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814 results for “radio”
1X4 radio channel data at 3.42 GHz for device-free human sensing
<p>Datasets for <em>CAMPAIGN-I and CAMPAIGN-II </em>from paper 'Beamsteering for Ad-Hoc Recognition of Multi-Human Targets Performing Distinct Activities'. The datasets belong to the <a href="http://ambientintelligence.aalto.fi/radiosense/">Radiosense</a> project.</p> <p>'.</p>
Evaluating F10.7 and F30 Radio Fluxes as Long-Term Solar Proxies of Energy Deposition in the Thermosphere
<p><span>We use model simulations and observations to examine how well the F10.7 and F30 solar radio fluxes represent solar forcing in the thermosphere during the last 60 years of weakening solar activity. We found that increased saturation of F10.7 during the last two extended solar minima leads to an overestimation of solar energy deposition, which manifests as a change in the linear relation between thermospheric parameters and F10.7. On the other hand, the linear relation between thermospheric parameters and F30 remains nearly the same throughout the whole studied period because of a recently found relative increase of F30 with respect to F10.7. Therefore, F30 is a more consistent proxy than F10.7 during the last 60 years. We note that continued evaluation is needed to see how well F10.7 and F30 will serve as solar proxies in the future when solar activity may start increasing toward the next grand maximum.</span></p>
Music Informatics for Radio Across the GlobE (MIRAGE) MetaCorpus (v0.2)
<h1>Overview</h1> <p>Welcome to the <strong><em>Music Informatics for Radio Across the GlobE</em></strong> (<em><strong>MIRAGE</strong></em>) <strong><em>MetaCorpus</em></strong>. The current (v0.2) development release consists of metadata (e.g., artist name, track title) and musicological features (e.g., instrument list, voice type, tempo) for 1 million events streaming on 10,000 internet radio stations across the globe, with 100 events from each station. </p> <p>Users who wish to access, interact with, and/or export metadata from the MIRAGE-MetaCorpus may also visit the MIRAGE online dashboard at the following url:</p> <ul> <li><a href="https://pearl-laboratory.github.io/mirage-mc/" target="_blank" rel="noopener">https://pearl-laboratory.github.io/mirage-mc/</a></li> </ul> <h1>Attribution</h1> <p>The current MIRAGE-MetaCorpus is available under a CC4 license. Users may cite the dataset here:</p> <blockquote> <p>Sears, David R.W. “Music Informatics for Radio Across the Globe (MIRAGE) Metacorpus -- 2024”. Zenodo, July 19, 2024. <a href="https://doi.org/10.5281/zenodo.12786202" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12786202</a>.</p> </blockquote> <p>Users accessing the MIRAGE-MetaCorpus using the online dashboard should also cite the following ISMIR paper:</p> <blockquote> <p>Ngan V.T. Nguyen, Elizabeth A.M. Acosta, Tommy Dang, and David R.W. Sears. "Exploring Internet Radio Across the Globe with the MIRAGE Online Dashboard," in <em>Proceedings of the 25th International Society for Music Information Retrieval Conference </em>(San Francisco, CA, 2024). </p> </blockquote> <h1>Data Sources</h1> <p>This repository of the MIRAGE-MetaCorpus contains 81 metadata variables from the following open-access sources:</p> <ul> <li>Radio Garden (RG) -- <a href="https://radio.garden" target="_blank" rel="noopener">https://radio.garden</a></li> <li>Natural Earth map data set (NE) -- <a href="https://www.naturalearthdata.com/" target="_blank" rel="noopener">https://www.naturalearthdata.com/</a></li> <li>Internet Radio Station Stream Encoder (SE)</li> <li>Annotator Review (AR)</li> <li>Monitoring/Matching Algorithm (MA)</li> <li>WikiData (WD) -- <a href="https://www.wikidata.org" target="_blank" rel="noopener">https://www.wikidata.org</a></li> <li>MusicBrainz (MB) -- <a href="https://musicbrainz.org/" target="_blank" rel="noopener">https://musicbrainz.org/</a></li> </ul> <p>Each event also includes attribution metadata from the following commercial sources:</p> <ul> <li>Spotify (SP) -- <a href="https://open.spotify.com/" target="_blank" rel="noopener">https://open.spotify.com/</a> <ul> <li>Note that users may examine an additional 19 metadata variables on the MIRAGE online dashboard that were obtained from the Spotify API.