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814 results for “radio”
A highly magnetized long-period radio transient exhibiting unusual emission features
Open the record for dataset details and reuse information.
Where did the finch go? Insights from radio telemetry of the medium ground finch (Geospiza fortis)
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Dataset for Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals
<p>Dataset of paper "Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals" presented in International Conference on Localization and GNSS (ICL-GNSS) 2020.</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted at an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing.</p> <p>The file "indoorMapping_processing.m" shows how to process and plot the shared data.</p> <p> </p> <p> </p>
The scalogram of the Radio Frequency Interference (RFI) signals
<p>This dataset includes the scalogram of RFI signals for RFI classification and Modulation recognition application.</p> <p>In this dataset, SoI is a video stream transmitted based on DVB-S2 standards in a real-time Satellite to Ground communication.</p> <p>CWI refers to a combination of SoI with Continous Wave Interference (CWI)</p> <p>MCWI is also a combination of SoI with Multi-CWI</p> <p>and CI indicates the combination of SoI with Chirp Interference.</p> <p>Moreover, SoI has been transmitted in four different modulation types namely QPSK, 8APSK, 16APSK and 32APSK.</p> <p> </p>
Reproduction package for "ClG 0217+70: A massive merging galaxy cluster with a large radio halo and relics"
<p>This is the reproduction package for "ClG 0217+70: A massive merging galaxy cluster with a large radio halo and relics", which has been accepted for publication in A&A.</p> <p>To use this package, please read the README.</p> <p>This package is tested in an environment that contains:</p> <ul> <li>SPEX v3.06</li> <li>CIAO v4.12</li> <li>python 3.6.5 <ul> <li>numpy 1.14.3</li> <li>astropy 3.0.2</li> <li>astroquery 0.4</li> <li>scipy 1.1.0</li> <li>matplotlib 2.2.2</li> </ul> </li> </ul>
Supporting data and scripts for Nature Astronomy article "Detection of two bright radio bursts from magnetar SGR 1935+2154" by Kirsten et al.
<p>This data set contains scripts and data used to analyze the observations described in Nature Astronomy article "Detection of two bright radio bursts from magnetar SGR 1935+2154" by Kirsten et al. 2020. The scripts used to create the plots therein also included.</p>
La radio latinoamericana en la crisis sanitaria
<p>En Foro Abierto hablamos sobre La radio latinoamericana en la crisis sanitaria junto a la Lic. Laura Elena Padrón, Productora de Radio Educación, Mexico, la Ph.D. Ana Deisy Guerrero, presidenta del Círculo Dominicano de Locutores, el Mgtr. Armando Grijalva, radiodifusor y académico ecuatoriano y la Lic. Bélgica Chela; comunicadora radiofónica comunitaria. Coordinadora del equipo informativo de Las Escuelas Radiofónicas Populares de Ecuador.</p>
Dataset: Radio Frequency Characteristics of Volcanic Lightning
<p>This data set contains broadband VHF waveforms of vent discharges and volcanic lightning<br> flashes collected during an explosive eruption of Sakurajima volcano in Japan on November 8,<br> 2019. Each file in the dataset is a 3 microsecond waveform. The amplitude values of the<br> waveform are in arbitrary voltage units. The sampling rate is 180 MS/s. There are two tar<br> archive files included in this data set: one for all of the vent discharge waveforms and one for all<br> of the flash waveforms. The UTC time of the waveform, corresponding to the time of the peak<br> of the waveform is included in each file name in the tar archives. The UTC time is given as<br> seconds of the day on November 8, 2020.</p>
Data from: UAV wildlife radio telemetry: system and methods of localization
