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422 results for “textures”
Figure 9 in Bone surface texture as an ontogenetic indicator in long bones of the Canada goose Branta canadensis (Anseriformes: Anatidae)
Figure 9. Relationships between texture type and parsimony-based and cluster-based percentage maturity indices. The circle diameter is proportional to the number of specimens.
Synchrotron X-ray Diffraction Results - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples
<p>A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from six differently orientated Ti-6Al-4V (Ti-64) samples, using two different analysis techniques of synchrotron X-ray diffraction (SXRD) data. The texture results are produced from two refinement methods for fitting intensities from SXRD pattern images; an established Rietveld refinement method using the software package <a href="http://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a> and a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. The texture results were also compared with electron backscatter diffraction (EBSD) measurements from a single sample orientation. The SXRD and EBSD textures were analysed using <a href="https://mtex-toolbox.github.io">MTEX</a> to enable a direct comparison of the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices. The SXRD texture is calculated from each of the six different sample orientations, a combination of the six sample orientations, and in a batch processing method for calculating spatially-resolved texture variation from 387 individual X-Y stage-scan SXRD measurements across one of the samples. The texture variation measured using stage-scan SXRD is directly compared with EBSD, by splitting up the EBSD map into an equivalent grid matrix using an automated script in MTEX.</p> <p><strong>Material </strong></p> <p>The Ti-64 material used in this study was pre-rolled to 87.5% reduction at 915ºC and then air-cooled to develop a characteristic texture. The run numbers from the experiment reference six different sample orientations, according to their alignment with the original rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction), and alignment with the horizontal (X) and vertical (Y) axes of the synchrotron detector.</p> <p><strong>MAUD / MTEX Analysis</strong></p> <p>The α and β phase texture for each of the six different sample orientations was calculated using MAUD, included in this <a href="https://doi.org/10.5281/zenodo.7311323">analysis dataset</a>, which produced ODFs in the form of text files. The texture files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> package, for plotting of the pole figures and ODF slices, along with calculation of pole figure maxima, ODF maxima and texture indices. The same procedure was used to analyse texture from all six orientations together; using MTEX to fit a single ODF text file. And a series of ODF text files were analysed to calculate texture variation from an X-Y stage scan of Sample 1 (103845). Two different ODF resolutions of 5º and 15º were initially used to fit the texture in MAUD, with the same ODF resolution applied to analyse the data in MTEX. However, an ODF resolution of 15° was found to reproduce the most reasonable texture strength intensity values, with the closest match to the EBSD results.</p> <p><strong>Continuous-Peak-Fit / MTEX Analysis </strong></p> <p>The lattice plane intensities for 21 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, included in this <a href="https://doi.org/10.5281/zenodo.7311323">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima and texture indices. The same procedure was used to analyse texture from all six orientations together, along with combinations of different sample orientations, by fitting combined lattice intensity text files in MTEX. And a series of lattice intensity text files were analysed to calculate texture variation from the X-Y stage scan of Sample 1 (103845). Lattice plane intensity distributions which had been normalised to a Ti-64 powder sample measurement were also analysed, to see if this had any effect on the texture intensities. Nevertheless, the powder-corrected texture was found to exactly match the raw intensity measurements. Three different ODF resolutions of 5º, 10º and 15º were initially used to fit the texture in MTEX. However, a kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values.</p> <p><strong>EBSD / MTEX Analysis </strong></p> <p>The indexed α-phase EBSD measurements were recorded over an area of around 100 mm<sup>2</sup>, with an equivalent sized map of β-phase orientations reconstructed from the data. Both the α and the β phase maps were analysed using the <a href="https://github.com/LightForm-group/MTEX-texture-block-analysis">MTEX-texture-block-analysis</a> package, which was used to split up the map into 387 individual square sections, with equivalent dimensions to the SXRD stage-scan measurement grid. For each of the 387 sections, MTEX was used to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima and texture indices.</p> <p><strong>Texture Variation Comparison</strong></p> <p>The texture values calculated from the SXRD stage scan measurements, with the two analysis methods, were used for a direct comparison with the texture variation recorded using EBSD. This analysis was recorded in the <a href="https://github.com/LightForm-group/texture-strength-comparison">texture-strength-comparison</a> package. The results show differences in texture variation across the piece depending on the method used to analyse the SXRD data. The Continuous-Peak-Fit analysis method shows the closest match with EBSD, producing clear texture intensity spikes for the different α and β lattice plane pole figure intensities, ODF maxima and texture indices, at the centre of the piece. The results were also used to develop SXRD maps showing the distribution of texture intensities across the sample.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated processing metadata for the SXRD and EBSD analyses, recording information about the different packages used to process the data, along with details about the different files contained within this results dataset.</p>
