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113 results for “textile”
Textiles: Balsaminov, "What you go for..."
The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Mikhailo Balsaminov in the play "What you go for, you will find" (1861) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. This work was supported by the Russian Historical Society and the History of the Fatherland Foundation under project titled "NashOstrovsky". DOI: 10.13140/RG.2.2.30337.12647 Source: Objaverse 1.0 / Sketchfab
Supplementary Material for "Exposure Assessment & Risks associated to wearing silver nanoparticle-coated textiles"
<p>Calculation of release rate constants and parameters for the calculations of dermal exposure. Source code for Matlab.</p>
Examining Cut-and-Sew Textile Waste within the Apparel Supply Chain
Open the record for dataset details and reuse information.
Data on the mineralised textile fragment A2A3_1 from Creney-près-Troyes – Le Paradis locality
<p>This dataset contains data regarding the mineralised textile fragment A2A3_1 from Creney-près-Troyes – Le Paradis locality:</p> <p>A2A3 1 1 back.001.tif: digital microscope image of the B side of the sample<br> A2A3 1 1.002.tif: digital microscope image of sample side A<br> Output_P5.tif: 5x magnification optical microscope image of the fibres on side A of the sample<br> A2A311_320mm_50ms_68kev_new_aligned_3420x3420x908.raw: concatenated 8 bit volume of sample A2A3_1<br> A3A311_back_layer1_3136x2640x114.raw: 8 bit volume of the B1 layer of the sample<br> A3A311_back_layer1__orientationXY_3136x2640x114.raw: volume of orientations of layer B1 of the sample<br> A3A311_back_layer2_3136x2640x121.raw: 8 bit volume of the sample layer B2<br> A3A311_back_layer2__orientationXY_3136x2640x121.raw: volume of orientations of the sample layer B2<br> A3A311_back_layer3_3136x2640x175.raw: 8 bit volume of the B3 layer of the sample<br> A3A311_back_layer3__orientationXY_3136x2640x175.raw: orientation volume of the B3 layer of the sample<br> A3A311_front_3016x2536x220.raw: 8 bit volume of the A layer of the sample<br> A3A311_front__orientationXY_3016x2536x220.raw: volume of the orientations of the A layer of the sample<br> mask_A3A311_back_layer1_3136x2640x114.raw: voxel mask of the B1 layer of the sample<br> mask_A3A311_back_layer2_3136x2640x121.raw: voxel mask of the B2 layer of the sample<br> mask_A3A311_back_layer3_3136x2640x175.raw: voxel mask of the B3 layer of the sample<br> mask_A3A311_front_3016x2536x220.raw: voxel mask of the A layer of the sample</p>
Textiles: Agnia, "It's Not All Shrovetide..."
The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Agnia Kruglova in the play "It's Not All Shrovetide for the Cat" (1871) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. Please contact us if you require seamless textures. The cloth 3D model by atomov is available at Turbosquid. DOI: 10.13140/RG.2.2.23626.24001 Source: Objaverse 1.0 / Sketchfab
Textiles: Lynyaev, "Wolves and Sheep"
The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Mikhail Lynyaev in the play "Wolves and Sheep" (1875) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. Please contact us if you require seamless textures. The cloth 3D model by atomov is available at Turbosquid. DOI: 10.13140/RG.2.2.33377.99681 Source: Objaverse 1.0 / Sketchfab
Textiles: Kukushkina, "A Profitable Position"
The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Felisata Kukushkina in the play "A Profitable Position" (1856) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. This work was supported by the Russian Historical Society and the History of the Fatherland Foundation under project titled "NashOstrovsky". DOI: 10.13140/RG.2.2.35055.71848 Please contact us if you require seamless textures Source: Objaverse 1.0 / Sketchfab
Textiles: Polenka (1), "A Profitable Position"
The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Polina Kukushkina in the play "A Profitable Position" (1856) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. Please contact us if you require seamless textures. The cloth 3D model by atomov is available at Turbosquid. DOI: 10.13140/RG.2.2.21633.94567 Source: Objaverse 1.0 / Sketchfab
Shortgrass Steppe site, station Treatment 4 (water and nitrogen addition) for ESA study, study of plant density of Allium textile in units of numberPerMeterSquared on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Shortgrass Steppe (SGS) contains plant density of Allium textile measurements in numberPerMeterSquared units and were aggregated to a yearly timescale.