1. The majority of bird and bat species are incapable of carrying tags that transmit their position to satellites. Given fundamental power requirements for such communication, burdened mass guidelines, and battery technology, this constraint necessitates the continued use of very high frequency (VHF) radio beacons. As such, efforts should be made to mitigate their primary deficiencies: detection range, localization time, and localization accuracy. 2. The integration of a radio telemetry system with an unmanned aerial vehicle (UAV) could significantly improve the capacity for data collection from VHF tags. We present a UAV-integrated radio telemetry system that relies on open source hardware and software. Localization methods including signal processing, bearing estimation based on principal component analysis, localization techniques, and test results are discussed. 3. Using a low power beacon applicable for bats and small birds, testing showed that the improved vantage of the UAV-radio telemetry system (UAV-RT) provided significantly higher received signal power compared to low level flights (maximum range beyond 1.4 km). Flight testing of localization methods showed median bearing errors between 2.3-6.8 degrees, with localization errors of between 5-14% of the distance to the tag. In a direct comparison to an experienced radio telemetry user, the UAV-RT system provided bearing and localization estimates with 53% less error. 4. This paper introduces the core functionality and use methods of the UAV-RT system, while presenting baseline localization performance metrics. An associated website hosts plans for assembly and software installation. The methods of UAV-RT use for tag detection will be further developed in future works. For both the detection and localization problems, the mobility of a flying asset drastically reduces tracker time requirements. A seven-minute flight would be sufficient to collect five equally spaced bearing estimates over a 1 km transect. The use of a software defined radio on the UAV-RT system will allow for the simultaneous detection and localization of multiple tags.
BRAMS Radio Spectrograms and Spectrogram Samples for Automatic Detection of Meteor Echoes
<p>The files in this dataset are based of radio recordings taped by BRAMS (Belgian RAdio Meteor Stations), the Belgian meteor detection network.</p> <p>Included in the dataset are the original BRAMS radio recordings (stored as .wav audio files), the spectrogram data for each radio recording (stored as .csv files) and the meteor and non-meteor samples extracted from the radio spectrograms (stored as .csv files).</p> <p>It should be noted that the the spectrogram data was sampled using a sliding window of size 30x20 pixels and the samples extracted in this manner were further processed by calculating the vertical average of each column in the 30x20 matrixes. The result of this sampling procedure is a set of data vectors containing the average power of the signal found in the original 30x20 spectrogram sample.</p>
eclipse radio propagation 2017
<p>Bill Riches, WA2DVU</p> <p>39.08, -7486</p> <p>MPG audio file start 1400UT</p> <p>Antenna: Mosley 20 meter beam at 60 feet pointing west.</p> <p>Receiver: HP 3586B Selective Voltmeter</p> <p>FX Reference: Lucent KS-24361 GPS</p> <p>FX measurement technique: I use the HP3586B to receive WWV on 10 mHZ. I send the 15625 hz IF output of the HP 3586B to Spectrum Lab. The 3586 is locked to my GPS. Spectrum lab calculates error every 60 seconds and generates an Excel chart of the frequency difference. Computer clock is corrected with NBS program, computer sound card is corrected with 1 PPS pulse from GPS fed to Spectrum lab. </p>
Reproduction package for the paper: "Detection of ultra-fast radio bursts from FRB 20121102A"
