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750 results for “coherence”
Dataset - Experimental analysis of the dynamic inflow effect due to coherent gusts
<p>Here the processed experimental data of the accepted paper given below is documented and made available:</p> <p>Berger, F., Neuhaus, L., Onnen, D., Hölling, M., Schepers, G., and Kühn, M.: Experimental analysis of the dynamic inflow effect due to coherent gusts, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-2, accepted, 2022.</p>
Data for the Attosecond coherent-control experiment at FEL FERMI
<p>In the excel sheets, data corresponding to the attosecond coherent control experiment has been provided. A word file is included to explain the data in the different excel sheets. </p>
First-order coherence of light emission from inhomogeneously broadened mesoscopic ensembles
<p>Data from all figures corresponding to the article from A. Delteil, V. Blondot, S. Buil, and J.-P. Hermier, "First-order coherence of light emission from inhomogeneously broadened mesoscopic ensembles", <a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.115302">Phys. Rev. B. <strong>106</strong>, 115302 (2022)</a> – <a href="https://arxiv.org/abs/2209.01137">arXiv:2209.01137</a></p> <p> </p> <p>Data are in tab-separated table format, with a header indicating the variable and unit for each column.</p>
Dataset: Enhancing Spin Coherence in Optically Addressable Molecular Qubits through Host-Matrix Control
<p>Dataset: Enhancing Spin Coherence in Optically Addressable Molecular Qubits through Host-Matrix Control</p>
Optical coherence tomography of the macular ganglion cell layer in children with neurofibromatosis type 1 is a useful tool in the assessment for optic pathway gliomas
<p><span>To investigate whether the ganglion cell layer assessed by OCT is a reliable measure to identify and detect relapses of symptomatic OPGs in children with NF1.</span></p>
Data for coherence measurements of polaritons in thermal equilibrium reveal a power law for two-dimensional condensates
<p>All the raw data sets collected for this project are included in this submission. The code for the numerics is also included. 'Readme.text' files are included with the data sets explaining what the data sets are and how to read them. </p>
Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion</em>”</strong> in Optics Express (doi.org/10.1364/OE.521702)<em>. </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage.</p> <p>We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>10050, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>CS50-28, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Mix, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated mixed sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Dark, 10 us.mat</p> </td> <td> <p>Background interference intensity from a custom setup.</p> </td> <td> <p>Na=2048, Nb=5, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Concentrated 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated mixed sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute mixed sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data for the Thorlabs OCT measurements.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis functions.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Custom_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the custom setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_dilute.py</p> </td> <td> <p>The script is for running analysis of raw dilute measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Room-temperature Quantum Nanoplasmonic Coherent Perfect Absorption
<p>Research data for the article <em>Room-temperature Quantum Nanoplasmonic Coherent Perfect Absorption</em>, to be published in <em>Nature Communications</em>.</p>
Data for "Overcoming Laser Phase Noise for Low-cost Coherent Optical Communication"
<p>The files contain the data for the paper "Overcoming laser phase noise for low-cost coherent optical communication".</p>
BPSD: A Coherent Multi-Version Dataset for Analyzing the First Movements of Beethoven's Piano Sonatas
<p>-- Full paper: <strong><a href="https://doi.org/10.5334/tismir.196" target="_blank" rel="noopener noreferrer">https://doi.org/10.5334/tismir.196</a></strong> --</p> <p>This repository contains the Beethoven Piano Sonata Dataset (BPSD), a multi-version dataset focusing on the first movements of Beethoven's 32 piano sonatas. Recognized as pivotal works in classical music, Beethoven's piano sonatas have profoundly shaped Western classical music, holding a significant place in cultural history.<br><br>The BPSD includes sheet music in different machine-readable formats and audio recordings from eleven performances, with four of them being in the public domain and freely accessible for research purposes. A key feature of BPSD is its coherence, ensuring alignment of all versions on a unified musical timeline and enforcing consistent structures through careful editing of both score and audio representations.<br><br>The focus and main motivation for the design choices made in BPSD are on the technical and computational level. In particular, BPSD facilitates the assessment of algorithmic approaches in tasks like harmony analysis, structure analysis, music transcription, beat and downbeat estimation, and score following. The dataset's coherence makes it an ideal platform for systematically training and evaluating deep learning methods, shedding light on their robustness and uncovering data biases across different data splits using cross-version strategies for evaluation. <br><br>To ease applicability for computational approaches, the BPSD is based on various simplifications that may be disputable from a musicological perspective. Rather than providing novel musicological annotations, the main conceptual contribution of BPSD with its measure annotations is to provide a framework for transferring existing annotations from the symbolic to the audio domain. We hope that, as such, BPSD is also useful for the systematic analysis and exploration of Beethoven's piano sonatas, providing insights into their influence on the development of harmony and structure in Western classical music. Beyond research applications, the dataset also holds educational potential, aiding in the preparation and presentation of Beethoven's work to a broader audience through interactive multimedia experiences.</p>
