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1,162 results for “fibers”
theoretical and experimental study of the deposition of dielectric stacks on twin-hole silica fibers for implementation of compact all-fiber resonators
<p>This dataset includes the Matlab code to engineer theoretically a Bragg stack made of different dielectric layers. By changing the number of alternating stacks, their thickness and the refractive index of each layer it is possible to obtain the transmission curve of the Bragg stack versus the wavelength of the light in a certain range of values. The dataset includes also the experimental measurements of the resonances related to one of the fabricated compact resonators (based on the twin-hole fiber not poled) and obtained sweeping the wavelength of the input light injected through one of the two Bragg stacks and collecting the power at the exit of the other Bragg stack. </p>
Small-angle X-ray scattering datasets for imaging crossing fibers in mouse, pig, monkey, and human brain
<p>Small-angle X-ray scattering datasets for resolving crossing fibers (myelinated neuronal axon bundles), as described in</p> <p>"<strong><em>Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering</em></strong>"</p> <p>deposited in bioRxiv:</p> <p>https://doi.org/10.1101/2022.09.30.510198</p>
Atomic clock dataset for 'Coherent Optical-Fiber Link Across Italy and France'
<p>Dataset of the comparison of the atomic clocks at LNE-SYRTE and INRIM via optical fibre link between October 2021 and February 2022. Results discussed in Clivati et al., Coherent Optical-Fiber Link Across Italy and France, <em>Phys. Rev. Applied, American Physical Society, </em><em> 18</em>, 054009, <strong>202<em>2</em></strong>.</p> <p>The involved atomic clocks are the Cs fountains SYRTE-F02Cs, IT-CsF2, the Rb fountain SYRTE-F02Rb and the Yb optical lattice clock IT-Yb1.</p> <p>Data is organized in folders, one for each comparison. In the folders data is separated is one file per day. Data is reported as fractional frequency ratios in bins of 864 s. Timetags are reported in modified Julian date (MJD). A validity flag is given where 0 = invalid, valid otherwise. Each folder includes a yaml file with metadata required for generalized data processing as in [Lodewyck et al., 2020]. The Python package used for data processing can be found on <a href="https://github.com/INRIM/tintervals">github.</a></p> <p> </p>
Structural conversion of the spidroin C-terminal domain during assembly of spider silk fibers
<p>GENERAL INFORMATION<br>- Dataset title: Structural conversion of the spidroin C-terminal domain during assembly of spider silk fibers<br>- Description: The dataset contains raw data associated with the publication with the same name, accepted for publication in Nature Communications.<br>- Authors: Danilo Hirabae De Oliveira, Vasantha Gowda, Tobias Sparrman, Linnea Gustafsson, Rodrigo Sanches Pires, Christian Riekel, Andreas Barth, Christofer Lendel, My Hedhammar </p> <p>ORGANIZATION<br>The folder contains zip-files for each figure in the publication. Each zip-file contains data and a .txt file describing the content, the methods for data acquisition and analysis, and the file types.</p> <p><br>DATA COLLECTION<br>Data collection and analysis is described in the paper and in the .txt files included in each zip-file.</p>
Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction
<p>We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.</p>
Data: Homochiral metal-organic frameworks coated double-plasmon active optical fiber for in-situ enantioselective detection
<p>This dataset is focused on utilization of optical fiber with double-plasmon activity (ensured by a spatially separated gold and silver nanocoating of the fiber core) and subsequent surface grafting by HMOFs for enantioselective capture of organic enantiomers.</p>
Simulated dMRI images and ground truth of random fiber phantoms in various configurations
<p>This archive contains simulated dMRI images of random fiber phantoms in various configurations created with Fiberfox and other tools available in MITK Diffusion (<a href="http://mitk.org/wiki/DiffusionImaging">http://mitk.org/wiki/DiffusionImaging</a>). RandomFibers_Example.png illustrates one of the random fiber configurations used for these phantoms.</p> <p>If you are using any of these datasets or the tools used to generate them, please don't forget to cite the dataset itself as well as other relevant publications.</p> <p>Each subfolder contains the following elements:<br> The simulated dMRI image with b-values and gradient directions: dwi.nii.gz, dwi.bvals, dwi.bvecs<br> The fibers used for simulation: AllBundles.fib (binary vtk format)<br> parameters.ffp: Fiberfox simulation parameters<br> parameters.ffp.bvals: b-value file for Fiberfox simulation<br> parameters.ffp.bvecs: gradient vector file for Fiberfox simulation<br> parameters.ffp_VOLUME1.nii.gz: fiber compartment volume fraction map for Fiberfox simulation<br> The logfile detailing all steps of the generation process of the respective phantom: LOGFILE.json</p> <p>bundles: folder containing the individual fiber bundles (binary vtk format .fib)<br> centroids: folder containing the centerlines of each bundle<br> masks: folder containing the binary envelope of each bundle<br> peaks: folder containing the principal fiber direction image (peaks) of each bundle</p> <p>Each subfolder contains the fibers