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Comparing V2X and RADAR safety performance in NLOS scenarios
<p><strong>Scenario 1: </strong>Highway car following in road curve </p> <p>This scenario simulates a highway environment where two vehicles (HV and RV) communicate via V2X and HV is also equipped with radar sensor, while navigating a curved road. The leading remote vehicle (RV) is moving with constant speed and it is intially out of range of HV's radar sensor.</p> <p>Safety metrics such as Time-to-Collision (TTC) are evaluated to analyze the system's performance under the influence of NLOS situations and road curvature. <br><em>Dataset file: <code>Highway_road_curve_scenario.csv</code></em><br><br><strong>Scenario 2: </strong>Intersection scenario <br><br>This scenario involves two vehicles crossing each other paths and communicating via V2X at an intersection. Radar and V2X data are used to calculate safety indicators such as Time-to-Intersection (TTI), assessing the effectiveness of cooperative communication in mitigating collision risks. <br><em>Dataset file: <code>Intersection_scenario.csv</code></em></p>
BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars
<p><em>BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 151 European weather radars in 18 countries, with varying coverage from 2012 to 2023. These data were created by processing weather radar data - provided by the Operational Programme for the Exchange of Weather Radar Information (<a href="https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/">OPERA</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=baltrad/">Aloft bucket</a>.</p> <div> <div>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</div> </div> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 2 radars in Belgium.</li> <li><strong>ch.tgz</strong>: VPTS data from 5 radars in Switzerland.</li> <li><strong>cz.tgz</strong>: VPTS data from 2 radars in Czechia.</li> <li><strong>de.tgz</strong>: VPTS data from 20 radars in Germany.</li> <li><strong>dk.tgz</strong>: VPTS data from 5 radars in Denmark.</li> <li><strong>ee.tgz</strong>: VPTS data from 2 radars in Estonia.</li> <li><strong>es.tgz</strong>: VPTS data from 15 radars in Spain.</li> <li><strong>fi.tgz</strong>: VPTS data from 13 radars in Finland.</li> <li><strong>fr.tgz</strong>: VPTS data from 26 radars in France.</li> <li><strong>hr.tgz</strong>: VPTS data from 7 radars in Croatia.</li> <li><strong>il.tgz</strong>: VPTS data from 1 radar in Israel.</li> <li><strong>nl.tgz</strong>: VPTS data from 3 radars in the Netherlands.</li> <li><strong>no.tgz</strong>: VPTS data from 11 radars in Norway.</li> <li><strong>pl.tgz</strong>: VPTS data from 8 radars in Poland.</li> <li><strong>pt.tgz</strong>: VPTS data from 3 radars in Portugal.</li> <li><strong>se.tgz</strong>: VPTS data from 22 radars in Sweden.</li> <li><strong>si.tgz</strong>: VPTS data from 2 radars in Slovenia.</li> <li><strong>sk.tgz</strong>: VPTS data from 4 radars in Slovakia.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands
<p><em>UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 24 weather radars in Belgium, Germany and the Netherlands, with varying coverage from 2008 to 2023. These data were created by processing weather radar data - provided by the Royal Meteorological Institute of Belgium (<a href="https://www.meteo.be/">RMI</a>), German Meteorological Service (<a href="https://www.dwd.de/">DWD</a>) and Royal Netherlands Meteorological Institute (<a href="https://www.knmi.nl/">KMNI</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=uva/">Aloft bucket</a>.</p> <p>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 3 radars in Belgium.</li> <li><strong>de.gz</strong>: VPTS data from 18 radars in Germany.</li> <li><strong>nl.gz</strong>: VPTS data from 3 radars in the Netherlands.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
Typhoon radar images
<p>The high-resolution radar reflectivity images from 2010 to 2023. The data is for South China, especially for the Great Bay Area</p>
Poker Flat Incoherent Scatter Radar (PFISR) Observations of E-region Neutral Winds
