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19 results for “Microstructure characterization”
Application of X‑ray Microcomputed Tomography for the Static and Dynamic Characterization of the Microstructure of Oleofoams
<p>Raw, greyscale image stacks collected during a X-Ray tomography analysis on cocoa butter-based oleofoams. The dataset is divided into three subsets: aeration, storage and heating, which contain samples that have been aerated for different amounts of time, samples that have been stored for 3 and 15 months at 20 °C, and finally samples that have been heated to the melting point of the stabilizing crystals, respectively. The dataset contains instructions and the scripts for ImageJ and MATLAB (as text files) to process and measure the bubble size distribution, and the thickness of the continous phase.</p>
Figure 8 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 8. Evolution of I(q) vs. q upon drying for (a) Rivecourt and (b) Angeac. The inset is a close-up of the area between 0.01 and 0.07 µm−1.
Figure 5 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 5. Evolution of I(q) vs. q upon wetting for (a) Rivecourt and (b) Angeac. The inset is a close-up of the area between 0.01 and 0.07 µm−1.
Figure 3 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 3. Evolution with time of average grey levels in the wetting experiment for (a) Rivecourt and (b) Angeac. (c) Evolution with time of normalized grey levels. Blue: Rivecourt sample. Red: Angeac sample (see text for details).
Figure 2 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 2. Radiographic images of sample upon sorption experiments. The three pictures on the top are from the Angeac sample, while the four on the bottom are that of Rivecourt. The scale represents 1 cm. The marked areas correspond to the zones used for measuring average grey levels.
Figure 1 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 1. (a) Picture of neutron guide through experimental chamber. (b) Sorption experiment setup and radiographic image obtained. Wood samples were placed in an aluminum cup filled with water. Water appears dark, while aluminum is transparent to neutrons. (c) Desorption experiment setup and radiographic image obtained. Wood samples were wrapped in aluminum foils and placed in a tube, with direct air input (plastic tube, on top).
Figure 7 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 7. Evolution with time of average grey levels in the drying experiment for (a) Rivecourt and (b) Angeac. (c) Evolution with time of normalized grey levels. Blue: Rivecourt sample. Red: Angeac sample (see text for details). (d) Evolution of average grey levels in the drying experiments plotted as a function of the square root of time. Blue: Rivecourt sample. Red: Angeac sample.
Figure 6 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 6. Radiographic images of sample upon desorption experiments. The Angeac sample is on the top, while the Rivecourt sample is on the bottom. The scale represents 1 cm. The marked areas correspond to the zones used for measuring average grey levels. (For Rivecourt, it was done on another sample due to implosion of the sample.)
Figure 4 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 4. Evolution of average grey levels in the wetting experiments plotted as a function of the square root of time. (a) Rivecourt and (b) Angeac.
Cardiac structure discontinuities revealed by ex-vivo microstructural characterization. A focus on the basal inferoseptal left ventricle region
<p>Data and materials regarding the submission of the paper. See Data Avaibility section and https://github.com/valeryozenne/Cardiac-Structure-Database for more information and code.</p>
Tractography derived quantitative estimates of tissue microstructure depend on streamline length: A characterization and method of adjustment.
<p>In respect of the brain imaging files upon which these analyses are based, three participants provided consent for their exemplar, pseudo-anonymised, data to be placed in the public domain. The example R code can be used to analyse these data.</p>
Unsupervised Machine Learning and Cepstral Analysis with 4D-STEM for Characterizing Complex Microstructures of Metallic Alloys
<p>Raw 4D-STEM data of Ni50Ti26Hf20Al4 used for analysis in the publication "Unsupervised Machine Learning and Cepstral Analysis with 4D-STEM for Characterizing Complex Microstructures of Metallic Alloys". Datasets were collected using the electron microscope pixel array detector (EMPAD) with a Themis Z STEM. Custom python scripts used for data analysis are available upon request to one of the corresponding authors.</p>
Dataset: Data of microstructural and magnetic characterization of HPT-deformed Fe-Cu
<p>This dataset contains the raw data of the microstructural and magnetic characterization of Fe and Fe-Cu samples with a Cu-content ranging from 5 at.% to 30 at.% processed by HPT-deformation. </p> <p>The dataset contains data from:</p> <ul> <li>Hardness measurements of all samples</li> <li>SQUID measurements of all samples</li> <li>Magnetostriction measurements of all samples</li> <li>EDS measurements of all Fe-Cu samples</li> <li>Synchrotron X-ray diffraction measurements of selected Fe-Cu samples</li> </ul>
Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs
ClinicalTrials.gov study NCT06690190. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Data from: Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Open the record for dataset details and reuse information.