and dMRI simulations with the following fiber specifications:<br> Phantom 1:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 2:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 3:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 4:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 5:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 6:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 7:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 8:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 9:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 10:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 11:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 12:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 13:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 14:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 15:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 16:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p>
Project STORM: monitoring the masonry of Michelangelo's Cloister, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2018 - 2019
<p>This dataset for monitoring the masonry of Michelangelo's Cloister at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0 and S3);</li> <li>Temperature of masonry (sensors S1, S2 and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2018 to May 2019 and separated by month in 8 sheets. In total more than 2 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Hall I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0, S2 and S3);</li> <li>Temperature of masonry (sensors S1, and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2017 to May 2019 and separated by month in 14 sheets. In total more than 4 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Azcorra2023 - Raw fiber photometry recordings
<p>Raw data from fiber photometry recordings of different subtypes (Vglut2+, Calb1+, Anxa1+ and Aldh1a1+ as well as DAT+) SNc dopamine neurons labelled with GCaMP6f, as used in Azcorra et al. Nat Neuro 2023. Metadata for these recordings (recording location, mouse sex...) can be found in the pre-processed dataset (see below).</p> <p>The code used to pre-processed this data to get DF/F is available on GitHub (<a href="https://github.com/DombeckLab/Azcorra2023/releases/tag/Azcorra2023">https://github.com/DombeckLab/Azcorra2023/releases/tag/Azcorra2023</a>) and Zenodo (DOI: 10.5281/zenodo.7872052, <a href="https://zenodo.org/record/7872052">https://zenodo.org/record/7872052</a>). We have also made the pre-processed data available on Zenodo (DOI: 10.5281/zenodo.7871982, <a href="https://zenodo.org/record/7871982">https://zenodo.org/record/7871982</a>). The code necessary to analyze this data and generate the figures shown in the manuscript is is found in that same GitHub repository as the pre-processing code above.</p>
Biobased Structurally Compatible Polymer Blends Based on Lignin and Thermoplastic Elastomer Polyurethane as Carbon Fiber Precursors
<p>The production of carbon fibers based on lignin reduces the cost and the environmental impact associated with carbon fiber manufacturing. However, the melt processing of lignin as a carbon fiber precursor is challenging due to its brittleness and limited thermoplastic behavior. For this reason we produce biopolymer blends based on Alcell organosolv hardwood lignin, hydroxypropyl modified Kraft hardwood, and a thermoplastic elastomer polyurethane (TPU). Samples with TPU content greater than 30% showed excellent melt processability and carbonization yield (35% carbon yield for the samples containing 30% of TPU). The thermal properties were analyzed by differential scanning calorimetry, rheology and thermogravimetric analysis. Fourier infrared measurements were utilized to explain the lignin/TPU interactions which governed the thermal and rheological behavior of the blends. SEM analysis showed that the blends produce a homogeneous structure which was void free after carbonization. These structurally complementary biopolymeric blends should open up new avenues for lignin valorization and bring closer the realization of the production of carbon fibers from biosources.</p>
Dataset used in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters
<p>3000fullhisy: raw data to reproduce Fig. 2<br> ppsdspec.npz: all spectrogram as shown in Fig. 3a<br> AllVelMods: All velocity model shown in Fig. 3b<br> ac.out.final.npz: auto-correlation image in Fig. 3c<br> DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d<br> </p> <p> </p>
Heat Dissipation Test with single Fiber Optic cable
<p> A Heat Dissipation Test implies heating a conducting element within the saturated soil until its temperature increase reaches steady state while monitoring the temperature development of the heating element during heating and cooling phases. In this case, we used a single Fiber Optic (FO) cable to perform a Heat Dissipation Test, aiming to quantify groundwater flow. The FO cable is installed along the outer casing of a piezometer located in an unconsolidated shallow aquifer.</p> <p>The data presented here are the maximum temperature reached each depth, the filtered temperature increment for the most representative depths, and the resulting values of thermal conductivity and groundwater flow based on the interpretation of the recorded data.</p> <p>Additionally, we included all the raw data obtained from the heated cable installed in the N325 borehole which was calibrated externally. And finally, we added two more files were we included the smooth heating curves and log-derivative resulting from filtering all data obtained from the heat dissipation test.</p>