<p>Updated: 12-15-2021</p> <p><strong>RULES OF THE ROAD:</strong></p> <p>You are welcome to use the data 'as is', however, please inform me via email if you plan to use the dataset. There are a number of small issues with the dataset that are best discussed. We are interested in publications that use the data and derived values that are presented within the dataset. <strong>If you plan to publish these results, please circulate a draft by me (SRK) and we would appreciate an offer of co-authorship or at minimum an acknowledgement. You should include the NSF funding numbers NSF AGS - 1853408</strong></p> <p> </p> <p>As a general warning, the data from PFISR are quite noisy and you may need to perform significant averaging to produce usable results. Again, please contact me and we can discuss this in more detail.</p> <p>Version v0.6.4.2021.07.12 - This was the final processed version at the time that the final report was submitted to the NSF.</p> <p> </p> <p><strong>--------------- Previous from before ------------------</strong></p> <p>This file contains Poker Flat Incoherent Scatter Radar (PFISR) E-region Neutral Winds Data. These data correspond to monthly data files that include the E-region neutral winds and other parameters for the from March 2013-June 2019.</p> <p><strong>Publications of the Joule Heating Results:</strong></p> <p>https://doi.org/10.1029/2021JA029371</p> <p>https://doi.org/10.1029/2021JA029719</p> <p> </p> <p><strong>Publication of Neutral Wind Results:</strong></p> <p>Hopefully we will have something in 2021. </p> <p> </p> <p><strong>RAW ISR Data:</strong> These data were processed from the following files found in: https://data.amisr.com/database/tmp/Kaeppler/winds/ and https://data.amisr.com/database/tmp/Kaeppler/missing_IPY.tar.gz Please note that the error on the line of sight velocities may have been overestimated in these data and we scaled them by a eVLOS/sqrt(10). Interested persons should contact Ashton Reimer or Roger Varney at SRI International for more information about these data, please see amisr.com</p> <p>Truthfully, the ISR data should eventually be reprocessed and then the winds algorithm run over it again. This is a step for future work.</p> <p> </p> <p><strong>Processing Code is available upon request via email.</strong></p> <p> </p> <p><strong>File Documentation:</strong></p> <p> </p> <p><strong>Please see the change log:</strong></p> <p>Purpose: This is the overarching program and functions which process the<br> E region neutral winds from the fitted AC and LP data from PFISR.<br> This is a conversion fo process_eregwinds_srk.py which was originally written by<br> Nicolls into a more formal python class structure.</p> <p>2017-10-05 - v0.2</p> <p>The ProcessEregionNeutralWinds.py file has been validated against process_eregwinds_srk.py<br> using 20161121.001_ac_3min-fitcal.h5, 20170301.013_ac_3min-fitcal.h5, 20170302.001_ac_3min-fitcal.h5.<br> The program to run these is ComparePrograms.py. At this point these program match.<br> I am going to start diverging the code base, first subtly in the Joule Heating<br> since I found that Mike just looped over Nbeams, which isn't quite right, you need to loop<br> over the beams that were selected.</p> <p>Changes from this point forward will produce different results.</p> <p>2017-10-10 - v0.3.2017.10.10</p> <p>Version v0.3, I made some IO changes but I may start processing some data with this version.</p> <p>Version v0.4 - lots of small edits made to the IO and the plotting software. It all seems to work<br> I have also included the SNR and Ne into the monthly plots and other information.<br> Made processing smoother.</p> <p>03 13 2018 - added solar local time converion</p> <p>v0.4.1 - 09 08 2018 added some ability to extract out the raw electron and SNR densities for each altitude bin<br> v0.4.2 - 10 15 2018 added in obtaining the F-region flows - want to check against the electric field.<br> v0.4.3 - 10 29 2018 added in some more altitude into the Joule Heating so I can make better figures<br> v0.4.4 - 11 20 2018 made some pretty major changes to IO to include consistent calculation of<br> Pedersen conductivity from FastConductivity.py. Made some changes to the Joule heating calculation and checked<br> formulas. It is worth checking again.</p> <p>v0.4.5 - 11 20 2018: added in Hall and Pedersen conductivities from fitted electron density data.<br> v0.4.6 - 12 03 2018: Tried to fix some of the double counting and time problems in testMakeMonthlyh5</p> <p>05 22 2019: added some statements to bypass the geophysical parameters. Also need in config file now.<br> Additionally wrote in IOEregionwinds a try except statement</p> <p>07 29 2019: Running the code for the 06 data reprocessed by Ashton</p> <p>v0.5.0 - 10-15-2019: put in some filtering on the LOS velocity discharging bad Chi square and bad error codes on the fit.