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422 results for “Texturing”
Effect of Meal Texture on Glucose-metabolism and Gut Hormone Response After Bariatric Surgery
ClinicalTrials.gov study NCT04082923. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Textured Food Introduction Information and Parental Feeding Practices
ClinicalTrials.gov study NCT04570059. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: BMI and WHR are reflected in female facial shape and texture: a geometric morphometric image analysis
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Habitat heterogeneity captured by 30-m resolution satellite image texture predicts bird richness across the U.S.
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Nitrogen-fixing plants increase soil nitrogen and neighboring plant biomass, but decrease community diversity: A meta-analysis reveals the mediating role of soil texture
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Data from: Broad-scale patterns of soil carbon (C) pools and fluxes across semiarid ecosystems are linked to climate and soil texture
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Data from: Sensing the structural characteristics of surfaces: Texture encoding by a bottom-dwelling fish
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Data from: Making soil particle size analysis by laser diffraction compatible with standard soil texture determination methods
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Instructional Video: Metashape - Build Dense Cloud, Mesh, and Texture
<p><a href="https://www.youtube.com/watch?v=Obj45jVQr1U&feature=youtu.be">https://www.youtube.com/watch?v=Obj45jVQr1U&feature=youtu.be</a></p>
Experimental Data for Emergent Helical Texture of Electric Dipoles
<p>BiCu0.1Mn6.9O12_NPD.zip contains TOF neutron powder diffraction patterns collected in the temperature range of 100K-600K</p> <p>BiCu0.1Mn6.9O12_XRPD_T300K.txt is room temperature X-ray synchrotron powder diffraction pattern</p> <p>BiCu0.05Mn6.95O12-105.51mg-exp-Dec-06-2016-DSC.prn contains differential scanning calorimetry data</p> <p>BiCu0.1Mn6.9O12-109.89mg-exp-Nov-08-2016-DSC.prn contains differential scanning calorimetry data</p> <p> BiCu0.1Mn6.9O12_PE_loop_data_T77K.dat is P-E loop data</p>
Imaging non-collinear antiferromagnetic textures via single spin relaxometry
<p>Data related to the publication, arXiv 2006.13130.</p>
Data from: Preference evaluation of ground beef by untrained subjects with three levels of finely textured beef
After receiving bad publicity in 2012 and being removed from many ground beef products, finely textured beef (referred to as 'pink slime' by some) is making a comeback. Some of its proponents argue that consumers prefer ground beef containing finely textured beef, but no objective scientific party has tested this claim—that is the purpose of the present study. Over 200 untrained subjects participated in a sensory analysis in which they tasted one ground beef sample with no finely textured beef, another with 15% finely textured beef (by weight), and another with more than 15%. Beef with 15% finely textured beef has an improved juiciness (p < 0.01) and tenderness (p < 0.01) quality. However, subjects rate the flavor-liking and overall likeability the same regardless of the finely textured beef content. Moreover, when the three beef types are consumed as part of a slider (small hamburger), subjects are indifferent to the level of finely textured beef.
Data from: Satellite image texture for the assessment of tropical anuran communities
The relationship between environmental heterogeneity and biodiversity represents a cornerstone of ecological research. While environmental descriptors over large extents usually have medium to low spatial resolution, in-situ measures provide accurate information for limited areas, and a gap remains in providing remote descriptors that represent local environmental structure. Texture from satellite images can represent fine-scale heterogeneity over wide spatial coverage, but to date, it has mostly been used to predict general aspects of species diversity, such as richness. Here, we assess the utility of image textures from high resolution satellite images (RapidEye 3A) and in-situ variables to predict differences in the composition of anuran communities in a tropical savanna (Cerrado) of Brazil. While in-situ measures accounted for compositional differences of the whole community, two measures of image textures were associated only with the variation of species within the Hylidae family (adj. R² = 0.16 and 0.14). Comparatively, image textures predicted ~2/3 of the variation explained by in-situ measures (adj. R² = 0.23). When both approaches were combined, a greater compositional variation was achieved (adj. R² = 0.28), with 1/5 of it shared by both in-situ and textures, and 1/5 attributed solely to texture. Our findings suggest that image texture can complement the assessment of environmental heterogeneity acting on the assembly of local anuran communities. This approach can be valuable for explicitly including spatial heterogeneity in biological assessments over broad spatial extents, especially for biological groups strongly filtered by environmental conditions.
