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1,162 results for “fibers”
Supplementary material from: Wolz D, Seidel-Greiff R, Behnisch T, Kruppke I, Kuznik I, Bertram P, Jäger H, Gude M, Cherif C (2024) Potentials of Polyacrylonitrile Substitution by Lignin for Continuous Manufactured Lignin/Polyacrylonitrile-Blend-Based Carbon Fibers
<p>This material contains all research data concerning the analyses conducted in the work taht was published as "<strong>Potentials of Polyacrylonitrile Substitution by Lignin for Continuous Manufactured Lignin/Polyacrylonitrile-Blend-Based Carbon Fibers</strong>" in the <strong>MDPI</strong> journal <strong>Fibers.</strong></p> <p>Process data cannot be included for reasons of confidentiality.</p>
Raw data for the main figures of the paper: "Demixing fluorescence time traces transmitted by multimode fibers"
<p><strong>RawMovies.zip </strong></p> <p>This zip file includes the raw movies for each main figure of the paper. </p> <p>For figures 02, 03, 04, 04 and 06, we included 2 tiff files (2 stacks of images): <br>- The first one gives the measured footprints of the sources (ground truth). <br>- The second one is the raw movie (temporal sequence of images) acquired on the microscope for the specific experiment. </p> <p>For figure 07, the tiff file corresponds to a movie acquired while moving a single fluorescent bead away from the optical axis of the microscope (as in figure 7c). </p> <p><strong>RawRata.zip</strong></p> <p>This zip file contains raw data for figures 03, 04, 05 and 06. We have included two files for each figure:<br>- the _gt file is a matrix of the GT time traces (dimensions: number of sources x number of time bins)<br>- the other file is a 3D matrix corresponding to all the images acquired during the experiment. The first time frames are the measured footprints of each of the sources (ground truth). They were acquired by illuminating each source sequentially. The remaining frames correspond to the raw movie acquired during the experiment while illuminating the sources with the GT time traces. Dimensions of this 3D matrix are: (number of pixels in the x dimension) x (number of pixels in the y dimension) x (number of sources + number of time bins)</p> <p>This raw data is the input data to the python analysis function located in :<br>https://github.com/comediaLKB/DemixedFiberPhotometry. </p>
Dataset for "Rise of amplified spontaneous emission in high-power thulium-doped fiber lasers and amplifiers due to self-heating"
<p>The dataset represents the simulation and experimental data for publication "Rise of amplified spontaneous emission in high-power thulium-doped fiber lasers and amplifiers due to self-heating" </p>
Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"
<p>Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable" </p> <p><a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data </p> <p><a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data </p> <p><a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data </p> <p>Figure 2: Figure2.npy</p> <p>Figure 3: Figure 3 abc .npy</p> <p>Figure16: <a href="../api/records/13133835/draft/files/spatial_Vc.npy/content" target="_blank" rel="noopener noreferrer">spatial_Vc.npy</a> & <a href="13133835" target="_blank" rel="noopener noreferrer">spatial_h.npy</a> </p> <p>Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a></p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 5. Fiber artist Alexandra Rusu (NUA) working at a Roman vertical loom (video movie)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages</p>
Phase transitions as intermediate steps in the formation of molecularly engineered protein fibers
<p>This upload contains raw and unprocessed data sets including: Tensile test, diffraction, simulation, surface tension measurement, viscosity measurement, amino acid sequence and videos.</p>
Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration
<p>Interactive plots showing the correlation between histological parameters of optic nerves and chiasm, and metrics derived from diffusion MRI in a rat model of unilateral retinal ischemia.</p> <p> </p> <p>There are two .html files, each containing an interactive figure, one for data pertaining to the optic nerve, the other for the chiasm. The left panel shows the correlation matrix. Click on any cell to see the corresponding scatter plot on the right panel. </p>
2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy
<p><strong>General</strong></p> <p>This dataset consists out of multiple Second Harmonic Generation (SHG) microscopy scans of collagen fibers in mice bones. Some mices are diseased with osteogenesis imperfecta (brittle bone).</p> <p>We used this data to investigate the segmentation of uncertain local collagen fiber orientations. The corresponding paper "2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy" is accepted at GCPR 2019.</p> <p><strong>Abstract</strong></p> <p>Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet-3D and transferred 2D ImageNet weights achieves a human comparable accuracy.</p> <p><strong>Links</strong></p> <p>A preprint of the paper is available at <a href="https://arxiv.org/abs/1907.12868">https://arxiv.org/abs/1907.12868</a>.</p> <p>The final publication is available at Springer via <a href="https://doi.org/10.1007/978-3-030-33676-9_26">https://doi.org/10.1007/978-3-030-33676-9_26</a></p> <p>The source code is available at <a href="https://github.com/Emprime/uncertain-fiber-segmentation">https://github.com/Emprime/uncertain-fiber-segmentation</a>.</p> <p><strong>Data description</strong></p> <p>Please read the accompanying paper for more information about the dataset. Please see the source code for more information about the usage of the data.</p> <ul> <li>shg-ce-de: contains the enhanced and denoised scans as image slices, the scans are sorted by mice (wt wildtyp, het ill mice), scan location and individual scan</li> <li>shg-masks: contains the ground truth masks for the three different classes (similar - Green, dissimilar - Red, not of interest - blue)</li> <li>shg-featues: contains the input and gt for the second stage of the proposed two stage segmentation</li> <li>shg-cross-splits: contains the 10 random splits for the 10 fold cross validation</li> <li>logs-prediction: contains the 10 tensorboard logs, weights and predictions for the 10 fold cross validations</li> </ul>
Harmonization of experimental procedures to assess mitochondrial respiration in human permeabilized skeletal muscle fibers
<p>DatLab files of the experiments included in the "Harmonization of experimental procedures to assess mitochondrial respiration in human permeabilized skeletal muscle fibers" manuscript (<a href="https://doi.org/10.1016/j.freeradbiomed.2024.07.039" target="_blank" rel="noopener">https://doi.org/10.1016/j.freeradbiomed.2024.07.039</a>).</p>
Fiber and vessel dataset for segmentation and characterization
<p>This repository hosts a comprehensive collection of datasets used to develop an innovative deep learning model designed to enhance the segmentation and characterization of macerated fibers and vessel forms in microscopy images. Included in the deposit are raw images, alongside meticulously prepared training and validation datasets. We present an automated segmentation approach that utilizes the one-stage YOLOv8 model, which has been specifically adapted to process high-resolution microscopy images up to 32640 x 25920 pixels. Our model excels in cell detection and segmentation, demonstrating exceptional proficiency.</p>
Data from: Technological Evaluation of Fiber Effects in Wheat-Based Dough and Bread
<p>This dataset is linked to the article by Celeste Verbeke, Els Debonne, Stien Versele, Filip Van Bockstaele and Mia Eeckhout, published in Foods (August 2024):<br>"Technological Evaluation of Fiber Effects in Wheat-Based Dough and Bread" (DOI: https://doi.org/10.3390/foods13162582).</p> <ul> <li>Farinogram curve data.csv & Alveogram curve data.csv & Pasting curve data.csv <ul> <li>Observations: <ul> <li>Ref = wheat flour</li> <li>PF1/5/10 = 1/5/10% pea fiber</li> <li>CF1/5/10 = 1/5/10% cocoa fiber</li> <li>AF1/5/10 = 1/5/10% apple fiber</li> <li>1/2/3/avg = Replicate 1/2/3 & average of the three replicates</li> </ul> </li> </ul> </li> <li>Dough and bread characteristics.csv <ul> <li>Observations:<br> <ul> <li>Ref = wheat flour</li> <li>PF1/5/10 = 1/5/10% pea fiber</li> <li>CF1/5/10 = 1/5/10% cocoa fiber</li> <li>AF1/5/10 = 1/5/10% apple fiber</li> </ul> </li> <li>Abbreviations: <ul> <li>WRC = water retention capacity</li> <li>WA = water absorption</li> <li>DDT = dough development time</li> <li>STAB = stability</li> <li>SOFT = softening</li> <li>P = tenacity</li> <li>L = extensibility</li> <li>W = deformation energy</li> <li>PH = proving height</li> <li>IV = initial viscosity</li> <li>T_past = pasting temperature</li> <li>V_peak = peak viscosity</li> <li>T_peak = peak temperature</li> <li>HS = holding strength</li> <li>V_final = final viscosity</li> <li>BD = breakdown</li> <li>SB_peak = setback from peak</li> <li>SB_total = total setback</li> </ul> </li> </ul> </li> </ul>
Dataset: Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics
Open the record for dataset details and reuse information.
