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814 results for “radio”
Solar eclipse radio frequency measurements
<p>Measurements of the carrier frequency of the NIST radio station WWV on 10 MHz, as performed in north suburban Milwaukee, Wisconsin, during the solar eclipse of August 21, 2017. Details are in the file "readme.pdf".</p> <p>Steven Reyer, WA9VNJ, approx. Lat/Long = 43.218, -87.951. WAV file start time = 1400 UTC. Antenna is a DX Engineering RF-PRO-1B aimed north-south, receiver is a Yaesu FT-857D locked to a Trimble Thunderbolt GPS via an XRef-FT oscillator interface. I tuned the radio to 9999.00 kHz USB and listened for the resulting nominal 1000 Hz tone, which was measured by Spectrum Lab software, doing 512k-point FFTs, overlapping 75%, resulting in a measurement every 12 seconds. </p> <p> </p> <p> </p>
Wideband I/Q Recording at VU2PTT for 160m Ham Radio Band during August 21, 2017 Solar Eclipse in USA
<p>Recorded by Prasad Rajagopal, VU2PTT in Bangalore, India</p> <p>Location: Latitude 12°58'24.8"N, Longitude 77°39'15.7"E</p> <p>Grid Locator: MK82tx</p> <p>Recorded using Skimmer Server & CWSL File</p> <p>Playback: Each time stamped I/Q file is a 15 minute slice and can be played back with HDSDR software</p> <p>Antenna was a full size 1/4 wave vertical on 40m (7 MHz)</p> <p>Receiver was a QS1R SDR capable of simultaneously recording 7 bands @ 192 kHz bandwidth </p>
Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023
<p>Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023</p>
Sagittarius A lunar occultation measured by Dwingeloo Radio Telescope 2023-12-13
<p>This dataset contains raw spectra from the Lunar occultation of Sagittarius A on December 13, 2023. The data were obtained with the Dwingeloo Radio Telescope, which is operated by Stichting CAMRAS.</p> <p>Three bands are measured:</p> <ul> <li>1418MHz, 6MHz wide. The spectra show absorption and emission of the hydrogen line.<br> <ul> <li>Spectra with 2000 bins, integration time 0.2 seconds (corrected for bandpass).</li> <li>Total power with integration time 1 second.</li> </ul> </li> <li>1330MHz, 10MHz wide. <ul> <li>Spectra with 5000 bins, integration time 1 second.</li> <li>Total power with integration time 1 second.</li> </ul> </li> <li>415MHz, 4MHz wide. This data is severely affected by radio frequency interference.<br> <ul> <li>Spectra with 2000 bins, integration time 1 second.</li> <li>Total power with integration time 1 second.</li> </ul> </li> </ul> <p>The telescope was tracking Sagittarius A* during the occultation. The altitude ranged from 1 to 8 degrees above the horizon during the measurements. This is very low, leading to quite severe radio frequency interference. Partially, these are caused by LTE masts transmitting at 1456 MHz, for which the receiving system is insufficiently shielded. All frequencies are topocentric, i.e. no LSR-correction has been applied.</p> <p>Files are in the ECSV format, which can be read with Astropy, or with any other program that can read CSV-data (such as Microsoft Excel). Apart from the spectra, also telescope pointing information and exact times are stored in every row.</p>
Dataset for: Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio
<p>Repository of data plotted in figures for the journal publication "Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio" (doi: 10.1364/OE.500077 ).</p> <p>Please see metadata file for details on individual data files.</p>
Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset
<p>In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>, but in a fast manner.</p> <p><a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#general-dataset-structure">General Dataset Structure</a></p> <p>A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>. This implementation was carried out in the MATLAB software <a href="https://github.com/johanflorez98/WLANs-optimal-designs-methodology">WLANs-optimal-designs-methodology</a>.</p> <p>Thus, this research provides a dataset that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems.</p> <p>We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points.</p> <p>The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.</p> <p>The pictures have a depth of 8 bits and size of <em>256pixels X 256pixels</em> equivalents to indoor constructions of <em>20 X 20</em> m<sup>2</sup>. These ones make reference to offices's spaces at general or classroom.</p> <p><a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#obtained-models">Obtained Models</a></p> <p>To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used.</p> <p>To manipulate the codes better, click here in the <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset">repository</a>.</p>
Dataset for Evaluating habitat-specific interference in automated radio telemetry systems: implications for animal movement studies