Coherent soliton condensation in the optical event horizon (experimental data)
<p>tar.gz-archive of experimental data for the article</p> <p>"Coherent soliton condensation in the optical event horizon"</p> <p>S. Bose (1,2), O. Melchert (1,3), I. Babushkin (1,3), M. Pal (2), U. Morgner (1,3), G. Steinmeyer (5,6), and A. Demircan (1,3)</p> <ol> <li>Institute of Quantum Optics, Leibnitz Universität Hannover, Welfengarten 1, 30167 Hannover, Germany</li> <li>Fiber Optics and Photonics Division, CSIR-Central Glass and Ceramic Research Institute (CGCRI), Kolkata, India</li> <li>Cluster of Excellence PhoenixD, Welfengarten 1, 30167, Hannover, Germany</li> <li>Max-Born-Institut, Max-Born-Straße 2A, 12489 Berlin, Germany</li> <li>Institut für Physik, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany</li> </ol>
Data associated with the study: Unlocking DAS amplitude information through coherency coupling quantification
<p>Data associated with the study: Unlocking DAS amplitude information through coherency coupling quantification</p> <p>Here we include all DAS data used in the study that is not included in an open access repository elsewhere.</p> <p>Contents of this repository are:<br>Rutford icestream data:<br>1. Rutford_ice_stream_das_data/icequakes_information.csv - A csv file containing icequake information, including origin times and seismic moment.<br>2. Rutford_ice_stream_das_data/tdms/*.tdms - Raw DAS data recordings over the time periods when the icequakes occured. Data is recorded by a Silixa iDas.<br>(All other information on the deployment can be found in Hudson et al. (2021), JGR).</p> <p>Gornergletscher data:<br>3. Gornergletscher_das_data/gornerglethscer_das_qm_stations.csv - A file containing coordinates of all the fibre channels.<br>4. Gornergletscher_das_data/segy/*.sgy - Raw data files for the time periods used in this study. </p> <p> </p>
Coherent structures of a hydrothermal buoyant plume in the near-field obtained through LES at ultra-high resolution.
<p>Three-dimensional coherent structures identified by iso-surfaces of lambda2 = -1 (a) and lambda2 = -10^(-2) (b) using the method by Jeong and Hussain (1995) for a buoyant forced plume (Gamma0 = 1.14, where Gamma is the flux balance parameter defined by B. Morton and Middleton, 1973). The colormap represents the absolute temperature anomaly. The time interval in the video corresponds directly to the simulation time of the plume.</p> <p>This is an output of a Large Eddy Simulation (LES) performed using the <a href="http://basilisk.fr/">Basilsk</a> code, an adaptive mesh refinement (AMR) code featuring a second-order accurate finite-volume solver for the Navier–Stokes equations. These equations are solved in their three-dimensional Boussinesq form for an incompressible fluid. The plume modeled here represents a typical hydrothermal vent, with source conditions set to 300 °C for temperature, 0.7 m/s for velocity, and a vent radius of 2.8 cm.</p> <div> <div> <div>Source :</div> <div>Jeong J, Hussain F. On the identification of a vortex. <em>Journal of Fluid Mechanics</em>. 1995;285:69-94. doi:10.1017/S0022112095000462 <div>MORTON, B. R. et MIDDLETON, Jason. Scale diagrams for forced plumes. <em>Journal of Fluid Mechanics</em>, 1973, vol. 58, no 1, p. 165-176.</div> </div> </div> </div>
Coherent Electric Field Manipulation of Fe3+-spins in PbTiO3. Open data set
<p>Data supporting figures 3 and 4 of the related publication.</p>
Artificial Neural Network Symbol Demapper for Coherent Optical Fiber Systems
<p>M-files and datasets that implement an artificial neural network (ANN) demapper targeted to the compensation of fiber nonlinearities in coherent optical transmission systems. </p> <p>The dataset contains simulation data of a 11-channel WDM fiber link with numerical propagation implemented by the split-step Fourier method over standard single-mode fiber with 100 km per span and inline optical amplification with 5 dB noise figure. The launched optical power is varied in the range of 0 to 5 dBm and the distance is swept up to 30 fiber spans. The transmitted signal is a root-raised cosine single-carrier 16QAM at 64 Gbaud. </p>
Coherent light emission in cathodoluminescence when using GaAs in a scanning (transmission) electron microscope_experimental dataset
<p>This dataset contains the raw unprocessed experimental data for the "Coherent light emission in cathodoluminescence when using GaAs in a scanning (transmission) electron microscope" by Michael Stöger-Pollach et al., Ultramicroscopy 224 (2021) 113260. </p>
Quantum coherent spin-electric control in a molecular nanomagnet at clock transitions. Open data set
<p>Data supporting the related publication.</p>
Raw data for "Measurement of the coherent beam properties at the CoSAXS beamline"
<p>One dataset for each measurment presented in the article.</p>
OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images
<p>This is a dataset of OCTA images used in the development of the manuscript <em>OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images</em></p>
ICEBEAR 3D coherent scatter radar data for 2020, 2021
<p>Daily ICEBEAR 3D data for 2020, 2021, organized in nx12 matrices where n is the number of observations (echoes) in any given day. The columns are</p> <ol> <li>Year (UT)</li> <li>Month (UT)</li> <li>Day (UT)</li> <li>Hour (UT)</li> <li>Minute (UT)</li> <li>Second (UT)</li> <li>Longitude [degrees]</li> <li>Latitude[degrees]</li> <li>Altitude [km]*</li> <li>Doppler velocity [m/s]</li> <li>SNR [dB]</li> <li>Beam number (1 = east, 2 = center, 3 = west)</li> </ol> <p>* The west-beam altitudes are anomalous.</p> <p> </p>
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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.