</li> </ul> </li> <li>Musixmatch (MX) -- <a href="https://www.musixmatch.com/" target="_blank" rel="noopener">https://www.musixmatch.com/</a></li> <li>YouTube (YT) -- <a href="https://www.youtube.com/" target="_blank" rel="noopener">https://www.youtube.com/</a></li> <li>Genius (GE) -- <a href="https://genius.com/" target="_blank" rel="noopener">https://genius.com/</a></li> <li>AZlyrics (AZ) -- <a href="https://www.azlyrics.com/" target="_blank" rel="noopener">https://www.azlyrics.com/</a></li> </ul> <h1>Data Sets</h1> <p>The metadata reflect information about each event's location (e.g., city, country), station (name, format, url), event (id, local time at station, etc.), artist (name, voice type, etc.), and track (e.g., title, year of release, etc.). For that reason, the MIRAGE-MetaCorpus includes the following datasets:</p> <ul> <li>MIRAGE.csv -- the complete metacorpus (1 million)</li> <li>events.csv -- all event-level metadata (1 million)</li> <li>tracks.csv -- all track-level metadata (414,886)</li> <li>artists.csv -- all artist-level metadata (259,783)</li> <li>stations.csv -- all station-level metadata (10,000)</li> <li>locations.csv -- all location-level metadata (4,324)</li> </ul> <p>A subset of the MIRAGE-MetaCorpus is also available for events with metadata from online music libraries that reliably matched the event's description in the radio station's stream encoder:</p> <ul> <li>MIRAGE_reliable.csv (473,850)</li> <li>events_reliable.csv (473,850)</li> <li>tracks_reliable.csv (204,969)</li> <li>artists_reliable.csv (80,005)</li> <li>stations_reliable.csv (9,284)</li> <li>locations_reliable.csv (4,142)</li> </ul> <h1>Contact</h1> <p>If you are a copyright owner for any of the metadata that appears in the MIRAGE-MetaCorpus and would like us to remove your metadata, please contact the developer team at the following email address: <a href="mailto:miragedashboard@gmail.com" target="_blank" rel="noopener">miragedashboard@gmail.com</a> </p>
BLE RSS dataset for fingerprinting radio map calibration
<p>The dataset contains Bluetooth Low Energy signal strengths measured in a fully furnished flat. The dataset was originally used in the study concerning RSS-fingerprinting based indoor positioning systems. The data were gathered using a hybrid BLE-UWB localization system, which was installed in the apartment and a mobile robotic platform equipped for a LiDAR. The dataset comprises power measurement results and LiDAR scans performed in 4104 points. The scans used for initial environment mapping and power levels registered in two test scenarios are also attached.</p> <p>The set contains both raw and preprocessed measurement data. The Python code for raw data loading is supplied.</p> <p>The detailed dataset description can be found in the <em>dataset_description.pdf</em> file.</p> <p>When using the dataset, please consider citing the original paper, in which the data were used:</p> <p>M. Kolakowski,<strong> “Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning”</strong>, <em>Sensors</em> , vol. <em>21</em>, 6270, Sep. 2021 <a href="https://doi.org/10.3390/s21186270">https://doi.org/10.3390/s21186270</a></p> <p> </p>
Migrating Tides in the Stratosphere from COSMIC Radio Occultation Data
<p>These analyses of the migrating tides in temperature, microwave refractivity, and geopotential in the Earth’s stratosphere are analyzed using GPS radio occultation (RO) data obtained by the COSMIC constellation of six satellites in orbit planes separated by 30° in ascending node. The tides are analyzed monthly, beginning with November 2006 and ending with December 2016. The radio occultation retrievals used as input to the analyses are obtained from the Climate Data Record v1 of the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF; romsaf.org). The reference model that is used for the sake of comparison are the 3-, 6-, 9-, and 12-hr forecasts of the ERA-Interim reanalysis project. </p>
Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"
