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ShareScore release 0.9.0
Dataset results
422 results for “Texturing”
Well Simple Texture
with simple gradien Texture : 3 x 2048px Source: Objaverse 1.0 / Sketchfab
Union College Idol with Texture
Source: Objaverse 1.0 / Sketchfab
Old bricks floor texture
Herring bricks. Red. Old. Hand painted Source: Objaverse 1.0 / Sketchfab
Villa Romana Casale Mosaic Floors (no texture)
Villa Romana del Casale in Piazza Armerina, Sicily is home of 3500m of the most important Roman mosaics in the world. 350 CE. A UNESCO World Heritage site. This composite 3D model is the result of combined FARO scans and photogrammetry datasets returned to GDH in C4D format. The model for each room was exported out of C4D, cleaned in Meshlab, retopologized in ZBrush, UV unwrapped in Blender, and textured and normal-map baked in Blender or Substance Designer. The individual models were then uploaded to Sketchfab together and baked maps were applied to each model. The original project was a joint effort between the U. of Catania and CVAST at USF. Dr. Mariarita Sgarlata and Dr. H. Maschner, Investigators. We gratefully acknowledge the participation of the administrators of the Villa Romana del Casale and the Museo Archeologico di Aidone. The data were transferred to GDH for processing and analysis. Funding for this project, both at USF and at GDH, has been provided by the Hitz Foundation, H. Maschner, PI Source: Objaverse 1.0 / Sketchfab
New Texture Model
Source: Objaverse 1.0 / Sketchfab
Stone Tiles Texture 3D scan
Photogrammetry Stone Tiles Texture Lowpoly model Source: Objaverse 1.0 / Sketchfab
NUUR-Texture2953: A Diverse Dataset of High Resolution Homogeneous Textures
<p>Permission to copy and use this dataset for noncommercial use is hereby granted provided this notice is retained in all copies and the dataset distribution and the paper mentioned below are clearly cited.</p> <p>Contacts:<br> Jue Lin: jue.lin@u.northwestern.edu<br> Zhiwei Xu: zhiweixu2050@u.northwestern.edu<br> Gaurav Sharma: gaurav.sharma@rochester.edu<br> Thrasyvoulos N. Pappas: pappas@ece.northwestern.edu</p> <p>"NUUR-Texture2953" is a dataset of diverse texture images created to facilitate research on texture analysis and synthesis, and is build upon our previously established dataset "NUUR-Texture500". The textures in this dataset are spatially homogeneous, ranging from regular to stochastic, typically containing repeated elements with random variations in position, shape, orientation and color. For a detailed description of the dataset construction and contents, readers should refer to the following paper:</p> <p>Jue Lin, Zhiwei Xu, Gaurav Sharma, Thrasyvoulos N. Pappas, "Texture Representation via Analysis and Synthesis with Generative Adversarial Networks", Journal of e-Prime - Advances in Electrical Engineering, Electronics and Energy (2023), https://doi.org/10.1016/j.prime.2023.100286.</p> <p>Jue Lin, Gaurav Sharma, Thrasyvoulos N. Pappas, "NUUR-Texture500: A Diverse Dataset of High Resolution Homogeneous Textures", Zenodo, https://doi.org/10.5281/zenodo.7127079</p> <p>Disclaimer: </p> <p>The dataset is provided "as is" with ABSOLUTELY NO WARRANTY expressed or implied. Use at your own risk.</p> <p>Acknowledgment: </p> <p>The NUUR-Texture2953 texture images are curated from a number of publicly accessible sources. We acknowledge and thank the original sites for their contributions:<br> Flickr www.flickr.com<br> NeedPix www.needpix.com<br> Pexels www.pexels.com<br> PickUpImage www.pickupimage.com<br> Pixabay www.pixabay.com<br> PublicDomainPictures www.publicdomainpictures.net<br> RawPixel www.rawpixel.com<br> Unsplash www.unsplash.com<br> WikimediaCommons commons.wikimedia.org</p>
Data for "Visualizing thickness-dependent magnetic textures in few-layer Cr2Ge2Te6"
<p>Data from all the figures in the main text of "Visualizing thickness-dependent magnetic textures in few-layer Cr2Ge2Te6". All files contain the data displayed in the paper and are in *.fig format (MATLAB). They can be opened using MATLAB.</p>
Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data
<p>Data accompanying our paper on: <em>Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data</em></p> <p> </p> <p><a href="https://github.com/tlpss/diffusing-synthetic-data" target="_blank" rel="noopener">github repository</a></p> <p>meshes.zip contains the 3D meshes used to generate the synthetic data for all three object categories</p> <p>real-datasets.zip contains the real-world image datasets gathered to evaluate the synthetic data</p> <p> </p> <p> </p>
Advancing Carriers Mobility in MnSb2Te4 Thermoelectrics via Tailored Textures and Vacancy Modification
<p>The full data sets for the manuscript "Advancing Carriers Mobility in MnSb2Te4 Thermoelectrics via Tailored Textures and Vacancy Modification" by Xu, et al.</p> <p>DOI: <a href="https://doi.org/10.1002/aenm.202500838">10.1002/aenm.202500838</a></p>
Theoretical simulations for: Emergence of exotic spin texture in supramolecular metal complexes on a 2D superconductor