Supporting data for 'Chemically Reduced Graphene Oxide based Aerogels (rGOAs) - insight on the surface and textural functionalities dependent on handling the synthesis factors'
<p>Experimental data for the 'Chemically Reduced Graphene Oxide based Aerogels (rGOAs) - insight on the surface and textural functionalities dependent on handling the synthesis factors' manuscript/publication.</p> <p>Package contains following data:<br> 1. File with description of the experimental conditions for rGOAs synthesis, format: .pdf, number of files: 1 <br> 2. Data of Boehm titration for graphene oxide used for synthesis of rGOAs, format: .txt, number of files: 12<br> 3. Fourier-transform infrared spectra of rGOA samples and GO used for synthesis, format: .csv, number of files: 16<br> 4. Raw chromatograms of test probes for rGOA samples, format .txt, number of files: 15 folder with 11 files in each</p> <p> </p>
Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval
<p>This dataset provides the steps of the image analysis techniques used to soil texture classes retrieval related to the paper 'Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval' in wich the principles of the spectral mixture analyses are used.</p>
Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"
<p>There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset.</p> <p>The <strong>tissue_classification </strong>file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets.</p> <p>The <strong>binary_lymphocytes </strong>file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset.</p> <p>All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous.</p> <p>The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) <strong>resnet18_binary_lymphocytes.pth</strong> and (2) <strong>resnet18_tissue_classification.pth</strong>. Codes and instructions to use these are found in <a href="https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis">https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis</a>.</p> <p> </p> <p>If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2):<br><strong>(1) </strong><strong>Brummer, O., Pölönen, P., Mustjoki, S. <em>et al.</em> Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. <em>Br J Cancer</em> 129, 683–695 (2023). </strong><a href="https://doi.org/10.1038/s41416-023-02329-4">https://doi.org/10.1038/s41416-023-02329-4</a></p> <p><strong>(2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: </strong><strong><a href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</a></strong><strong>.</strong></p>
Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'
<p>Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article: </p> <p>Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405</p> <p>The .zip file has the following folders:</p> <p>1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents</p> <p>2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction</p> <p>3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil texture prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil texture prediction</p>
iSDAsoil: soil texture class (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_texture.class_c_30m_*..*cm_2001..2017_v0.13_wgs84.tif = soil texture class,</li> </ul> <p>Classes:</p> <pre><code>Code,Name,Value,Color Cl,clay,1,#d5c36b SiCl,silty clay,2,#b96947 SaCl,sandy clay,3,#9d3706 ClLo,clay loam,4,#ae868f SiClLo,silty clay loam,5,#f86714 SaClLo,sandy clay loam,6,#46d143 Lo,loam,7,#368f20 SiLo,silt loam,8,#3e5a14 SaLo,sandy loam,9,#ffd557 Si,silt,10,#fff72e LoSa,loamy sand,11,#ff5a9d Sa,sand,12,#ff005b NODATA,,255,#ffffff </code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Data from: Dietary constraints of phytosaurian reptiles revealed by dental microwear textural analysis
Phytosaurs are a group of large, semi-aquatic archosaurian reptiles from the Middle–Late Triassic. They have often been interpreted as carnivorous or piscivorous due to their large size, morphological similarity to extant crocodilians and preservation in fluvial, lacustrine and coastal deposits. However, these dietary hypotheses are difficult to test, meaning that phytosaur ecologies and their roles in Triassic food webs remain incompletely constrained. Here, we apply dental microwear textural analysis to the three-dimensional sub-micrometre scale tooth surface textures that form during food consumption to provide the first quantitative dietary constraints for five species of phytosaur. We furthermore explore the impacts of tooth position and cranial robusticity on phytosaur microwear textures. We find subtle systematic texture differences between teeth from different positions along phytosaur tooth rows, which we interpret to be the result of different loading pressures experienced during food consumption, rather than functional partitioning of food processing along tooth rows. We find rougher microwear textures in morphologically robust taxa. This may be the result of seizing and processing larger prey items compared to those captured by gracile taxa, rather than dietary differences per se. We reveal relatively low dietary diversity between our study phytosaurs and that individual species show a lack of dietary specialisation. Species are predominantly carnivorous and/or piscivorous, with two taxa exhibiting slight preferences for 'harder' invertebrates. Our results provide strong evidence for higher degrees of ecological convergence between phytosaurs and extant crocodilians than previously appreciated, furthering our understanding of the functioning and evolution of Triassic ecosystems.
