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455 results for “microwave”
Data and code for figures: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance, <em>Supercond. Sci. Technol.</em> <strong>37</strong> 075013</p>
All-sky information content analysis for novel passive microwave instruments - data
<p>This dataset is the underlying data for the article:</p> <p>Grützun, V., S. A. Buehler, L. Kluft, M. Brath, J. Mendrok, and P. Eriksson (in press, 2018), All-sky Information Content Analysis for Novel Passive Microwave Instruments in the Range from 23.8 GHz up to 874.4 GHz, Atmos. Meas. Tech., doi:10.5194/amt-2017-377. </p> <p>Please refer to that article for a description of the scientific background of the data and to the attached README file for a technical documentation. </p> <p>Contact: Verena Grützun, verena.gruetzun@uni-hamburg.de<br> </p>
The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA
<p><strong>Related paper containing detailed description</strong>: <strong><a href="https://essd.copernicus.org/articles/12/177/2020/essd-12-177-2020.html">Moesinger et al. (2020)</a></strong></p> <p>Vegetation optical depth (VOD) describes the attenuation of radiation by plants. VOD a function of frequency as well as vegetation water content, and by extension biomass. VOD has many possible applications in studies of the biosphere, such as biomass monitoring, drought monitoring, phenology analyzes or fire risk management.</p> <p>We merged VOD observations from various spaceborne sensors (SSM/I, TMI, AMSR-E, AMSR2, WindSat) to create global long-term vod time series. Prior to aggregation the data has been rescaled to AMSR-E, removing systematic differences between them.</p> <p>There is a product for C-band (~6.9 GHz, 2002 - 2018), X-band (10.7 GHz, 1997 - 2018) and Ku-band (~19 GHz, 1987 - 2017). The data is global sampled on a regular 0.25 degrees grid. Each product is available as daily global netcdf4 files.</p> <p> </p> <p>Currently there is an issue with opening the file using ESA SNAP. As an alternative <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a> can be used to quickly visualize the data. </p> <p>An update of VODCA, addressing this issue and potentially including an extension of the dataset, is foreseen to be published on Zenodo early 2020.</p> <p> </p> <p><strong>Please contact us if you have any questions, problems or suggestions for improvement!</strong></p> <p> </p> <p><strong>Files:</strong></p> <ul> <li>"VODCA_C-band_2002-2018_v01.0.0.zip" (unzipped size: ~140 GB): <ul> <li>VODCA C-band files, sorted into yearly folders</li> </ul> </li> <li>"VODCA_X-band_1997-2018_v01.0.0.zip" (unzipped size: ~180 GB): <ul> <li>VODCA X-band files, sorted into yearly folders</li> </ul> </li> <li>"VODCA_Ku-band_1987-2017_v01.0.0.zip" (unzipped size: ~270 GB) : <ul> <li>VODCA Ku-band files, sorted into yearly folders</li> </ul> </li> <li>"vodca_v01-0_K-band_2007-06-01.nc" <ul> <li>sample file of the Ku-band product</li> </ul> </li> <li>"ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" <ul> <li>Contains a global land mask, VODCA only has data for land locations. Source: https://github.com/TUW-GEO/smecv-grid</li> </ul> </li> </ul> <p><strong>Variables of data in VODCA files:</strong></p> <ul> <li>"VOD": Unitless, Vegetation Optical Depth of the respective band</li> <li>"sensor_flag": Bit-flag indicating which sensors contributed to each observation. <ul> <li>Values: <ul> <li>1 = AMSR-E</li> <li>2 = AMSR2</li> <li>3 = SSM/I F8</li> <li>4 = SSM/I F11</li> <li>5 = SSM/I F13</li> <li>6 = TMI</li> <li>7 = WindSat</li> </ul> </li> </ul> </li> <li>"processing_flag": Bit-flag indicating irregularities during processing affecting the quality of the observations <ul> <li>Values: <ul> <li>0 = Everything is fine</li> <li>10 = AMSR-2 7.3 GHz band is used instead of 6.9 GHz</li> <li>11 = Sensor is scaled to matched TMI instead of AMSR-E</li> <li>12 = Sensor scaled without temporally overlapping observations</li> </ul> </li> </ul> </li> <li>"time"/"lon"/"lat": Dimensions of the data.</li> </ul> <p> </p>
Replication Data for: Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins
<p>Data repository for: <strong>Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins</strong></p> <p><em>Data description.pdf</em> describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>Esrfit_Npeak.m</em>, <em>Esrfit_xN.m</em>, <em>GaussianFunc.m</em>, <em>Gaussian_xN_Func.m</em>, <em>Lorentz_Func.m</em>, <em>Lorentz_xN_Func.m</em>, <em>Rabifit_xN.m</em>, <em>Rabi_xN_Func.m</em>, <em>FourierTransformRabi.m</em> are Matlab code files to transform and fit the data.</p>
Dataset for: In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier
<p>Dataset for manuscript "In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier", <a href="https://arxiv.org/abs/2406.03063">arXiv:2406.03063</a></p> <p>Containing the uncalibrated raw measurement data and the calibrated dataset after applying the 8-term error model.</p>
Electron-spin decoherence in trityl radicals in the absence and presence of microwave irradiation
<p>Experimental data sets on bare-spin and dressed-spin decoherence of some trityl radicals, DFT-predicted hyperfine couplings and atom coordinates, simulation scripts, and simulated data. </p>