</p> <p>v0.5.1 - 10-23-2019: changed chi square to 0.01 for lower boundary</p> <p>v0.5.3 - 12-02-2019: Added in that now passing in the Chi2 and Fitcode filtering by Config file<br> Bigger change that I am scaling the AC dVlos by some sort of factor while Ashton figures this out.<br> We decided that a conversative scaling would be to reduce the dVLOS by 1/sqrt(10).<br> The chi square produced in the data Ashton sent me typically was around 0.01, so the uncertaintiies on the LOS velocities<br> may be over estimated. So we are just changing this as a temporary fix while Ashton fixes the uncertainty estimation.</p> <p>v0.5.5 02 01 2020 - Added in calculation of Coriolis, Centrifugal, and Lorentz forcing<br> v0.5.5 02 10 2020 - Added a correction to qvert so that way I can calculate the lorentz term.<br> Found an error where qvert = 0 in the if statement goes to false.</p> <p>v0.5.5 02 15 2020 - Put in nuInscaler into the main program, scaling ALL kappas by the scaler number</p> <p>v0.5.6 02 28 2020 -- Added some more vlos diagostics and the calculation of the scale height. Added Altitude offset</p> <p>v0.5.6.2020.03.12_nuin_fracoff - testing putting in the Brekke formula for ion neutral collision frequency and took out frac</p> <p>v0.5.7.2020.04.10 - Put in Ashton's revised ion neutral collision frequency formulas into IO.<br> Also wrote a testscript and at least for the file I used was only different by 2.5%.<br> Revised where the mag data is being pulled from since the URL is deprecated<br> Added in Kappa which is now being interpolate - plan to see where kappa =1 is located for the paper.<br> commented out nuin scaler just so I am not chasing my tail</p> <p> test v0.5.7.2020.04.13_org commented back in original ion neutral collision frequency method<br> possible mistake that not summing up properly.</p> <p> test v0.5.7.2020.04.13_newnuin_orgsum_noTr800 - new formula for nuin except took off Tr>800.<br> I expect this should be almost the same as before since the formulas are basically the same.<br> did the original sum using frac[0] and frac[1] want to see if I am underestimating</p> <p> v0.5.7.2020.04.13_newnuin_orgsum_yesTr800 - same as above except now including Tr>800.</p> <p> 'v0.5.7.2020.04.13_newnuin_newsum_noTr800' - using the new sum now and new col freq</p> <p> v0.5.7.2020.04.13_updatedorg - updated original uses original method but including the NO term</p> <p>v0.6.0.2020.04.15 -- Now think I have the new ion neutral collision frequency working and validated.<br> Found a mistake in how I was calculating the ion neutral collision frequency that<br> the fraction weight I was using only included the O+ and O2+ terms and not NO+<br> Turns out I was basically weighting by about 0.5, so I was effectively reducing the<br> ion neutral collision frequency by about a factor of 0.5 or less...<br> From this point forward need to start using any results from > v0.6<br> This revision has changed previous results signi</p> <p>v0.6.0.2020.04.21 -- updated to now include the temperature correction for the O2+</p> <p>v0.6.1.2020.04.23 -- made a number of changes to the geomagnetic files and reprocessed from CDAweb.<br> Wrote new code to be able to process the files from CDAweb in the new format.<br> Also changed the geomagnetic data files</p> <p>v0.6.1.2020.06.07 -- changed the generation of Monthly files to hopefully be in order now<br> Added in missingIPY files given to me by ashton, maybe improve data covarege<br> Some work going to need to be done to make sure that all of the 10, 15, and 20 minute data are there.</p> <p>v0.6.1.2020.06.15_Weijia -- Updated the data for Weijia's study in particular since we are missing a lot of IPY data for 02-04 2013 and 2014.</p> <p>'v0.6.2.2020.07.01' -- Updated the data with new IPY27 mode for 2013 and 2014 Ashton processed. Also now put in mechanical Joule heating term.<br> Put in the conductance and conductivity now too.</p> <p>v0.6.2.2020.07.30 -- Made some changes to IO since Weijia noticed the mechanical heating terms were missing from the monthly files.</p> <p>v0.6.3.2020.10.19 -- Tried to elimated all extra instance of nuinscaler, and also output that variable. Added in variables<br> To get the Ti, Tn, ion neutral collision frequency along the vertical beam for diagnostic purposes<br> included dVest for F-region plasma drifts for Rafael</p> <p>v0.6.4.2020.11.20 -- Extracted some more parameters including F107 and the Hall and Pedersen Drags</p> <p>v0.6.4.2021.07.21 -- Final Run of data for NSF project</p> <p> </p>
Radar measurements on drones, birds and humans with a 77GHz FMCW sensor.