Bone - Textured
Practicing In Zbrush, and transferring over Normal Maps to Substance Painter. Started with a lowpoly cylinder in maya. Working on getting it lower poly. 2048 texture maps. Source: Objaverse 1.0 / Sketchfab
Spitfire Texturing Challenge 2017
French Spitfire 1945 Based on "[Aircraft Painting Contest – Base](https://sketchfab.com/models/e1b4d01dbd7c42bb95c5a0f922747ac2)" by [Renafox](https://sketchfab.com/kryik1023), licensed under CC Attribution-ShareAlike. Source: Objaverse 1.0 / Sketchfab
Ullevi Gås Dilln 207 12c Texture
Ullevi, Gåsinge-Dillnäs 207:12 detalj, Södermanland. Hällristning med skålgropar och skepp. Sörmlands museum och Opus Heritas. 3D-SFM. Source: Objaverse 1.0 / Sketchfab
Ullevi Gås Dilln 207 Drone Textur
Ullevi, Gåsinge-Dillnäs 207, Södermanland. Hällristningsområdet. Drönarbild, 3D-SFM. Sörmlands museum och Opus Heritas. Source: Objaverse 1.0 / Sketchfab
Lion Statue - Hi-Res Textures
Taken with the Trnio app version 2. Still haven't incorporated improved mesh geometry algorithms, but the capture process and flow is simplified. Source: Objaverse 1.0 / Sketchfab
Ullevi Gas Dilln 207 15 Textur
Ullevi, Gåsinge-Dillnäs 207:15, Södermanland. Hällristning med skeppsfigurer och skålgropar. Sörmlands museum och Opus Heritas. 3D-SFM. Source: Objaverse 1.0 / Sketchfab
August von Gneisenau - Textured
the texture increased the filesize thus the resolution of the main subject had to be reduced. feel invited to share your preference! Source: Objaverse 1.0 / Sketchfab
Bastardstown 30_3_2022_Textured Model Geo
Coastal erosion and protection works to date 30/03/2022 at Bastardstown, Kilmore Co Wexford Ireland. Source: Objaverse 1.0 / Sketchfab
Ullevi Gas Dilln 207 10 11 Textur
Ullevi, Gåsinge-Dillnäs 207:10-11, Södermanland. Hällristning med skeppsfigur, skålgropar och korsfigur. Sörmlands museum och Opus-Heritas. 3D_SFM Source: Objaverse 1.0 / Sketchfab
Tiger 1 - Worn Tank (2k Textures)
A Tiger 1 tank. 2k Textures included for download. https://www.artstation.com/artwork/b5B1JE Modelled in Blender. Textured in Substance and Blender. I have a set of "clean" textures as well, message me if you're interested. Source: Objaverse 1.0 / Sketchfab
House with Caryatids_Sibiu_no_texture
A medium quality model (here decimated to 1000k, no texture) of the entrance to the House with Caryatids, Sibiu, Romania. The house, built between 1801 and 1802 follows the Late Baroque style and was influenced by the building of the Bruckenthal Palace, in the Main Square. It is now a historical monument. (copyright Universitatea 1 Decembrie 1918 din Alba Iulia, 2017). The model is based on 119 photos, taken with 28 and 180 mm lens from ground level. Source: Objaverse 1.0 / Sketchfab
Sketchfab Texturing Challenge: Spitfire
Sir James M. Robb's personal SL721 Spitfire (1947)  Based on "[Aircraft Painting Contest – Base](https://sketchfab.com/models/e1b4d01dbd7c42bb95c5a0f922747ac2)" by [Renafox](https://sketchfab.com/kryik1023), licensed under CC Attribution-ShareAlike. Source: Objaverse 1.0 / Sketchfab
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.