<h1>Abstract </h1> <p>Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of one week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats.</p> <p> </p>
Evaluation of Upper Tropospheric Geopotential Height Anomalies over the Tropical and Subtropical Oceans in CMIP6 Models Using GNSS Radio Occultation Observations
<p>The set-up of CESM2-CAM6 sensitivity experiments for winter season (Dec-Jan-Feb: DJF), with prognostic falling ice radiative effects on (SON) and off (NOS), is an updated two-moment stratiform cloud scheme (MG2, Gettelman & Morrison, 2015) in the CESM2 atmospheric component of CAM6. CESM2-CAM6 participated in CMIP6. Both the NOS and SON simulations were configured following the same approach as the CMIP6 "historical" run spanning from 1980 to 2014.</p> <p> </p> <p>The data are:</p> <p> </p> <p>TS: skin temperature (K)</p> <p>TAUX: zonal surface wind stress</p> <p>TAUY: meridinal surface wind stress</p> <p>DTCOND: moist condensation heating rate</p> <p>QRL: long wave heating rate</p> <p>OMEGA: vertical motion</p> <p>Z3: geopotential height</p> <p> </p>
Global Traditional Radio Advertising Market 2024 to 2033
<p><a href="https://www.custommarketinsights.com/report/traditional-radio-advertising-market/" target="_blank" rel="noopener">Traditional Radio Advertising Market Size</a>, Trends and Insights By Type (Terrestrial Radio Broadcast Advertising, Satellite Radio Advertising), By Enterprise Size (Large Enterprises, Small and Medium Enterprises (SMEs)), By Industry Vertical (Automotive, Financial Services, Media and Entertainment, Fast-Moving Consumer Goods (FMCG), Retail, Real Estate, Education, Other Industry Verticals), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</p> <p><strong>Reports Description</strong></p> <p>As per the current market research conducted by the CMI Team, the global <a href="https://www.custommarketinsights.com/report/traditional-radio-advertising-market/" target="_blank" rel="noopener"><strong>Traditional Radio Advertising Market</strong></a> is expected to record a CAGR of <strong>2.3%</strong> from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD <strong>28.6 Billion</strong>. By 2033, the valuation is anticipated to reach USD <strong>35.1 Billion</strong><strong>.</strong></p> <p>Traditional radio advertising will probably be cautiously positive because that particular industry must adapt to consumer behaviors and technological changes, which digital platforms must also offset. For local news and entertainment, traditional radio still attracts a considerable audience. It is worthwhile since people depend more and more on the radio when there are crises, whether in the case of the COVID-19 pandemic.</p> <p>Furthermore, with slow growth in corporate advertising expenditures over time, radio stations will continue to innovate and implement a new digital strategy that will spur listenership and engagement.</p> <p>Segments such as satellite radio advertisements are also expected to grow steadily, mainly driven by increased technological advancement and higher demands for diversified content.</p> <p>This article talks about how small and medium-sized businesses can help growth by using low-cost advertising options to reach local customers and how traditional radio advertising and new ideas come together in places like the Asia-Pacific region, which has the most growth potential due to more people living in cities, the use of digital technologies, and new ideas. In general, the marketplace is ready to be leveraged with strengths to adjust to an ever-changing landscape of advertisements.</p> <p>DOWNLOAD FREE SAMPLE Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=59003" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=59003</a></p>
'Profiles in Music: Gwendolyn Koldofsky'—Radio Interview with William Triplett
<p>Previously unpublished recording of an extended radio interview from spring of 1989, titled 'Profiles in Music: Gwendolyn Koldofsky'. It was likely broadcast at a later date by the South Carolina Radio Educational Network, under the direction of William D. Hay as part of a broadcast series titled Stage One. The initial broadcast was not initially finished or broadcast due to the untimely death of host William Triplett. </p> <p>This recording of that later broadcast was discovered in the personal archive of Koldofsky student Russell Miller, and was provided by his husband Todd Graber after Miller's passing, although how Miller acquired it has not been determined nor have other rights-holders been located. No other record or further information have been found in the archives of any of Koldofsky's institutes, including KUSC, USC or Santa Barbara, and communication with SCETV was likewise not conclusive. </p> <p>Host: William Triplett</p> <p>Featuring: Gwendolyn Koldofsky as well as students Mark Trawka (Piano), Pauline Burke (Mezzo-Soprano), Hector Vásquez (Baritone)</p> <p>This recording was digitized for this publication, created in connection with the monograph 'Accompaniment in America: Contextualizing Collaborative Piano' by VanderHart et al. (Routledge: 2025).</p> <p> </p>
Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster (Public Data Release)
<p>This Zenodo dataset contains the data for radio bursts B1-B9 from FRB 20200120E, as described in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) (see: https://doi.org/10.1038/s41550-024-02386-6).</p> <p>The following data products are included:</p> <ul> <li>Channelized total intensity (Stokes I) data containing radio bursts B1-B5 from FRB 20200120E, recorded using the Effelsberg radio telescope during Pinpointing Repeating CHIME Sources with the EVN (PRECISE) VLBI observations. These data have a time resolution of 8 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b1_8us_burst_data.npy</li> <li>frb20200120e_b2_8us_burst_data.npy</li> <li>frb20200120e_b3_8us_burst_data.npy</li> <li>frb20200120e_b4_8us_burst_data.npy</li> <li>frb20200120e_b5_8us_burst_data.npy</li> </ul> </li> <li>Channelized total intensity (Stokes I) data containing radio bursts B6-B9 from FRB 20200120E, recorded using the Effelsberg radio telescope. These data have a time resolution of 64 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b6_64us_burst_data.npz</li> <li>frb20200120e_b7_64us_burst_data.npz</li> <li>frb20200120e_b8_64us_burst_data.npz</li> <li>frb20200120e_b9_64us_burst_data.npz</li> </ul> </li> <li>Frequency-summed total intensity (Stokes I) burst profiles of radio burst B4. The frequency range and time resolution of the data are listed below. These data were used in Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).<br> <ul> <li>frb20200120e_b4_8us_1254-1510mhz_burst_profile.npz; (frequency range, time resolution) = (1254-1510 MHz, 8 μs)</li> <li>frb20200120e_b4_1us_1302-1478mhz_burst_profile.npy; (frequency range, time resolution) = (1302-1478 MHz, 1 μs)</li> <li>frb20200120e_b4_31.25ns_1398-1414mhz_burst_profile.npy; (frequency range, time resolution) = (1398-1414 MHz, 31.25 ns)</li> </ul> </li> </ul> <p>We also provide the following Python code containing functions that can be used to load and plot the radio data. The plots generated by this code are similar to those shown in Figure 1 and Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).</p> <ul> <li>plot_frb20200120e_radio_data_pearlman+2024_nature_astronomy.py</li> </ul> <p>The X-ray data (from <em>NICER</em>, <em>XMM-Newton</em>, <em>Chandra</em>, and <em>NuSTAR</em>) used in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) are publicly available and can be accessed through NASA's High Energy Astrophysics Science Archive Research Center (HEASARC) archive.</p> <p>If the data or Python code included in this Zenodo repository are used, please include the following two citations in your work:</p> <ol> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster. <em>Nature Astronomy</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.1038/s41550-024-02386-6</a></li> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster (public data release). <em>Zenodo</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.5281/zenodo.13359005</a></li> </ol> <p>If you have questions about the contents of this Zenodo repository, please contact the lead author: Dr. Aaron B. Pearlman (<a href="mailto:aaron.b.pearlman@physics.mcgill.ca">aaron.b.pearlman@physics.mcgill.ca</a>)</p>
Solar and meteorological data collected from the Radio Telescope Bras D'Eau station (Mauritius) by the ENERGY-Lab at the University of La Reunion between November 2015 and March 2023
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Radio Science observations of Mars Express and Tianwen-1 spacecraft from the 2021 Maritan Solar Conjunction
<p>Phase scintillation dataset from the 2021 Martian solar conjunction is contained in the DATA.zip file. The Read Me pdf file contiants information useful to understanding the file naming conventions and their contents.</p>
Time-lapse of AARTFAAC detections of radio meteors in Perseids 2020
<p>LOFAR AARTFAAC images (integrated over all observing bands) during the Perseids meteor shower in the night 2020 August 12 --13. The large-scale diffuse emission is the Galactic plane. The brightest radio sources Cassiopeia~A and Cygnus~A have been subtracted, sometimes leaving some artefacts. In red, trajectories as computed from the CAMS BeNeLux optical observations are overlaid.</p> <p>Observing frequencies were in the range 30 to 60 MHz.</p>
Reproduction package for the paper "Constraining a neutron star merger origin for localized fast radio bursts"