<p>This data set contains radar measurements on birds, humans and six different drones with a total of 75868 samples.</p> <p>The sensor was a frequency modulated continuous wave (FMCW) radar operating at 77 GHz with a mechanically scanning antenna.</p> <p>The 'ReadMe.txt' file contains a detailed description of the data.</p> <p>The data set is used in [1] where only FM-sweeps corresponding to azimuth index 54 to 203 are used (out of the provided 256), or 150 sweeps. </p> <p>When using this data set please refer to:</p> <p>[1] A. Karlsson, M. Jansson and M. Hämäläinen, "Model-Aided Drone Classification Using Convolutional Neural Networks," <em>2022 IEEE Radar Conference (RadarConf22)</em>, 2022, pp. 1-6, doi: 10.1109/RadarConf2248738.2022.9764194.</p> <p>The data in version 1.0 and 2.0 is identical apart from the format, ".mat" in 1.0 and ".npy" in 2.0</p>
Data for the Manuscripts of "Variability of Jakarta Rain-Rate Characteristics Associated with the Madden-Julian Oscillation and Topography" and "Subdaily Rain-Rate Properties in Western Java Analyzed Using C-Band Doppler Radar"
<p>This archive consists of the post-processed data of C-Band Doppler Radar (CDR) over Jakarta and surrounding regions for the studies of "Variability of Jakarta Rain-Rate Characteristics Associated with the Madden-Julian Oscillation and Topography" and "Subdaily Rain-Rate Properties in Western Java Analyzed Using C-Band Doppler Radar".</p> <p>The dataset is a gridded rainfall data derived from the local relationship of Z (reflectivity) from the CDR and rainfall (R) from stations. The derived rainfall data are in daily estimates from 2009 to 2012 with the format in NetCDF files.</p> <p>The CDR data were obtained from the projects “Hydrometeorological Array for Intraseasonal Variation-Monsoon Automonitoring (HARIMAU)” (JFY 2005-2009), and the Science Technology Research Partnership for Sustainable Development (SATREPS) “Maritime Continent Center of Excellence (MCCOE) (JFY 2009-2013) of the Japan Science and Technology Agency (JST)/Japan International Cooperation Agency(JICA) under a collaboration of the Agency for the Assessment and Application of Technology (BPPT)-Indonesia and Japan Agency for Marine-earth Science and Technology (JAMSTEC)-Japan.</p>
Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight"
<p>Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight". The cloud radar data files contains all the data used in the publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight" by Charlotte E. Wainwright, Sabrina N. Volponi, Phillip M. Stepanian, Don R. Reynolds, and David H. Richter, published in Methods in Ecology and Evolution in 2022. The MATLAB code implements the method described in the paper on the data files included.</p>
Spatially Aggregated Rain Radar Forecast for the Koeln Weiden, Germany
<p>radar_forecast.csv contains time series data generated by spatial aggregation of rain radar forecasts constructed using robust local optical flow extrapolation.</p>
Radar data for 12 hail events in Switzerland
Radar-based daily hail hazard data at 1km spatial resolution for 12 hail days in Switzerland between 2017 and 2021 provided by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss). Included dates are the ones where hail damage information is available from <a href="https://doi.org/10.5281/zenodo.11064767">"Reported hail damage for 12 hail events in Switzerland"</a> (YYYY-MM-DD): 2017-06-27, 2017-07-08, 2017-08-01, 2019-06-15, 2019-06-30, 2019-07-01, 2021-06-20, 2021-06-21, 2021-06-28, 2021-07-12, 2021-07-13, 2021-07-24. The dataset contains two variables based on single-polarization radar data: the Maximum Expected Severe Hail Size (MESHS) and the Probability of Hail (POH).