<p>This dataset was created as suplementary material for research article: <strong>Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms</strong></p> <p>This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p> <p>The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office.</p> <p>The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files:</p> <p><strong>Data</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table:</p> <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"><strong>...</strong></th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td><strong>B1</strong></td> <td>1.85</td> <td>0.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td><strong>B2</strong></td> <td>1.85</td> <td>1.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td><strong>B3</strong></td> <td>1.85</td> <td>2.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">...</th> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td><strong>B11</strong></td> <td>1.85</td> <td>10.1</td> <td><strong>...</strong></td> </tr> </tbody> </table> <p><strong>Data_Eval</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected.</p> <p><strong>Processed_Data</strong></p> <p>Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values.</p> <p>The CSV files are in a format:</p> <blockquote> <p><code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code></p> </blockquote> <p><strong>Data_Scenarios</strong></p> <p>This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table:</p> <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> <p>Data Name</p> </th> <th scope="col"> <p>Scenario Description</p> </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> <p>The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table:</p> <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> <p><strong>Suffix</strong></p> </td> <td> <p><strong>Suffix Description</strong></p> </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> <p>These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc.</p> <p>The RSSI data are in format:</p> <blockquote> <p>RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</p> </blockquote> <p>The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0)</p> <blockquote> <p>X, Y, 0, 0, 0</p> </blockquote>
Graph 9: TV and Radio Broadcasts of Heiner Müller's Interviews.
<p>Graph 9 shows when and where Heiner Müller's interviews were initially broadcasted and with whom he recorded the respective interview.</p>
Dataset Graph 9: TV and Radio Broadcasts of Heiner Müller's Interviews.
<p>Dataset for Graph 9. Data collected based on the Heiner Müller Werkausgabe.</p> <p>Graph 9 shows when and where Heiner Müller's interviews were initially broadcasted and with whom he recorded the respective interview.</p>
Pulse Profiles and Times of Arrival Measurements from a Rotating Radio Transient Census with the Irish LOFAR station
<p>The reduced data produced as a part of a census of rotating radio transients (RRATs) with the Irish LOFAR station.</p> <p> </p> <p>This deposit contains:</p> <ul> <li>Metadata regarding observed data</li> <li>A copy of RFI-zapped, single pulse archives</li> <li>A copy of the time-flattened periodic emission archives</li> <li>A copy of the measured pulse times of arrival</li> <li>Ephemerides used and produced as a part of the work</li> </ul> <p>Additional data can be made available on request to the author.</p>
Dataset from VR Streaming Server (Emulated) and Radio Access Network for Streaming Traffic
<p>The dataset contains an experiment in a site where UEs attach to a gNodeB that provides access to a streaming server that is stressed with high demanding transcoding workloads to emulate VR/AR processes. The UEs are realized through the Remote UE mode enabled by Amarisoft Simbox emulator, and the gNodeB is realized through the Amarisoft Callbox, which also provides the user plane function. The emulated VR streaming server is deployed as a Nginx pod in a Kubernetes cluster.