<p><br># Reproduction package for the paper "Detection of ultra-fast radio bursts from FRB 20121102A"<br>Authors: Mark P. Snelders, K. Nimmo, J.W.T. Hessels, Z. Bensellam, L.P. Zwaan, P. Chawla, O.S. Ould-Boukattine, F. Kirsten, J.T. Faber and V. Gajjar.<br>arXiv link: https://arxiv.org/abs/2307.02303<br>DOI published article: Nature Astronomy, 19 October 2023, https://doi.org/10.1038/s41550-023-02101-x<br><br>This work has been made possible by an NWO Vici grant (Principal investigator, J.W.T.H.). <br><br>## Raw Data<br><br><strong>- The data are 100% publicly available and are explained in great detail in the following post: http://seti.berkeley.edu:8000/frb-data/</strong><br><strong>- The data are available from the Breakthrough Initiatives Open Data Portal with target name FRB121102: https://breakthroughinitiatives.org/opendatasearch</strong><br><br>In this paper we have re-processed and re-analysed data from the Green Bank Telescope that made use of the Breakthrough Listen digital backend. I will call this the GBT BL data. Below you can find links to multiple papers, GitHub repositories and blogposts that explain the GBT BL data.</p><p>- My paper describing the search and analysis of the ultra-fast radio bursts:<br> * https://ui.adsabs.harvard.edu/abs/2023arXiv230702303S/abstract<br> * https://www.nature.com/articles/s41550-023-02101-x<br>- First detection of the bursts at 8 GHz: https://ui.adsabs.harvard.edu/abs/2018ApJ...863....2G/abstract<br>- More bursts from the same dataset with machine learning detections: https://ui.adsabs.harvard.edu/abs/2018ApJ...866..149Z/abstract<br>- Explaining the Breakthrough Listen project: https://ui.adsabs.harvard.edu/abs/2017AcAau.139...98W/abstract<br>- Explaining the GBT breakthrough listen recorder: https://ui.adsabs.harvard.edu/abs/2018PASP..130d4502M/abstract<br>- Explaining the data formats: https://ui.adsabs.harvard.edu/abs/2019PASP..131l4505L/abstract<br>- Python 2 code to work with the baseband data: https://github.com/greghell/extractor (NOTE THAT IT IS PYTHON 2!!) (I recommend using Python 2.7 if you make use of that repo)<br>- Structure of the baseband data: https://github.com/UCBerkeleySETI/breakthrough/blob/master/doc/RAW-File-Format.md<br>- More information: https://github.com/UCBerkeleySETI/breakthrough/blob/master/GBT/waterfall.md<br>- A version of dspsr, called bl-dspsr, that can work with the GBT BL baseband data: https://github.com/UCBerkeleySETI/bl-dspsr<br><br>## Software<br><br>- The data was processed on multiple machines with various operating systems, which include, but are not limited to, macOS, Ubuntu and centOS.<br>- All the used software is open source, see the section above for more information, and also see the 'software' section in the paper.<br><br>## Figures and Tables</p><p>The files in this Zenodo package should be self-explanatory. E.g. `table_1.tar` contains all the scripts/notebooks/files needed to make table_1, and also contains table 1 itself. <br>The file: 'general_info.tar' is basically a txt file with the same info as provided here and it contains an offline version of the Breakthrough Listen blogpost that that explains the raw data. <br>The file: `helper_functions.tar` is a tarball that contains a Python file with a collection of helper functions that are used in the Jupyter notebooks (and the figures are made in the Jupyter notebooks). It also contains some files that are needed to e.g. remove the instrumental delay from the data.<br> <br>## End-to-End analysis scripts<br>The Python code/Jupyter notebooks in the tarfiles are end-to-end. </p><p>## Intermediate data products </p><p>The file `data_and_data_info.tar` contains two intermediate data products (both several gigabytes in size) and a txt file explaining the files and how they were made. Due to the Zenodo file size limitations I cannot upload everything. Please contact me at snelders@astron.nl or m.p.snelders@uva.nl or via ORCID to request any other files. <br><br><br><br> </p>
The Discovery of 63 Giant Radio Galaxies in FIRST using DRAGN-hunter