Stimuli used in the experiments reported by Wallis et al., "A parametric texture model based on deep convolutional features closely matches texture appearance for humans"
<p># Stimuli for "A parametric texture model based on deep convolutional features closely matches texture appearance for humans" by Wallis et al.</p> <p>This repository contains the stimuli used in</p> <p>Wallis, Funke, Ecker, Gatys, Wichmann and Bethge (submitted). A parametric texture model based on deep convolutional features closely matches texture appearance for humans. </p> <p>Code can be found in a complementary archive (http://doi.org/10.5281/zenodo.438029; stored separately due to license restrictions on images).</p> <p>The first experiment reported in the paper uses the stimuli in the subdirectory `stimulus_set_3`; the second experiment uses `stimulus_set_4`.</p> <p>## License</p> <p><strong>The original texture images (stored in `stimulus_set_3/raw_textures`) and their derivatives (`stimulus_set_3/preprocessed_ims/` and `stimulus_set_4/preprocessed_ims/`) remain copyright of www.textures.com (shared here with permission for scientific, non-commercial purposes).</strong></p> <p>Other images are shared under a CC-BY-NC license.<br> </p>
Complex Isomerism Influencing the Texture Properties of Organometallic [Cu(salen)] Porous Polymers: Paramagnetic Solid-State NMR Characterization and Heterogeneous Catalysis
<p>Raw data from NMR experiments and numerical simulations.</p>
Texturing of a Solar Cell
<p>Texturing of a Solar Cell</p>
Influence of Prior Visual Information on Exploratory Movement Direction in Texture Perception
<p>Raw data of all individual participants</p>
Tailored deformation behavior of 304L stainless steel through control of the crystallographic texture with Laser-Powder Bed Fusion
<p>Laser-powder bed fusion (L-PBF) has gained significant research interest, not only for its profound advantage of producing near-net shape complex geometries of metallic parts, but also for the possibility of producing tailored microstructures. Recent observations have shown that by adjusting the process parameters it is possible to manipulate the crystallographic texture, through the control of the geometrical features of the melt pool. It is also known that the deformation behavior, namely the transformation induced plasticity or twinning induced plasticity effects, of austenitic stainless steels are dependent on the crystallographic texture. Based on the aforementioned observations, the deformation behavior of austenitic stainless steels processed by L-PBF can be tailored. By adjusting the laser power and the laser scanning speed, tailored crystallographic textures were obtained, along the uniaxial loading direction in 304L stainless steel samples produced by L-PBF. The possibility to engineer the crystallographic textures and thus the deformation behavior, in metastable stainless steels, is demonstrated by performing in situ neutron diffraction and uniaxial tension and compression tests. The influence of the initial and the evolving crystallographic texture on the deformation behavior is demonstrated and elaborated accordingly. The observed asymmetry in the deformation behavior between tension and compression is also discussed in detail.</p>
Strength and seismic anisotropy of textured FeSi at planetary core conditions
<p>Data of the radial diffraction experiment at 300 K and 1100 K</p>
Automated Classification of Estuarine Sub-depositional Environment Using Sediment Texture
<p>The repository corresponding to the paper "Automated classification of estuarine sub-depositional environment using sediment texture" by Houghton et al., 2023. <a href="https://doi.org/10.1029/2022JF006891">https://doi.org/10.1029/2022JF006891</a></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)
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.
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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.
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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.