Resolution Enhancement of UWB Time-Reversal Microwave Imaging in Dispersive Environments (dataset)
<p>These files are the simulation data used to create the figures illustrated in the journal paper with the same title which has been accepted for publication as a regular paper in IEEE Transactions on Computational Imaging. Each filename indicates the figure number associated with the file. These files are text files. The column structure of each file is described in the README file.</p>
Microwave Single Scattering Properties Database (Horizontally Aligned Aggregates of Dendrites)
<p>The database contains physical and microwave single scattering properties of horizontally aligned frozen hydrometeors as large as 11 cm in diameter. </p> <p>A description of the aggregation model used for particle generation can be found in:<br> Leinonen, J., and Szyrmer, W. (2015), Radar signatures of snowflake riming: A modeling study, <em>Earth and Space Science</em>, 2, 346– 358, doi:<a href="https://doi.org/10.1002/2015EA000102">10.1002/2015EA000102</a>.<br> The code used for particle generation is freely available at: <a href="https://github.com/jleinonen/aggregation">https://github.com/jleinonen/aggregation</a></p> <p>The scattering properties of particles were computed using discrete dipole approximation using ADDA software package (<a href="https://github.com/adda-team/adda">https://github.com/adda-team/adda</a>)</p> <p>Terminal velocity of snowflakes was computed using 4 hydrodynamical models that were implemented as a part of snowScat library (<a href="https://github.com/OPTIMICe-team/snowScatt">https://github.com/OPTIMICe-team/snowScatt</a>)</p> <p>Approximately one half of the snowflake structure files and one quarter of scattering properties (for X, Ku, Ka and W band) were generated for the publication of Leinonen and Szyrmer (2015). The remaining part of the dataset was generated using the ALICE High Performance Computing Facility at the University of Leicester.</p>
Data Analysis files for "Coherent optical control of a superconducting microwave cavity via electro-optical dynamical back-action"
<p>Data analysis files for the manuscript "Coherent optical control of a superconducting microwave cavity via electro-optical dynamical back-action", <a href="https://www.nature.com/articles/s41467-023-39493-3#data-availability">Nature Communications <strong>14</strong>, 3784 (2023)</a>, or <a href="https://arxiv.org/abs/2210.12443">arXiv:2210.12443 (2022)</a></p> <p>This contains the raw data, the data analysis files, and the figure generation files of the manuscript, which includes the following three parts,</p> <p>0. Data preparation</p> <p>The total size of the raw dataset is around 120GB. The raw data is pre-processed via digital down-conversion at 40MHz to obtain the optical/microwave transient response of the electro-optical device in the presence of strong optical pulses at different powers and frequencies.</p> <p>The processed data is adopted for data analysis of the response measurements for convenience.</p> <p>1. Data Analysis</p> <ul> <li>Detailed data analysis of the electro-optical (microwave and optical) responses in presence of the optical pump pulses for different mode and probing configurations.</li> </ul> <p>2. Figures for the manuscripts.</p> <ol> <li>Figures for the optical characterizations</li> <li>Figures for the coherent responses for different configurations</li> <li>Figures for the excess back-action</li> <li>Figures for the theoretical curves in the Supplementary Information </li> </ol>
Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery
<p>Netcdf files containing a set of correctional factors for GOES-16 and GOES-17 cloud liquid-water path (LWP). The correctional factors for both satellites are fractional corrections of microwave imager LWP divided by GOES-16/17 LWP at a given solar zenith, GOES sensor zenith, relative azimuth (solar - sensor zenith), and low-cloud fraction.</p> <p>Uncorrected GOES-16/17 LWP is derived from GOES retrieved cloud-optical thickness and cloud-top effective radius, and it is multiplied by the corresponding correctional factor (i.e. the bins which the uncorrected values are in).</p>
Primary data – 13C Solid-State Buildup and Microwave Sweep Profiles in a DNP Polarizer
<p>These primary datasets consist of:</p> <p>1. 13C polarization buildup data in solid-state</p> <p>2. 13C microwave profiles in solid state</p> <p>Please consult the Archive Guide.</p>
Dataset related to the publication "Electromagnetic Amplification of Microwave Phonons in Nonlinear Resonant Microcavities", DOI: 10.1109/TMTT.2018.2855176
<p>This folder contains the raw data from which the graphs in paper "Electromagnetic Amplification of Microwave Phonons in Nonlinear Resonant Microcavities", DOI: 10.1109/TMTT.2018.2855176, have been obtained.</p>
Data and code for "Probing the current-phase relation of graphene Josephson junctions using microwave measurements"
<p>Data and code for "Probing the current-phase relation of graphene Josephson junctions using microwave measurements"</p>
Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects
<p>The attached folder contains the dataset for the manuscript entitled "Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects".</p>
Data and code for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities
<p>Raw and preprocessed data for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities accepted for publication in the journal Atmospheric Measurement Techniques.</p> <p>The dataset includes total losses (transmitted - received power levels) of commercial microwave links and observations of rainfall, air temperature, and air relative humidity. In addition, theoretical gaseous attenuation calculated from air temperature and relative humidity is provided.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. Raw data contain not regular time series, preprocessed data contain regular time series at 1-min and 5-min temporal resolution. Metadata are stored in textfiles.</p> <p>Dataset contains also R code for processing and analyzing the data as presented in the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities. The code is in the form of R Markdown files and interactive html notebooks.</p>
OU/NSSL CLAMPS Microwave Radiometer and Surface Meteorological Data from LAPSE-RATE
<p>The microwave radiometer measures downwelling microwave radiance in 14 channels from 22.2 to 60.0 GHz at 1 s temporal resolution. It is one of the core instruments in the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS-1) facility.</p> <p>A simple statistical retrieval is performed to retrieve precipitable water vapor (PWV) and liquid water path (LWP) from the observations at 23.8 and 31.4 GHz (Turner et al. 2007).</p> <p>This microwave radiometer has a Vaisala all-weather met station included. This station is positioned about two feet above the back of the CLAMPS trailer. It is possible that the trailer is affecting these met station observations in some conditions. Note that the wind data in these files are likely not representative of true environmental wind. Due to power requirements, CLAMPS was positioned close to a building, which likely influenced the wind speed and direction. Any wind data should be used with caution.</p> <p> </p> <p>References:</p> <p>Turner, D. D., S. A. Clough, J. C. Liljegren, E. E. Clothiaux, K. E. Cady-Pereira, and K. L. Gaustad, 2007: Retrieving Liquid Wat0er Path and Precipitable Water Vapor From the Atmospheric Radiation Measurement (ARM) Microwave Radiometers. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>45</strong>, 3680–3690, <a href="https://doi.org/10.1109/TGRS.2007.903703">https://doi.org/10.1109/TGRS.2007.903703</a>.</p>
The imprint on the cosmic microwave background (CMB) of Bianchi cosmologies
<p>These animations display the imprint that homogeneous but anisotropic Bianchi models induce in the cosmic microwave background (CMB).</p> <p> </p> <p> </p> <p>scalars_movie.mp4 : Bianchi VIIh/VII0 scalar modes, imprint on the CMB for varying morphology parameters (matter and dark-energy density, rotation scale of shear principal axes)</p> <p>vectors_movie.mp4: Bianchi VIIh/VII0 vector modes, imprint on the CMB for varying morphology parameters (matter and dark-energy density, rotation scale of shear principal axes)</p> <p>tensor_movie.mp4: Bianchi VIIh/VII0 regular tensor modes, imprint on the CMB for varying morphology parameters (matter and dark-energy density, rotation scale of shear principal axes)</p> <p>Additional fixed parameters for the three animations above:<br /> cold-dark-matter physical density: 0.112<br /> baryon physical density: 0.226<br /> Pattern orientation additionally fixed to put spiral in full view</p> <p>_____________________________</p> <p>SVTT_movie.mp4: combinations of Bianchi VIIh/VII0 scalar, vector, regular and irregular tensor modes for varying relative amplitudes of these degrees of freedom and phase angle.</p> <p>Additional fixed parameters:<br /> cold-dark-matter physical density: 0.112<br /> baryon physical density 0.0226<br /> matter density: 0.27<br /> dark energy density: 0.7<br /> rotation scale of shear principal axes: 0.5<br /> Pattern orientation additionally fixed to put spiral in full view</p>
Primary Sea Ice Edge from Satellite Passive Microwave Observations
<p>These are matlab output files with smoothed and unsmoothed primary ice edges around Antarctica. The primary ice edge is defined as the northernmost contour of 15% sea ice concentration, and defines the outer boundary of sea ice extent. The brightness data come from three satellites; SSM/I, AMSR-E, and AMSR2 and are converted to sea ice concentrations with the NASA Team 2 algorithm and the ARTIST algorithm.</p>
Radiofrequency to Microwave Coherent Manipulation of an Organometallic Electronic Spin Qubit Coupled to a Nuclear Qudit
<p>Dataset containing ASCII files for Figures 2-8 of the paper </p><p>Radiofrequency to Microwave Coherent Manipulation of an Organometallic Electronic Spin Qubit Coupled to a Nuclear Qudit</p><p>Inorg. Chem. 2021, 60, 11273−11286</p>
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
ScienceDex guides
Understand access before you commit
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.