<p>A catalog of 63 giant radio galaxies (GRGs) identified in the FIRST survey using the DRAGN-hunter algorithm. Included here are the table of GRGs (in votable format; described below) and a tarball containing images for all 63 sources. These images consist of radio contours from FIRST overlaid on grz optical images from LS DR9, with the position of the identified host marked by a green circle.</p><p> </p><p><i><strong>Table description</strong></i><br><br><i>Columns [unit]:</i></p><ul><li><strong>Name,</strong> Name of the host galaxy (1)</li><li><strong>RAJ2000</strong> [deg], R.A. of the host galaxy</li><li><strong>DEJ2000</strong> [deg], Decl. of the host galaxy</li><li><strong>rmag</strong> [mag], r-band magnitude of the host galaxy</li><li><strong>z</strong>, Redshift of the host galaxy</li><li><strong>zType</strong>, Photometric or spectroscopic redshift (2)</li><li><strong>SFIRST</strong> [mJy], Flux density in FIRST</li><li><strong>logLFIRST</strong> [W/Hz], log10 of 1.4GHz luminosity</li><li><strong>LAS</strong> [arcsec], Largest Angular Size</li><li><strong>LLS</strong> [Mpc], Largest Linear Size (3)</li></ul><p><i>Notes:</i></p><ol><li>uncertain hosts are chosen to yield the lower of several possible redshifts; SDSS J010931.12-023723.8 is the central of three potential host galaxies at similar redshifts and blended into WISEA J010930.98-023722.0<br> </li><li>p = photometric redshift from 2022MNRAS.512.3662D<br>s = spectroscopic redshift from SDSS DR16 (2020ApJS..249....3A)<br> </li><li>based on H_0_=70 km/s/Mpc, Omega_m_=0.3, Omega_Lambda_=0.7<br><br> </li></ol>
Reproduction package for 'Low-frequency radio observations of recurrent nova RS Ophiuchi with MeerKAT and LOFAR'
<p>This is a basic reproduction package for the paper "Low-frequency radio observations of recurrent nova RS Ophiuchi with MeerKAT and LOFAR".</p><p> </p><p> </p>
Keynote: Bringing Reinforcement learning Into Radio Light Network for Massive Connections
<blockquote><p>3GPP standardization has been progressing at an astonishingly rapid phase, where Release 15 and Release 16 have set the foundations of the 5G system, while Release 17 provides enhancements and optimizations to enable support for further use cases. In parallel to 5G standardization efforts, several initiatives worldwide endeavour to drive and support the evolution of smart networks and services. Among others Europe is establishing the <i>Joint Undertaking on Smart Networks and Services</i> in the frame of the Horizon Europe programme for research and innovation. Other initiatives are complementing the European initiative, such as <i>Secure 5G & Beyond Act</i> in the U.S., <i>roadmap towards 6G </i>in Japan, <i>MSIT 6G programme</i> in S. Korea, and <i>MIIT 6G programme</i> in China.</p></blockquote><blockquote><p>The workshop will provide an opportunity for reflection and discussion about requirements and architectural considerations for future generations of mobile systems. The focus will be on presenting version 4.0 of the Architecture white paper developed by the 5G PPP architecture working group. It will allow to move from 5G and beyond towards a fully-fledged 6G architecture.</p></blockquote>
The Big Dipper with the Sardinia Radio Telescope SRT
<p>Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Time-lapses of rotation of Big Dipper or Southern Cross: The Big Dipper with the Sardinia Radio Telescope SRT, by Antonio Finazzi.</p> <p>This time-lapse captures the movement of the stars alongside the majestic 64-metre Sardinia Radio Telescope (SRT) from the National Institute of Astrophysics (INAF), with special attention to the renowned Big Dipper against the backdrop of the celestial sphere. The camera pans as the famous asterism sinks in the sky while planes fly past and the radio telescope rotates. The harmonious interplay between the stellar pathways and the colossal dish of the radio telescope creates a mesmerising visual ode to the cosmic ballet taken in September 2019, earning it an honourable mention in the category of Time-lapses of rotation of Big Dipper or Southern Cross.</p> <p><strong>Credit</strong>: Antonio Finazzi/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Data Set: Radio Frequency Senser (RFS) example waveforms, altitudes, and density plot