Materials Data Science for Microstructural Characterization of Archaeological Concrete
<ul> <li>Ancient Roman concrete: volcanic ash, lime, seawater, and volcanic rocks; </li> <li> <p>Baiae concrete was constructed about 55 BCE to 115 CE, and samples were obtained by the Roman Maritime Concrete Survey (ROMACONS) [12] drilling program from 2002 to 2006, which has been the most comprehensive study of Roman marine concrete. The field program extracted core samples from eleven locations throughout the Mediterranean;</p> </li> <li> <p>Imaging performed at ALS, LBNL.</p> </li> </ul>
Raw data for the article: Regional biomechanical characterization of human ascending aortic aneurysms: Microstructure and biaxial mechanical response
<p>The ascending thoracic aortic aneurysm (ATAA) is a permanent dilatation of the vessel with a high risk of adverse events, and shows heterogeneous properties. To investigate regional differences in the biomechanical properties of ATAAs, tissue samples were collected from 10 patients with tricuspid aortic valve phenotype and specimens from minor, anterior, major, and posterior regions were subjected to multi-ratio planar biaxial extension tests and second-harmonic generation (SHG) imaging. Using the data, parameters of a microstructure-motivated constitutive model were obtained considering fiber dispersion. SHG imaging showed disruptions in the organization of the layers. Structural and material parameters did not differ significantly between regions. The non-symmetric fiber dispersion model proposed by Holzapfel et al. [25] was used to fit the data. The mean angle of collagen fibers was negatively correlated between minor and anterior regions, and the parameter associated with collagen fiber stiffness was positively correlated between minor and major regions. Furthermore, correlations were found between the stiffness of the ground matrix and the mean fiber angle, and between the parameter associated with the collagen fiber stiffness and the out-of-plane dispersion parameter in the posterior and minor regions, respectively. The experimental data collected in this study contribute to the biomechanical data available in the literature on human ATAAs. Region-specific parameters for the constitutive models are fundamental to improve the current risk stratification strategies, which are mainly based on aortic size. Such investigations can facilitate the development of more advanced finite element models capable of capturing the regional heterogeneity of pathological tissues. STATEMENT OF SIGNIFICANCE: Tissue samples of human ascending thoracic aortic aneurysms (ATAA) were collected. Samples from four regions underwent multi-ratio planar biaxial extension tests and second-harmonic generation imaging. Region-specific parameters of a microstructure-motivated model considering fiber dispersion were obtained. Structural and material parameters did not differ significantly between regions, however, the mean fiber angle was negatively correlated between minor and anterior regions, and the parameter associated with collagen fiber stiffness was positively correlated between minor and major regions. Furthermore, correlations were found between the stiffness of the ground matrix and the mean fiber angle, and between the parameter associated with the collagen fiber stiffness and the out-of-plane dispersion parameter in the posterior and minor regions, respectively. This study provides a unique set of mechanical and structural data, supporting the microstructural influence on the tissue response. It may facilitate the development of better finite element models capable of capturing the regional tissue heterogeneity.</p>
Degradation of Ni-YSZ and Ni-GDC fuel cells after 1000 h operation: Analysis of different overpotential contributions according to electrochemical and microstructural characterization
<p>Datasets from the paper:</p> <p>"Degradation of Ni-YSZ and Ni-GDC fuel cells after 1000 h operation: Analysis of different overpotential contributions according to electrochemical and microstructural characterization". E3S Web of Conferences <strong>334</strong>, 04011 (2022).</p> <p>The activity was carried out within the framework of the European Project AD ASTRA. This project has received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking under <strong>Grant Agreement No 825027</strong>. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and Hydrogen Europe.</p>
Supplementary material for manuscript: Microcomputed X-ray Tomographic Imaging and Image Processing for Microstructural Characterization of Explosives
<p>This data contains the supplemental information that will be accessible to the public from the paper “Microcomputed X-ray Tomographic Imaging and Image Processing for Microstructural Characterization of Explosives”. The data set contains the reconstructed slices for the 3D images of three different high explosives including: HMX-HTPB, PBX 9501 and PBX 9502. The data sets are folders of reconstructed tiffs showing the microstructure (crystals, binder, voids) and a segmented data set of each. Finally, a .gif movie is also present that plays the slices sequentially. The folders contain the voxel size information. See the manuscript for more details.</p>
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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)
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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.