sup>, for samples hydrated at 4, 20 and 50ºC, respectively. These values are lower than that of gibbsite, 2.42 g<sup>.</sup>cm<sup>-3</sup>, the reference value used so far. Above all, this 3D imaging technique has allowed measuring the secondary water porosity developed in the conversion, which has an average pore dimension close to 140 nm. This work shows the way for porosity studies at the mesoscale in unaltered specimens.</strong></p>
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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.