RADAR – Guideline on Personal Data
<p><strong>The HTML publication is available at <a href="https://nfdi4culture.de/go/E5380" target="_blank" rel="noopener">https://nfdi4culture.de/go/E5380</a>.</strong></p> <p>This guideline is intended to help you understand what information falls under the term “personenbezogene Daten” and which of these can be published on RADAR4Culture.</p>
Radar Reflectivity at Whillans Ice Plain
<p>This is an extracted data product for radar bed reflectivity from Whillans Ice Plain, West Antarctica. The original data are hosted by the Center for Remote Sensing of Ice Sheets (CReSIS; see associated citation below). The files here can be recalculate and are meant to be used within a set of computational notebooks here:<br>https://doi.org/10.5281/zenodo.10859135</p> <p>There are two csv files included here, each structured as a Pandas dataframe. You can load them in Python like:<br><code>df = pd.read_csv('./Picked_Bed_Power.csv')</code></p> <p>The first file, 'Picked_Bed_Power.csv' is the raw, uncorrected power from the radar image at the bed pick provided by CReSIS. There are also other useful variables for georeferencing, flight attributes, etc.</p> <p>The second file, 'Processed_Reflectivity.csv' is processed from the first file. Processing includes: 1) a spreading correction; 2) an attenuation correction; and, 3) a power adjustment flight days based on compared power at crossover points. This file also has identifiers for regions including "grounded ice", "ungrounded ice", and "subglacial lakes".</p>
Airborne radar observation dataset of sea surface height on 8 December 2016
<p>This dataset contains the results of time-series sea surface height (SSH) observation data of flight No.1, 2, 3, 4, 7, and 8 on 8 December 2016 by airborne altimeter measurement using a frequency modulated continuous wave (FM-CW) radar. The observation flight were carried out south of Japan passed over the Kuroshio Current. The data files are written in CSV format, the columns are UTC date, time, latitude, longitude, flight altitude, observed SSH, 1 min moving averaged SSH values, geoid height, and tide height. The geoid height and the tide height are derived by the EGM 2008 model (Pavlis et al. 2012) and the Nao.99Jb model (Matsumoto et al. 2000), respectively. The original data sampling rate of 800 microseconds is resampled by 80 milliseconds in each file. The data comes from a paper under review for Geophysical Research Letter.</p>
QIceRadar Antarctic Index of Radar Depth Sounding Data
<p>Database of known Antarctic radar depth sounding data. </p> <ul> <li>qiceradar_antarctic_index.gpkg - Database with ground tracks, citation info, and URLs of available data</li> <li>qiceradar_antarctic_index.qlr - QGIS style file that organizes the transects into campaign/institution and styles them based on radargram availability.</li> </ul> <p>Download both files to the same directory, then drag qiceradar_antarctic_index.qlr into QGIS.</p> <p>(Do not rename them!)</p> <p>----------</p> <p>v0.2.0: Removed outlier points from CReSIS & UTIG surveys; imported tracks of SOAR Vostok survey</p> <p>v0.1.0: Support for BAS, CReSIS and most released UTIG data. Still missing some citations/references, waiting to hear back from data providers.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Test Input and Output Files for Cloud Resolving Radar Simulator (CR-SIM) Version 4.0
<h2>Overview</h2> <p>The dataset includes input and output files for testing the Cloud-Resolving Radar Simulator (Oue et al. 2020) version 4.0. </p> <p>The following files are included:</p> <ul> <li>crsimtest1_inp_MP10.tar.gz includes input files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_inp_MP50.tar.gz includes input files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_inp_MP40.tar.gz includes input files for Test-3 with the microphysical option MP40</li> <li>crsimtest1_out_ref_MP10.tar.gz includes example output files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_out_ref _MP50.tar.gz includes example output files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_out_ref _MP40.tar.gz includes example output files for Test-3 with the microphysical option MP40</li> </ul> <p>Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v4.0).</p>
