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1,162 results for “fiber”
Collagen fiber images and centerline annotations based on SHG imaging
<p>The dataset contains images of collagen fibers collected using SHG and manual annotations of the fiber centerlines, from sections of the pancreas, kidney, and breast.</p>
Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber
<p>The files with simulation results for ECOC 20223 submission "Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber".</p><p><strong>"4CCF_eigenvectorsPol"</strong> file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 4-coupled-core fiber (4CCF). These results are uploaded to the python notebook <strong>"4CCF_modelingECOC" </strong>in order to plot them to get Fig. 2 in the paper. <strong>"TransferMatrix"</strong> is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook <strong>"4CCF_modelingECOC"</strong>, where all the calculations for figures in the paper are presented<strong>.</strong></p><p> </p><p><strong>! </strong><i>UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [</i>A. Ankiewicz, A. Snyder, and X.-H. Zheng, "Coupling between parallel optical fiber cores–critical examination", Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986<i>]:</i></p><p>(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))</p><p>The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.</p><p><strong>! </strong><i>UPD 9.12.2023: I have noticed that in the published version of the code I forgot to change the wavelength range for impulse response calculation. So instead of seeing the nice shape as in the paper you will see resolution limited shape. To solve that just change the range of wavelengths, you can add "wl = [1545e-9, 1548e-9]" in the first cell after "Total power impulse response".</i></p><p><strong>P.s. </strong>In case of any questions or suggestions you are welcome to write me an email ekader@chalmers.se</p>
image classification dataset on carbon fiber reinforcement quality control
<p>Image classification dataset on carbon fiber quality control.</p> <p>To represent a practical quality control problem, a dataset was generated using carbon plain weave with a grammage of 200g/m². Pieces of the weave, measuring 300x300 mm², were cut using a CNC cutter table. Two such pieces were stacked, with a binder applied between them for shape stability after the forming process. The formed stacks were then scanned using a high-resolution camera mounted on a robotic arm, resulting in 500 images of the textiles' surfaces in three-dimensional shape. The images were then cropped to 341x384 pixels patches and transformed to grayscale.</p> <p>Each patch was classified into one of three classes: normal textile, gap, or fold. Images that were blurred, out of focus, or had bad contrast were sorted out. The dataset has not yet been released, and will be available upon the acceptance of the document.</p>
Notebooks and calculation files for: Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Models
<p>The files with simulation results for JLT submission "Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Modelsr".</p> <p><strong>"3CCF_supermodes"</strong> file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 3-coupled-core fiber (4CCF). These results are uploaded to the python notebook <strong>"3CCF_modelingJLTPaper" </strong>in order to plot them to get Fig. 3 in the paper. <strong>"TransferMatrix"</strong> is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook <strong>"3CCF_modelingJLTPaper"</strong>, where all the calculations for figures in the paper are presented<strong>.</strong></p> <p> </p> <p> </p> <p><strong>! </strong><em>UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [</em>A. Ankiewicz, A. Snyder, and X.-H. Zheng, “Coupling between parallel optical fiber cores–critical examination”, Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986<em>]:</em></p> <p>(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))</p> <p>The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.</p> <p> </p> <p><strong>P.s. </strong>In case of any questions or suggestions or if you need more explanations, you are welcome to write me an email ekader@chalmers.se. If it seems like the code does not work or mistakes in simulations are found, I also appreciate letting me know.</p>
High-speed TIRF and 2D super-resolution structured illumination microscopy with large field of view based on fiber optic components
<p>Super-resolved structured illumination microscopy (SR-SIM) is among the most flexible, fast, and least perturbing fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-built instruments are easily able to deliver two-fold resolution enhancement at video-rate frame rates, but the cost of the instruments is still relatively high, and the physical size of the instruments based on the implementation of their optics is still rather large. Here, we present our latest results towards realizing a new generation of compact, cost-efficient, and high-speed SR-SIM instruments. Tight integration of the fiber-based structured illumination microscope capable of multi-color 2D- and TIRF-SIM imaging, allows us to demonstrate SR-SIM with a field of view of up to 150 × 150 μm<sup>2</sup> and imaging rates of up to 44 Hz while maintaining highest spatiotemporal resolution of less than 100 nm. We discuss the overall integration of optics, electronics, and software that allowed us to achieve this, and then present the fiberSIM imaging capabilities by visualizing the intracellular structure of rat liver sinusoidal endothelial cells, in particular by resolving the structure of their trans-cellular nanopores called fenestrations.</p>
Data from: Cover cropping history affects cotton boll distribution, lint yields, and fiber quality
<p>This is digital research data corresponding to a published manuscript, Cover cropping history affects cotton boll distribution, lint yields, and fiber quality, in Crop Science, Vol. 63 p. 1209–1220. </p> <p>There has been limited introduction of new cover crop species into cotton (<em>Gossypium</em> <em>hirsutum</em> L.) production within the last 30 years. Mounting evidence shows that traditional cover cropping species may be detrimental to cotton production, either by depleting soil fertility with crop removal, immobilizing minerals from high carbon residue, or excessive quantity of residue remaining at planting. The objective of this study was to determine the effects of growing a novel cover crop species, carinata (<em>Brassica</em> <em>carinata</em> A. Braun), as a winter annual cover crop for cotton rotation in the southeastern Coastal Plain. Over a 2-year period, carinata, winter wheat (<em>Triticum</em> <em>aestivum</em> L.), and fallow covers were maintained over winter months, then rotated into cotton. Each year, seedcotton and lint yields were collected, along with subsamples for ginning and subsequent fiber quality analyses. Additionally, end-of-season plant mapping was conducted on plants from 1-m of row per plot to determine cover crop effects on boll formation, retention, and distribution, as well as canopy architecture.</p>