Dataset for "Nanoparticle doping as a way to enhance holmium fiber lasers efficiency"
<p>This dataset contains the specific numerical values of fiber properties used in Figures 2-4.</p>
Physical parameters for the Orion fibers
<p>Table with the physical properties determined for the 152 velocity-coherent fibers identified in the EMERGE Early ALMA Survey.</p> <p>The latter is a sample of 7 star-forming regions in Orion homogeneously surveyed with ALMA in N2H+ (1-0) at 4.5".</p> <p>The raw data was reduced with a uniform analysis using a combinaiton of custom spectral fitting routines, the identification algorithm in the PPV plane HIFIVe (Hacar et al. 2018) and the radial profile fitting routine FilChap (Suri et al. 2019).</p> <p>The header of the table includes the following parameters:</p> <ul> <li>ID: fiber identification number within the region assigned by HIFIVe during the identification process;</li> <li>x0, y0: average x-y coordinates of the fiber with respect to the reference pixel in the datacube [unit: ''];</li> <li>dV, sd_dV: median velocity dispersion (as FWHM of the spectral line) and corresponding standard deviation across the structure [unit: km s-1];</li> <li>sNT, sd_sNT: median non-thermal dispersion (in units of the sound speed) and corresponding standard deviation across the structure;</li> <li>Vlsr, sd_Vlsr: median centroid velocity and corresponding standard deviation across the structure [unit: km s-1];</li> <li>Mass: total mass of the strcuture [unit: Msun];</li> <li>points: number of fields associated to the structure;</li> <li>Tk: median kinetic temperature of the structure [unit: K];</li> <li>L: length of the structure [unit: pc];</li> <li>AR: aspect ratio (computed as L / FWHM);</li> <li>gradVx: median centroid velocity gradient along the axis knots [unit: km s-1 pc-1];</li> <li>ML: line mass of the fiber (computed as M / L) [unit: Msun pc-1];</li> <li>lgrad: mean velocity gradient across the whole axis length [unit: km s-1 pc-1];</li> <li>P: number of protostars associated to the structure;</li> <li>source: region in which the fiber was identified;</li> <li>FWHM: fiber width as derived from the fitting of its column density radial profile [unit: pc];</li> <li>N0: fiber peak column density as derived from the fitting of its column density radial profile [unit: cm-2];</li> <li>gradT: temperature gradient determined around the fiber axis [unit: K pc-1]. </li> </ul>
Dataset: Quantitative Evaluation of Enhanced multi-plane clinical fetal diffusion MRI with a crossing-fiber phantom
<p>This dataset provides MRI acquisitions of a customized crossing phantom for fetal brain. It contains:<br> <br> 1) High Resolution acquisitions of 1.5 mm<sup>3</sup> isotropic and 61 directions (with b-vectors/b-values)</p> <p>2) Six low resolution acquisitions of 1x1x4 mm<sup>3</sup> and 9-16-25 directions (with respective b-vectors/b-values)</p> <p>3) A structural T2-w acquisition</p>
Abb. 26-29 in Verbreitung und Bestand des Europäischen Bibers (Castor fiber LINNAEUS, 1758) in der Steiermark (Österreich)
Abb. 26-29: 26 (links oben): Lebensraum des Bibers an der Lafnitz bei Wörth, 17.12.2013; – 27 (rechts oben): Lebensraum des Bibers im Bereich der aufgestauten Mur oberhalb des Kraftwerks Gabersdorf, 22.03.2013; – 28 (links unten): Lebensraum des Bibers an der Raab bei Fehring, 19.03.2013; – 29 (rechts unten): Vom Biber genutzter Abschnitt der Lassnitz bei Leitersdorf, 09.04.2013. Fotos: B. Komposch.
Abb. 13 in Verbreitung und Bestand des Europäischen Bibers (Castor fiber LINNAEUS, 1758) in der Steiermark (Österreich)
Abb. 13: Biberreviere im Einzugsgebiet der Lafnitz im Lafnitztal zwischen Burgau und Unterrohr. Grafik: P. Zimmermann.
Abb. 11 in Verbreitung und Bestand des Europäischen Bibers (Castor fiber LINNAEUS, 1758) in der Steiermark (Österreich)
Abb. 11: Biberreviere im Einzugsgebiet der Mur im Bereich des Grazer Feldes sowie im Lassnitztal. Grafik: P. Zimmermann.
Abb. 10 in Verbreitung und Bestand des Europäischen Bibers (Castor fiber LINNAEUS, 1758) in der Steiermark (Österreich)
Abb. 10: Biberreviere im Einzugsgebiet der Mur im Leibnitzer Feld sowie im Sulm- und Lassnitztal. Grafik: P. Zimmermann.
Abb. 8 in Verbreitung und Bestand des Europäischen Bibers (Castor fiber LINNAEUS, 1758) in der Steiermark (Österreich)
Abb. 8: Durchschnittlicher Abstand zwischen den Revieren in den Einzugsgebieten von Lafnitz, Mur und Raab.
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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.
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.