Proof-of-Concept Measurement for "Radar Band Fusion Using Frame-Based Compressed Sensing"
<p>This data set was created for a proof-of-concept test of the method described in "Radar Band Fusion Using Frame-Based Compressed Sensing". It consists of a measurment against a metal plate.</p> <p> </p>
DATASET: A low elevation imaging radar using a non-uniform coplanar receiver array for E~region observations
<p>Ionospheric Continuous-wave E region Bistatic Experimental Auroral Radar 3-Dimensional (ICEBEAR-3D) dataset for validation of the receiver antenna array reconfiguration, Suppressed-Spherical Wave Harmonic Transform (Suppressed-SWHT), and proper geometry for vertical interferometry using the geocentral angle.</p>
Horizontal and vertical velocities in Ho Chi Minh city by Sentinel-1 radar interferometry
<p>Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr.</p> <p>File "Dinh_HCMUD_v1.tif" is the 50-m vertical velocity in mm/year. Negative velocities represent movement subsidence.</p> <p>File "Dinh_HCMEW_v1.tif" is the 50-m east-west horizontal velocity in mm/year. Positive velocities represent movement Eastward.</p> <p>For more details on the technique, the reader can be found in [1].</p> <p>[1] Ho Tong Minh, D.; NGO, Y.; Lê, T.T.; Le, T.C.; Bui, H.S.; Vuong, Q.V.; Le Toan, T. Quantifying Horizontal and Vertical Movements in Ho Chi Minh City by Sentinel-1 Radar Interferometry. <em>Preprints</em> <strong>2020</strong>, 2020120382. Available: https://www.preprints.org/manuscript/202012.0382/v2</p> <p> </p> <p> </p>
25 years of high-frequency ground penetrating radar measurements of snow studies in Svalbard - metadata and GPS tracks
<p><strong>Surveys by ground penetrating radar (GPR) are accurate and cost-efficient, and have been conducted on Svalbard for more than 25 years, thus permitting the assessment of long term changes. The campaigns so far have covered various areas and the data is dispersed. The purpose of this report is to collect information about the conducted GPR snow cover measurements. The activities initiated in this project will be continued in the coming years and extended with a comprehensive data analysis.</strong></p> <p><strong>The dataset includes a description of metadata from GPR snow cover measurements in 1997-2022 (.CSV file) and GPS traces (.SHP files) of measurements taken in Svalbard.</strong></p> <p><strong>This study is part of the State of Environmental Science in Svalbard Report 2022 published by Svalbard Integrated Arctic Earth Observing System (SIOS).</strong></p>
Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars
<p>The datasets here correspond to a paper by Dawkins et al., “Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars”, originally submitted in November 2022.</p> <p>The following datasets are sufficient to produce Figure 2 and 3 in the main manuscript.</p> <p>Figure 2:</p> <ul> <li>Please use the 12 individual files with filenames, “Dawkins_et_al_2022__meteor_peak_altitude__*_data.txt”. Here the asterisk should be replaced by one of the following station abbreviations: CAR, COL, CPa, DAV, KIR, KSS, ROT, SMa, SOD, SVA, TdF, and TRO.</li> <li>Each file contains 5 columns: Column 1 is year (from 1999 to 2022), Column 2 is the time series of the annual peak altitude residuals (no units), Column 3 is the corresponding standard error, Column 4 is the multilinear model fit, and Column 5 is the normalized annual solar flux (F10.7) in arbitrary units.</li> </ul> <p>Figure 3:</p> <ul> <li>Please use “Dawkins_et_al_2022__meteor_peak_altitude_trends.txt”. For ease, a description of the different columns is included within this file.</li> </ul> <p> </p>
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.