<p>This data set contains data for the paper entitled "Radio Frequency Sensor: radio frequency lightning detection in geostationary orbit", submitted to <em>Radio Science </em>in December 2023.</p> <p>This data set contains three types of RFS data. 1) The first are time domain waveforms of three lightning events, in both the RFS high band (116 – 142 MHz) and the RFS low band (10-60 MHz), sampled at 155 MHz. The waveforms are right-hand circularly polarized waveforms. 2) The second type of data is altitudes and locations of trans-ionospheric pulse pairs (TIPPs) over time. The locations were determined by time coincidence with geolocated World Wide Lightning Location Network strokes. 3) The third type is RFS events per square kilometer per year in latitude and longitude. The RFS event were located by time correlation to Earth Networks Global Lightning Network lightning strokes.</p> <p><strong>Data set 1:</strong></p> <p>Consists of six ASCII files – 3 high band & 3 low band example RFS right-hand circularly polarized waveforms. Each ascii file contains a header with the RFS event time in UTC, the label of “RFS high band (77.5 – 155 MHz)” or “low band (0 – 77.5 MHz)”, and sample rate (155 MHz). Data following the header are time samples of electric field in uV/m sampled at 155 MHz.</p> <p>Filenames are:</p> <p>RFS_waveform_HighBand_20230607_010803.txt</p> <p>RFS_waveform_HighBand_20230607_015553.txt</p> <p>RFS_waveform_HighBand_20230607_034055.txt</p> <p>RFS_waveform_LowBand_20230607_010803.txt</p> <p>RFS_waveform_LowBand_20230607_015553.txt</p> <p>RFS_waveform_LowBand_20230607_034055.txt</p> <p> </p> <p><strong>Data set 2:</strong></p> <p>Filename = ‘RFS_TIPPs_20230607_0100-0500_UTC.txt’</p> <p>1 ASCII comma separated value (CSV) file. Columns are:</p> <p>1. UTC date yyyy/mm/dd</p> <p>2. UTC seconds of day</p> <p>3. WWLLN-determined latitude (degrees, wwlln_latitude in header)</p> <p>4. WWLLN-determined longitude (degrees, wwlln_longitude in header)</p> <p>5. TIPP-estimated height (km, height in header)</p> <p> </p> <p><strong>Data set 3:</strong></p> <p>Filename = ‘RFS_map.csv’</p> <p>1 CSV file of a 2-dimensional data set.</p> <p>1. Row 1, Longitude (degrees, in 0.25-degree steps)</p> <p>2. Column 1, Latitude (degrees, in 0.5-degree steps)</p> <p>3. 2-D grid in latitude and longitude: events per square kilometer per year</p> <p>Notes: Since the RFS coverage range goes across longitude = -180/180 degrees, longitudes go from 147.75 to 180, then start at -180 to -12.75. Latitude range goes from -58.5 to 68.5, as there were no detected RFS events outside these latitudes.</p> <p>The three examples given in data set 1 are those shown in LA-UR-23-32419, Figure 2. The TIPP data in data set 1 is shown in LA-UR-23-32419, Figure 4, and comprises data from 07 June 2023 between 01:00-05:00 UTC. Data set 3 contains RFS event rates per sq. km per year for data from 1 March 2022 – 1 March 2023, with the caveats described in LA-UR-23-32419.</p>
Radio Tower, Komatubara Children's park, Kyoto
Created in RealityCapture by Capturing Reality from 566 images in 04h:24m:27s. with iphone 6s, huawei p20. Source: Objaverse 1.0 / Sketchfab
Vintage Montgomery Ward & Co. Airline Radio
Montgomery Ward & Co. Airline Radio Model 04BR-609A Manufactured in 1940 Scanned with an Artec Leo Processed with Artec Studio 16, Blender, and XNormal. Source: Objaverse 1.0 / Sketchfab
Korsnes museum, Old radio
https://www.museumnord.no/en/our-venues/korsnes-museum/ My 3D model from photos generated with photogrammetry software 3DF Zephyr v6.507 processing 125 images Source: Objaverse 1.0 / Sketchfab
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.