Supplementary Material: Microfluidic Fabrication Solutions for Tailor-Designed Fiber Suspensions
<p>Supplementary material for Berthet, H.; du Roure, O.; Lindner, A. Microfluidic Fabrication Solutions for Tailor-Designed Fiber Suspensions. <em>Appl. Sci.</em> <strong>2016</strong>, <em>6</em>, 385.</p> <p><strong>Video S1:</strong> Microfluidic fabrication technique of fibers by in situ photopolymerization</p> <p><strong>Video S2: </strong>In situ microfluidic measurement of the fiber’s Young’s modulus</p> <p><strong>Video S3: </strong>Microfluidic fabrication technique of fibers by super-paramagnetic particles self-assembly</p> <p><strong>Video S4: </strong>Fiber oscillating between the two lateral walls of a microfluidic channel</p> <p><strong>Video S5: </strong>Flow through a constriction of a suspension of parallel fibers fabricated by photo-polymerization</p> <p><strong>Video S6: </strong>Flow through a constriction of a suspension of rigid perpendicular fibers fabricated by photo-polymerization</p> <p><strong>Video S7: </strong>Flow through a constriction of a suspension of flexible perpendicular fibers fabricated by photo-polymerization</p> <p><strong>Video S8: </strong>Concentrated suspension of fibers flowing through a microfluidic constriction</p> <p><strong>Video S9: </strong>Fibers made by colloids self-assembly flowing through a constriction and forming non-permanent clusters.</p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Research data supporting "Enzyme Prodrug Therapy Engineered into Electrospun Fibers with Embedded Liposomes for Controlled, Localized Synthesis of Therapeutics"
<p>Research data supporting the publication: Chandrawati R. et al., 2017, Enzyme Prodrug Therapy Engineered into Electrospun Fibers with Embedded Liposomes for Controlled, Localized Synthesis of Therapeutics, Advanced Healthcare Materials. DOI: 10.1002/adhm.201700385</p>
Data for: New approach for processing recycled carbon staple fiber yarns to unidirectional reinforced recycled carbon staple fiber tape
<p>This data set was generated at Leibniz-Institut für Verbundwerkstoffe GmbH in the project "Process analysis of the pseudo-plastic deformation behavior of unidirectional reinforced staple fiber organo sheets" is funded by the German Research Foundation (DFG) – funding reference 471480678. The goal was to develop a novel process approach for the further processing of staple fiber yarns made from long recycled carbon fibers (rCF) and polyamide 6 (PA6) to highly aligned staple fiber tapes. For this purpose, the input material as well as the manufactured tapes were characterized and examined in thermal analyses. In addition, the influence of the process parameters in the tape manufacturing process on the width and thickness of the tapes and the mechanical properties were investigated. The flexural and tensile properties were then compared with a laminate wound from the staple fiber yarns. The data set is divided in the results of the TGA, DSC, laser profile scans, tensile tests and flexural tests.</p>
Nonlinear mechanosensation in fiber networks
<p>Dataset corresponding to the underlying numerical and experimental data of the research article "Nonlinear mechanosensation in fiber networks". </p> <p>This repository contains four folders containing the data used to produced the figures shown in the article:<br>- EXPERIMENTS.zip : microrheology measurements in biopolymer networks<br>- MACRO.zip : macroscopic loading of disordered fiber networks (simulations results)<br>- MICRO.zip : local probing of disordered fiber networks (simulations results)<br>- Spatial-NL.zip : local probing of disordered fiber networks, with records of the networks spatial deformations (simulations results)<br>The README.txt document provides a detailed description of the content, including files naming convention and data description.</p> <div> <div> <div> <p>We would like to acknowledge that this project has received funding (E.B. and C.P.B.) from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 891217 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project ID 201269156 - SFB 1032 (Project B12). P.R. is supported by France 2030, the French National Research Agency (ANR-16-CONV-0001) and the Excellence Initiative of Aix-Marseille University - A*MIDEX. M.G. and H.Y. acknowledge support from NIH Grant No. 1R01G140108.</p> </div> </div> </div>
Fig. 3 in First case of Trichinella spiralis infection in beavers (Castor fiber) in Poland and Europe
Fig. 3. The Neighbor-Joining tree of Trichinella species inferred from 5S rDNA inter-gene spacer region sequences. Phylogeny Test - Bootstrap method, No. of Bootstrap Replications - 10000 Jukes Cantor model.
Fig. 2 in First case of Trichinella spiralis infection in beavers (Castor fiber) in Poland and Europe
Fig. 2. Electrophoretic profiles of Trichinella spp. larva amplicons after multiplex PCR amplification. Lanes 1 and 8 marker (Generuler 100 bp DNA), 2–6 (T. britovi), 7 - T. spiralis from beaver (sample 754), lanes 9–12 controls (9 - T. papaue, 10 - T. pseudospiralis, 11 - T. spiralis, 12 - negative control).
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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.