<p>Here you will find the information necessary to reproduce the theoretical data published on "Emergence of Exotic Spin Texture in Supramolecular Metal Complexes on a 2D Superconductor" paper</p> <p>It contains:</p> <ol> <li>the geometries used in the DFT calculations</li> <li>inputs and outputs of the multiplet calculations implemented on matlab</li> </ol>
P, pH, lime and texture data of 70 Hungarian sites
<p>The database contains data of different phosphorous measurement methods</p>
National topsoil texture map of Scotland
<p>The National scale topsoil texture dataset is based on the digital (vector) version of the soils of Scotland 1:250 000 maps and the land cover of Scotland 1988.</p> <p>The topsoil texture in this data set has been determined using data from the Scottish soils knowledge and information base (SSKIB). The component sand, silt and clay contents held within SSKIB are derived from a subset from over 40 000 analyses held within the Scottish soils database. The median, silt and clay contents were calculated for each soil horizon of each soil taxonomic unit (soil series) delineated within the 1:250 000 national soil map of Scotland, using only data from those horizons that matched the typical horizon sequences for each taxonomic unit. The percentage of sand-sized particles was derived by subtraction the percentage of silt plus clay from 100. Where no data was available, information from analogous (similar) soil series (taxonomic units) were used. The data are presented in British Standards Institute texture classes (BRITISH STANDARDS INSTITUTION. Code of Practice for Site Investigations. BS5930:1981 Second Edition. British Standards Institution, London, 1981) and are applied to the dominant soil type within a map unit. Please cite as: <em>Soil Survey of Scotland Staff. 2014. National scale map of topsoil texture. James Hutton Institute. Aberdeen. 10.5281/zenodo.5786877. </em></p> <p>The preparation of this dataset was funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government.</p>
Data for the publication "Low sensitivity of three terrestrial biosphere models to soil texture over the South-American tropics"
<p>Code and data for the aforementionned publication </p>
Radiomics Textural Features by MR Imaging to assess clinical outcomes following liver resection in Colorectal Liver Metastases
<p>I uploaded the images od the manuscript "Radiomics Textural Features by MR Imaging to assess clinical outcomes following liver resection in Colorectal Liver Metastases"</p>
FIGURE. Blades of C. spongifolia. A. Sub-marginal and marginal collective veins, with laminal tissue (2–4 mm) between. B. Marginal collective veins fused below shallow sinus. C. Underside with spongy appearance produced by "false pores"; and thick, rubbery texture indicated by axial wrinkles formed in crease. D. Underside showing primary vein and pinnate lateral veins surrounded by spongy tissue. E. Sub-stomatal cavities revealed by transmitted light (scale unit 0.25 mm), F. Sub-stomatal cavities revealed by removing lower epidermis (A–D: Mengla County, 2018. E–F: Bach Ma NP seedling, ex situ). Photos: PJM. in Colocasia spongifolia sp. nov. (Araceae) in southern China and central Vietnam
FIGURE. Blades of C. spongifolia. A. Sub-marginal and marginal collective veins, with laminal tissue (2–4 mm) between. B. Marginal collective veins fused below shallow sinus. C. Underside with spongy appearance produced by "false pores"; and thick, rubbery texture indicated by axial wrinkles formed in crease. D. Underside showing primary vein and pinnate lateral veins surrounded by spongy tissue. E. Sub-stomatal cavities revealed by transmitted light (scale unit 0.25 mm), F. Sub-stomatal cavities revealed by removing lower epidermis (A–D: Mengla County, 2018. E–F: Bach Ma NP seedling, ex situ). Photos: PJM.
CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy
<p>This is the models as well as results for CLTS-GAN, a deep learning model that disentangles color and lighting and texture and specular information. The results for the model as well as an augmented polyp dataset are contained here.</p> <p><strong>Abstract:</strong></p> <p>Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. You can find the code and additional detail about CLTS-GAN via our Computation Endoscopy Platform at <a href="https://github.com/nadeemlab/CEP">https://github.com/nadeemlab/CEP</a></p>
Easter Island Statue (Textured)
Source: Objaverse 1.0 / Sketchfab
Realistic Texture Boat
Technical information: Texures: Base, Roughness and Normal. 2048x2048px PNG. Face orientation already checked. Mesh orientation - X Forward (Z up). Tris: 1.386 Softwares: Blender and Adobe Substance Painter. Source: Objaverse 1.0 / Sketchfab
Textured Barrel
Maya 3D Barrel model with texture class excercise Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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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.