<p>This is a reproduction package for the paper <a href="https://academic.oup.com/mnras/article/497/3/3131/5875920">"Constraining a neutron star merger origin for localized fast radio bursts"</a> by Gourdji et al. (2020) and published in MNRAS. This package provides a Jupyter notebook and the necessary information to reproduce the figures and main results of this paper.</p>
Database Search Results for Resource Management in Converged Optical and MillimeterWave Radio Networks Review
<p><strong>Paper Selection Procedure</strong></p> <p>In order to conduct the survey titled "Resource Management in Converged Optical and MillimeterWave Radio Networks: A Review", the authors reviewed works published in the literature with a focus on those that cover most of the identified optimization requirements for converged optical fronthaul and mmWave wireless access networks. The research method is based on the research steps given in "The PRISMA 2020 statement"[1]. The selection procedure is also illustrated in "Database Search Flow Chart.png" figure.</p> <p>The first step was the selection of the papers. We completed this step by making database searches in the ACM, Elsevier (Science Direct), IEEE, IET, MDPI, Optical Society (OSA), Springer, Taylor & Francis, and Wiley online library databases with keywords ``resource allocation AND converged mmWave fiber wireless (FiWi)'', ``resource management AND converged mmWave fiber wireless (FiWi)'', and ``resource allocation AND converged fiber wireless (FiWi)''. The searches in all databases were completed in May 2021. The resulting collection was screened, to exclude non-scientific texts, book chapters, out of context papers, and survey papers. The remaining 189 papers found in our database search are provided in the excel file titled "FiWi Resource Allocation Database Search.xlsx".</p> <p>Among these papers, our selection criteria was created to present the works that are most relevant to the target network architecture, providing novel implementation solutions to the requirements of the optimization objective. The criteria selected for our eligibility step can be summarized as follows:</p> <ul> <li>The study provided a sound research approach and published after a scholarly review process;</li> <li>The study had a resource management optimization objective for mmWave networks;</li> <li>The study explained the system model and proposed a well-defined optimization algorithm;</li> <li>The effects of the algorithm on a performance metric was reported and the different aspects of the performance metric was analyzed with different evaluation criteria.</li> </ul> <p>This review is limited to the focus scope on converged optical and mmWave radio network solutions and by the databases taken into consideration. The prioritization of the works that address a well-defined optimization algorithm led to the omission of relevant papers. We did not include works that do not clearly define a resource management objective, i.e., a study that focuses on the the hardware implementation aspects of optical and mmWave radio networks with no resource management perspective. We manually excluded all studies that do not match these criteria with a simple scoring system, in which a point is deducted from an eligible paper for each missing criterion. The initial screening process and the data collection steps were carried out by the first author and the final inclusion decision was made by all the reviewers for the studies with the highest scores. After this screening process, we identified 37 papers that focused on at least one of the resource management objectives of throughput maximization, delay minimization, energy-efficiency, and virtualized resource allocation. The papers that have joint objectives are classified under their main optimization focus of that paper. The list of the selected papers are provided in "FiWi Resource Allocation Papers Selected for Review.xlsx" file. Our target in this review is to understand the recent optimization techniques used in resource allocation for converged optical fronthaul and radio mmWave access network implementations, therefore we focused our search to the works completed in the last five years (between 2016 and 2021), and approximately 95% of the selected papers fit under this category.</p> <p><strong>Overview of the data collected from selected papers</strong></p> <p>In this section, we provide answers to the three following questions with the data collected from the eligible studies:</p> <ul> <li>Question 1: Which algorithms are used more often in performance optimization in converged mmWave networks?</li> <li>Question 2: Which performance metrics are determined to show that the optimization method achieves the objective?</li> <li>Question 3: Which criteria are used to evaluate the solution method?