</p> <p>We rely on MonB5G sampling functions that feed monitoring data (CPU and RAN parameters) to the monitoring system. A streaming video server has been deployed with the help of a NGINX server. It provides video-on-demand and video streaming, which can be accessed by any user (or UE) for real-time reproduction. This VR video streaming emulation aids to assess the performance of the network and therefore the benefits that each solution has brought. The video “Big Buck Bunny” with h.264 encoding and a resolution of 1920x1080p has been used for the experiments.The description of dataset features are:<br> 1-) Index Number,<br> 2-) Time: Time of the experiment,<br> 3-) N: number of VR streaming clients,<br> 4-) C: Average CPU of VR streaming server [mc]<br> 5-) O: Outbound traffic at the server average outbound traffic (O) flowing from the data interface of the video server. <br> 6-) R: Instantaneous downlink bit rate [Mbps],</p> <p>The original video file information:</p> <table> <tbody> <tr> <td> <p>Video codec </p> </td> <td> <p>Advanced Video Codec (AVC) </p> </td> </tr> <tr> <td> <p>Width </p> </td> <td> <p>1920 pixels </p> </td> </tr> <tr> <td> <p>Height </p> </td> <td> <p>1080 pixels </p> </td> </tr> <tr> <td> <p>Display aspect radio </p> </td> <td> <p>16:9 </p> </td> </tr> <tr> <td> <p>Duration </p> </td> <td> <p>10 min 34 s </p> </td> </tr> <tr> <td> <p>Max Bitrate </p> </td> <td> <p>16.7 Mb/s </p> </td> </tr> <tr> <td> <p>Frame rate </p> </td> <td> <p>30 FPS </p> </td> </tr> </tbody> </table>
Language Recognition for SSB modulated HF Radio Signals of Short Duration - Example Files
<p>These files are example files of the dataset used in our work "Language Recognition for SSB modulated HF Radio Signals of Short Duration". Two files are 10 second HF radio segments from Russian amateur radio communications. The other six files are a single recording from CommonLanguage (https://huggingface.co/datasets/common_language) and five modified versions of this file, created by applying the proposed HF radio simulation approach. Additional details can be found in the Paper.</p>
Image-based Classification of Intense Radio Bursts from Spectrograms: An Application to Saturn Kilometric Radiation
<p>A catalogue of 4874 of the Low Frequency Extensions (LFEs) of Saturn Kilometric Radiation (SKR) detected by Cassini/RPWS from the beginning of 2004 until mission end in 2017. The LFEs presented in this catalogue were identified using a modified U-Net architecture that applied semantic segmentation to spectrogram images in order to extract the exact frequency-time coordinates of the LFE. The files consist of a .json file with the coordinates of each LFE in Time Frequency Catalogue (TFCat) format (Cecconi et. al. 2023). We also include a .csv file with the start and stop times of each LFE in the form of python datetime timestamps, with the average predicted probability per LFE as an accompanying column. </p>
Known-fate survival information for radio-tagged snowshoe hares captured in Bonanza Creek Experimental Forest from June 2008 to November 2012
This dataset contains known-fate survival information for radio-tagged snowshoe hares captured in two 200 x 450 m live-trapping grids in Bonanza Creek Experimental Forest from June 2008 to November 2012. The data can be sorted and viewed by year, site, number at risk, and number of mortalities.
LOFAR dataset for deep learning assisted data Inspection for radio astronomy
<p>This dataset is used for the training of the magnitude and phase-based VAE in the paper entitled "Deep learning assisted data inspection for radio astronomy".</p> <p>For uploading purposes the dataset has been separated into 4 different .zip files. In order to use this dataset each of the zip files should be extracted into a single directory so that the training can be performed on all files at the same time. <br> <br> More information can be found on <a href="https://github.com/mesarcik/DL4DI">the project github repository</a>. </p>
Radio Frequency Interference (RFI)
<p>In this dataset, Signal of Interest (SoI) is a real-time video stream that is transmitted using DVB-S2 standards in four modulation types including (QPSK, 8/16/32 APSK). Further, this SoI combined by three well-known jamming signals namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI), and Chirp Interference (CI). This dataset includes 300 samples per modulation type for each type of signal. Therefore, totally there are 4800 samples in the dataset and each sample is a vector of size 1 by 32488 (8ms) at sample frequency 40 Hz. Also, AWGN power is -140 dBm which is approximately equal to SNR=9 dB.