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422 results for “Texturing”

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zenodo32/100

HomeWork - Automapping and simple texture

1 / 5 HomeWork - Automapping and simple texture. Auto UV mapping Source: Objaverse 1.0 / Sketchfab

opencc-byApr 2020View details →
zenodo32/100

owl texture test

photoscan w/ 15 images from dslr Source: Objaverse 1.0 / Sketchfab

opencc-byOct 2016View details →
zenodo32/100

Chair Texture

This is my chair texture model. Source: Objaverse 1.0 / Sketchfab

opencc-byOct 2020View details →
zenodo32/100

Textured Box

2k Textures Source: Objaverse 1.0 / Sketchfab

opencc-byNov 2020View details →
zenodo32/100

Nanoscale engineering and dynamical stabilization of mesoscopic spin textures - Experimental Data

<p>Experimental data for "Nanoscale engineering and dynamical stabilization of mesoscopic spin textures".</p> <p>Data is organized by figure in which it is discussed. Filenames are organized by date/timestamp collected. Figures composed of multiple, separately taken datasets have been uploaded into separate folders, organized by subfigure or other identifier.</p> <p>All datasets are matlab (.m) filetype. Each contains three column vectors; the signal and phase of the FFT of readout, and the time at which the readout was performed.</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Pediatric CT Noise Texture Augmentation

<p>Dataset of routine and quarter dose anthropomorphic phantom (<a href="https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4926847">XCAT</a>) CT simulations using the <a href="https://github.com/JeffFessler/mirt">Michigan image Reconstruction Toolbox (MIRT)</a>.</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Dataset of images SfM - FRM - Lighting and Artificial texture

<p>This collection of images was employed to examine the impact of various configurations in the 3D modeling process for short-distance environments. This analysis established settings to achieve submillimeter accuracy in the RMSE values of the analyzed points.&nbsp;</p> <p>Set of images used to evaluate the use of light aids (softboxes) and artificial textures in a short-distance environment. This set was used to evaluate the configurations and the possibility of using the SfM technique in the 3D modeling of structural tests. Test specimens used (Concrete, Metal and Wood)&mdash;artificial texture in white Chalk (on Concrete and Wood) and red marker (on metal).<br>Texture patterns were drawn in a checkerboard fashion (T1) and a more closed checkerboard shape (T2).</p> <p>Images in CR3 - Conversion to TIFF or JPG required for use and processing in Agisoft Metashape.</p>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

<p>We uploaded the&nbsp;15 morphological features of&nbsp;&nbsp;91 samples of 85 patients&nbsp;analyzed in the manuscript:&nbsp;Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria L. Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, and Antonella Petrillo. 2021. &quot;Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions&quot; Applied Sciences 11, no. 4: 1880. https://doi.org/10.3390/app11041880</p>

opencc-by-4.0Feb 2021View details →
zenodo32/100

FIGURE. Growth patterns of tree species and texture of their branches. A. Small tree erect lax canopy in Ch. orbiculata. B. Small tree with little branched stem in Ch. tocantinensis. C. Small tree with very branched stem in Ch. trichortyrsus. D. Stems with bark cerous in Ch. claussenii. E. Stems with bark longitudinally fissured in Ch. celiae. in Taxonomic review of Chamaecrista sect. Absus subsect. Absus ser. Paniculatae (Benth.) H.S. Irwin & Barneby (Leguminosae, Caesalpinioideae)

FIGURE. Growth patterns of tree species and texture of their branches. A. Small tree erect lax canopy in Ch. orbiculata. B. Small tree with little branched stem in Ch. tocantinensis. C. Small tree with very branched stem in Ch. trichortyrsus. D. Stems with bark cerous in Ch. claussenii. E. Stems with bark longitudinally fissured in Ch. celiae.

opennotspecifiedApr 2021View details →
zenodo32/100

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification.

<p>We uploaded the database of 104 lesions included in the manuscript: Fusco R, Piccirillo A, Sansone M, Granata V, Rubulotta MR, Petrosino T, Barretta ML, Vallone P, Di Giacomo R, Esposito E, Di Bonito M, Petrillo A. Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification. Diagnostics (Basel). 2021 Apr 30;11(5):815. doi: 10.3390/diagnostics11050815. PMID: 33946333; PMCID: PMC8146084.</p>

opencc-by-4.0Apr 2021View details →
zenodo32/100

The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments

<p>This article presents a visual&ndash;inertial dataset gathered in indoor and outdoor scenarios with a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware-synchronized data from a commercial stereo camera (Bumblebee&reg;2), a custom stereo rig, and an inertial measurement unit. The most distinctive feature of this dataset is the strong presence of low-textured environments and scenes with dynamic illumination, which are recurrent corner cases of visual odometry and simultaneous localization and mapping (SLAM) methods. The dataset comprises 32 sequences and is provided with ground-truth poses at the beginning and the end of each of the sequences, thus allowing the accumulated drift to be measured in each case. We provide a trial evaluation of five existing state-of-the-art visual and visual&ndash;inertial methods on a subset of the dataset. We also make available open-source tools for evaluation purposes, as well as the intrinsic and extrinsic calibration parameters of all sensors in the rig. The dataset is available for download at&nbsp;<a href="http://mapir.uma.es/work/uma-visual-inertial-dataset">http://mapir.uma.es/work/uma-visual-inertial-dataset</a></p>

opencc-by-4.0Jul 2020View details →
zenodo32/100

Medieval Ornament Axe (Texturing Challenge)

My more traditional entry for the challenge Source: Objaverse 1.0 / Sketchfab

opencc-byMay 2020View details →
zenodo32/100

Scroll With Texture

Scroll with a UV Texture Map Source: Objaverse 1.0 / Sketchfab

opencc-byOct 2020View details →
ClinicalTrials.gov32/100

Evaluation of Treatment Response With CHOI and RECIST Criteria and CT Texture Analysis in Patients With Metastatic Colorectal Cancer Treated With Regorafenib

ClinicalTrials.gov study NCT02699073. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

The Texture, Eating Rate and Glycaemic Index (TERAGI) Study

ClinicalTrials.gov study NCT04683432. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Effectiveness of Active Exploration of Simulated Textures for Sensorimotor Recovery

ClinicalTrials.gov study NCT06962397. IPD Sharing: YES. Countries: 1. Publications: 15.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

Effects of Intraarterial Octreotide on Pancreatic Texture

ClinicalTrials.gov study NCT01400100. IPD Sharing: Not stated. Countries: 1. Publications: 4.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Textured Insoles and Foot Massage in Individuals Diagnosed With Rheumatoid Arthritis

ClinicalTrials.gov study NCT05045898. IPD Sharing: NO. Countries: 1. Publications: 3.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

The Effects of Texture Modified Diets on Dietary Intake and Appetite Responses in Older Adults

ClinicalTrials.gov study NCT04521374. IPD Sharing: NO. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Effects of Microtox on Facial Skin Quality, Fine Wrinkles, and Texture

ClinicalTrials.gov study NCT06604832. IPD Sharing: YES. Countries: 1. Publications: 7.

controlledIPD-YESFeb 2026View details →

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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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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record