Molecular dynamics simulation of Conus textile conotoxin Txd13 in complex with a3b2, a3b4, a6b2 or a6b4 nAChR subtypes.
<p>This folder contains the coordinate and parameter files used to run molecular dynamics simulations of the toxin Txd13 (sequence GCCSNPPCIANPMC) in complex with four nicotinic acetylcholine receptor (nAChR) subtypes: a3b2, a3b4, a6b2 and a6b4 nAChRs. For each system several files are provided:</p> <p>1) an homology model that was used as a starting conformation is provided (eg 'a3b2_txd13.B99990023.pdb'),<br> 2) an Amber Parm7 topology file (eg 'a3b2_txd13_0023.prmtop'),<br> 3) a trajectory files containig 1250 frames extracted from a 100 ns molecular dynamics simulation (eg 'a3b2_txd13_0023_md_smaller.nc') created using pmemd from the Amber 18 package,<br> 4) the log file of this simulations (eg 'a3b2_txd13_0023_md.log'), and<br> 5) the coordinate file representing the minimized verion of the centroid frame of each simulation (with water and ions removed for conveniance) (eg 'a3b2_txd13_0023_md_centroid_min_nowat.pdb')</p> <p>The parameters used for the molecular dynamics simulations are provided in the 'md.in' file.</p> <p>All the text files have been compressed in the 'xz' format</p>
Day 14 - Old textile sign
Old textile sign and its magical creature. Captured with iPhone 12 Pro - Lidar + Polycam Trying to use Sketchfab as a volumetric logbook. Source: Objaverse 1.0 / Sketchfab
Dataset: Modular Piezoresistive Smart Textile for State Estimation of Cloths
<p>This set contains data obtained using the smart textile featured in "Modular Piezoresistive Smart Textile for State Estimation of Cloths", R. Proesmans et al., MDPI Sensors.<br> </p>
Enrollment and Attrition Rate of Fashion and Textiles Students Dataset
Open the record for dataset details and reuse information.
OMMT-Fibre: textile fibres acquired using OptoMechanical Modulation Tomography (OMMT)
<p><strong>Description</strong></p><p>This dataset contains compressed sensing images of fluorescent textile fibres acquired using OptoMechanical Modulation Tomography (OMMT).</p><p>The dataset was collected to illustrate a reconstruction algorithm for 3D fluorescence microscopy data that uses a 1+2D TV regularization to enforce volumetric coherence constraints while being efficient to compute.</p><p> </p><p><strong>Reference</strong></p><p>If you use this dataset, please cite the following publication:</p><p>François Marelli and Michael Liebling, "Efficient compressed sensing reconstruction for 3D fluorescence microscopy using OptoMechanical Modulation Tomography (OMMT) with a 1+2D regularization," Opt. Express 31, 31718-31733 (2023) <br><a href="https://doi.org/10.1364/OE.493611">https://doi.org/10.1364/OE.493611</a></p>
image classification dataset on tailored textiles quality control
<p>This dataset was geared towards representing practical quality control scenarios, specifically involving the quality inspection of glass fiber fabric. Continuous rolls of glass fiber fabric were cut into samples of 300x200 mm. Half of these samples were reinforced with a single carbon fiber. These samples were then classified into six different categories based on the presence of common defects or if they were error-free textiles. Each category consists of 300 images, with a resolution of 4288x2848 pixels.</p>
Out-of-plane performance of structurally and energy retrofitted masonry walls: Geopolymer versus cement-based textile-reinforced mortar combined with thermal insulation
<p>Data corresponding to all figures and tables presented in the publication</p>
Canasta hecha de textiles incas
Source: Objaverse 1.0 / Sketchfab
Exploratory Study on Bio-signal Telemonitoring Using Electronic Textiles in a Pediatric Acute and Critical Care Setting
ClinicalTrials.gov study NCT05961176. IPD Sharing: NO. Countries: 1. Publications: 8.
Multifaceted Intervention for Protection Against Cotton Dust Exposure Among Textile Workers
ClinicalTrials.gov study NCT03738202. IPD Sharing: NO. Countries: 1. Publications: 5.
Evaluate the Clinical Efficacy of Precious Metal Fiber Textile (Germanium Titanium π Element) for Erectile Dysfunction
ClinicalTrials.gov study NCT03359265. IPD Sharing: Not stated. Countries: 1. Publications: 4.
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.