</p> <p>More importantly, SoI is modulated and processed by GNU radio and transmitted using a Universal Software Radio Peripheral (USRP-N210). In GNU radio, the modulation type and amplitude of the transmitted signal can be easily adjusted. A SatCom Emulator (RTLogic T400) is used for modeling a real-time communication channel. The programmatic control of the channel simulator is facilitated over an Ethernet connection using a control protocol or optional plugin to STK software. The Channel Simulator produces IF/RF signals with extracting signal characteristics for any scenario. The Kratos STK plugin provides real-time, phase-continuous control of the channel simulator when playing STK scenarios. Further, the generated jamming signals (CWI, MCWI, and CI) are transmitted using a NanoBee modem and combined to SoI by a combiner. Finally, the combined signal is received by a MegaBee modem. </p> <p>Notably, in this dataset, each jammer indicates a combination of SoI with that jammer, as an instance "CWI_16APSK " refers to SoI (16APSK)+CWI.</p> <p> </p>
Reproduction package for the paper "The variable radio counterpart of Swift J1858.6-0814"
<p>This is a basic reproduction package for the paper "The variable radio counterpart of Swift J1858.6-0814" by J. van den Eijnden et al. (2020). It aims to provide the data products underlying the figures in the paper, report where the analyzed observations can be accessed, and list the software used to perform the analysis. </p> <p>An open access version of the paper can be found at <a href="https://arxiv.org/abs/2006.06425">https://arxiv.org/abs/2006.06425</a>. </p>
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
<p>The WIDEFT data corpus has been created to provide bursts from wireless devices in the spectrum of Bluetooth, WiFi, and other RF signals to further research of the acquisition and usage of wireless device fingerprints. WIDEFT was developed through the efforts of the Physical Science Laboratories (PSL) at New Mexico State University (NMSU). Data collection was recorded at PSL and at NMSU main campus and cataloged, maintained, and prepared for release at PSL.</p> <p>Please cite the following article:</p> <p>A. Bucker Siddik, D. Drake, T. Wilkinson, P. L. De Leon, S. Sandoval, and M. Campos, “WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research,” <em>IEEE Int. Symp. Technol. Homel. Secur. (HST)</em>, 2021.</p>
Reproduction package for the paper "Mapping the spectral index of Cas A: evidence for flattening from radio to infrared"
<p>This is a basic reproduction package for the paper "Mapping the spectral index of Cas A: evidence for flattening from radio to infrared" by V. Domček et al. (2021). It provides raw, intermediate and final data sets, including figures and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data. </p> <p>An open access version of the paper can be found at https://arxiv.org/abs/2005.12677</p>
Properties of flat-spectrum radio-loud Narrow-Line Seyfert 1 Galaxies
<p>Numerical tables of the Spectral Energy Distributions (SEDs) of Figs. 8-13 of the paper http://arxiv.org/abs/1409.3716v2</p>
Radio variability in the Phoenix Deep Survey at 1.4 GHz
<p>This repository contains the final images that were used in the production of the paper titled: " Radio variability in the Phoenix Deep Survey at 1.4 GHz"</p> <p>Paper abstract:</p> <p>We use archival data from the Phoenix Deep Survey to investigate the variable radio source population above 1 mJy beam-1 at 1.4 GHz. Given the similarity of this survey to other such surveys we take the opportunity to investigate the conflicting results which have appeared in the literature. Two previous surveys for variability conducted with the Very Large Array (VLA) achieved a sensitivity of 1 mJy beam<sup>-1</sup>. However, one survey found an areal density of radio variables on time-scales of decades that is a factor of ~4 times greater than a second survey which was conducted on time-scales of less than a few years. In the Phoenix deep field we measure the density of variable radio sources to be ρ = 0.98 deg<sup>-2</sup> on time-scales of 6 months to 8 yr. We make use of Wide-field Infrared Survey Explorer infrared cross-ids, and identify all variable sources as an active galactic nucleus of some description. We suggest that the discrepancy between previous VLA results is due to the different time-scales probed by each of the surveys, and that radio variability at 1.4 GHz is greatest on time-scales of 2-5 yr.</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.