</li> </ul> <p>Regarding the first question, the figure titled "Distribution of Optimization Algorithms in Selected Papers" shows the distribution of the optimization algorithms used by the selected papers. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 (Distribution of Evaluation Criteria) and the evaluation criteria to test the performances of the selected papers are grouped in Table 2 (Distribution of Main Performance Metrics Depending on Optimization Objectives), which shows how many times each criterion is used together with how many of the resource management objectives use these criterion.</p> <p><strong>References: </strong></p> <p>[1] Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;Brennan, S.E.; Chou, R.; Glanville, J.; Grimshaw, J.M.; Hróbjartsson, A.; Lalu, M.M.; Li, T.; Loder, E.W.; Mayo-Wilson, E.;McDonald, S.; McGuinness, L.A.; Stewart, L.A.; Thomas, J.; Tricco, A.C.; Welch, V.A.; Whiting, P.; Moher, D. The PRISMA 2020statement: an updated guideline for reporting systematic reviews.Systematic Reviews2021,10. doi:10.1186/s13643-021-01626-4.</p>
Reproduction package for "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data"
<p>This is a basic reproduction package for the paper "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data" by Gourdji et al. (2021) published in MNRAS. It describes the software and settings used to obtain the final data products of the analysis. It also includes a Jupyter notebook and required data to reproduce the tables and figures of this paper.</p>
Reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif"
<p>This is a basic reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif".</p>
Search for merger ejecta emission in Short Gamma Ray Bursts from very late time radio observations
<p>Coalescence of inspiral binary neutron stars (BNS) system, giving rise to short Gamma Ray Bursts (GRBs), are one of the most probable candidates for Gravitational Waves (GWs). If the resultant product of the merger is a millisecond magnetar, a significant proportion of the rotational energy deposited to emerging ejecta that produce late time radio brightening from the interaction with the surrounding ambient medium. Detection of this late-time radio emission from short GRBs can have profound implications for understanding the physics of the progenitor. This study presents the deepest and an extensive search for radio emission at late times following a short GRB to date incorporating proper frequency regime, wider observation span and relativistic correction. Five short GRBs were observed with the Giant Meter Wave Radio Telescope (GMRT) at 1250, 610, and 325 MHz band $\sim$ 2 - 11 years since the burst to search for radio emission from the merger ejecta. The estimated upper limits at the burst location are used to constrain the parameters of the burst and its surrounding environment. The magnetar model, with appropriate modifications, constrains the number density of the ambient medium for these bursts to be between $10^{-4}$ - $10^{-2}$ $cm^{-3}$. Our analysis rules out a stable magnetar with an energy of $10^{53}$ erg for four out of the five GRBs in our sample.</p>
Where did the finch go? Insights from radio telemetry of the medium ground finch (Geospiza fortis)
<p><span>Movement patterns and habitat selection of animals have important implications for ecology and evolution. Darwin's finches are a classic model system for ecological and evolutionary studies, yet their spatial ecology remains poorly studied. We tagged and radio-tracked five (three females, two males) medium ground finches (<em>Geospiza fortis</em>) to examine the feasibility of telemetry for understanding their movement and habitat use. Based on 143 locations collected during a three-week period, we analysed, for the first time, home-range size and habitat selection patterns of finches at El Garrapatero, an arid coastal ecosystem on Santa Cruz Island (Galápagos). The average 95% home range and 50% core area for <em>G. fortis</em> in the breeding season were 20.54 ha ± 4.04 ha SE and 4.03 ha ± 1.11 ha SE, respectively. For most of the finches, their home range covered a diverse set of habitats. Three finches positively selected the dry-forest habitat, while the other habitats seemed to be either negatively selected or simply neglected by the finches. In addition, we noted a communal roosting behaviour in an area close to the ocean, where the vegetation is greener and denser than the more inland dry-forest vegetation. We show that telemetry on Darwin's finches provides valuable data to understand the movement ecology of the species. Based on our results, we propose a series of questions about the ecology and evolution of Darwin's finches that can be addressed using telemetry.</span></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.