Datasets for "Scalable, flexible carbon fiber electrode thread arrays for three-dimensional spatial profiling of neurochemical activity in deep brain structures of rodents"
<p>Datasets used in the manuscript titled, "<strong>Scalable, flexible carbon fiber electrode thread arrays for three-dimensional spatial profiling of neurochemical activity in deep brain structures of rodents</strong>" are uploaded here. </p> <p><strong>Brightfield and fluorescent stained images of brain tissue used for Fig. 5(a):</strong></p> <p>Malt3-20190624_Region 009_DAPI.png</p> <p>Malt3-20190624_Region 009_qCy5.png</p> <p>Malt3-20190624_Region 009_qFITC.png</p> <p>Malt3-20190624_Region 009_qTexasRed.png</p> <p>Malt3_BF20190628_Region 001.png</p> <p><strong>Fluorescent image of brain with embedded CFETs:</strong></p> <p>MALT2_Rat_100um_MOR1_x500_TSA_AF488.jpg</p> <p>Rat_100um_MOR1_x500_TSA.czi</p> <p><strong>In vivo dopamine recording data for Fig. 3:</strong></p> <p>ratarrays822_163.mat</p> <p>ratarrays822_57.mat</p>
Data for the publication: "Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor"
<p>This data set contains all raw data for the publication “Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor”:</p> <p>- Raw sensor data</p> <p>- Python scripts</p> <p>- particle photon scripts</p> <p>- CAD Drawings</p> <p>- PCB Designs</p>
Temperature measurements of full-scale wall element using Type K thermocouples to observe internal convection in loose-fill wood fiber insulation
<p>Internal convection of insulation materials is a phenomenon that occurs when a construction element is subjected to a temperature difference on either side of the element, as the temperature difference inside the insulation will facilitate an onset of air movement due to thermal buoyancy. This dataset represents the results of 11 unique experiments conducted at Aalborg University at the Department of the Built Environment, where a full-scale wall element insulated with loose-fill wood fiber insulation is investigated for internal convection. A large guarded hotbox is used to control the boundary conditions of either side of the wall element, to imitate a construction element subjected to external and internal boundary conditions, similar to a wall in a house. This dataset can be used to benchmark other insulation materials investigated at similar boundary conditions.</p> <p>The dataset is structured into steady-state experiments and dynamic experiments, where a total of 7 unique cases are conducted in steady-state conditions, and 4 unique cases are conducted in dynamic conditions. The dataset for the steady-state experiments is structured by the temperature difference that the full-scale wall element is exposed to, from the cold and hot side, while the dynamic experiments are structured by the amplitude of the temperature variation, along with if an artificial sun is used or not.</p> <p>The results for the internal convection of the loose-fill wood fiber insulation show similar results as other studies that have conducted experiments on other insulation materials.</p> <p>For more information, see doi: 10.54337/aau488363266</p>
Supplemental Material to Article "Quantification of process-induced effects on fatigue life of short-glass-fiber-filled adhesive used in wind turbine rotor blades"
<p>This set supplements the figure data to the article "Quantification of process-induced effects on fatigue life of short-glass-fiber-filled adhesive used in wind turbine rotor blades", DOI: xxx</p>
Data for publication "Aerodynamic interactions of drops on parallel fibers"
<p>A collection of the data and scripts used to produce figures and derive conclusions for: "Aerodynamic interactions of drops on parallel fibers."</p> <p>The DOI number for this paper is 10.1038/s41567-023-02159-4. It is available at the following URL: <a href="https://www.nature.com/articles/s41567-023-02159-4">https://www.nature.com/articles/s41567-023-02159-4</a>.</p> <p>Please see Repo_Contents.pdf for details.</p>
Dataset_Multiscale Fast Fourier Transform homogenization of additively manufactured fiber reinforced composites from component-wise description of morphology
<p>Original micro-CT imaging data and output of processing via the OpenFiberSeg software of additively manufactured carbon fiber reinforced composites. </p> <p>This dataset accompanies the publication in Composite Science and Technology available at https://doi.org/10.1016/j.compscitech.2023.110261</p> <p> </p>
Effect of Preoperative Fiber on Postoperative Bowel Function
ClinicalTrials.gov study NCT04882995. IPD Sharing: NO. Countries: 1. Publications: 6.
Fiber and Fish Oil Supplements for the Prevention of Colorectal Cancer
ClinicalTrials.gov study NCT04211766. IPD Sharing: NO. Countries: 1. Publications: 1.
Dietary Fibers and Satiety in Bariatric Patients
ClinicalTrials.gov study NCT03573258. IPD Sharing: NO. Countries: 1. Publications: 2.
The Influence of Silicone Oil on Nerve Fiber Layer Thickness After Pars Plana Vitrectomy
ClinicalTrials.gov study NCT01255306. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Phase IV Study of the Impact of Dietary Fibers on Symptoms and Esophageal Motility in Patients With Non-erosive GERD
ClinicalTrials.gov study NCT01882088. IPD Sharing: YES. Countries: 1. Publications: 6.
Examining the Efficacy of Fecal Microbiota Transplantation (FMT) and Dietary Fiber in Patients With Ulcerative Colitis
ClinicalTrials.gov study NCT03998488. IPD Sharing: NO. Countries: 1. Publications: 2.
IVIg for Small Fiber Neuropathy With Autoantibodies TS-HDS and FGFR3
ClinicalTrials.gov study NCT03401073. IPD Sharing: NO. Countries: 1. Publications: 15.
Prebiotics and Metformin Improve Gut and Hormones in Type 2 Diabetes in Youth (MIGHTY-fiber)
ClinicalTrials.gov study NCT04209075. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.