Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
814
datasets available to search
ShareScore release 0.9.0
Dataset results
814 results for “radio”
Figs. 44–50 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 44–50. Habitus images of Selenophorus species, dorsal aspect. 44) S. blanchardi; 45) S. pedicularius; 46) S. planipennis; 47) S. aeneopiceus; 48) S. breviusculus; 49) S. fatuus; 50) S. parumpunctatus.
Figs. 37–43 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 37–43. Habitus images and genitalia illustrations of Selenophorus species. 37) S. aequinoctialis, dorsal aspect; 38) S. palliatus, dorsal aspect; 39) S. sinuaticollis, dorsal aspect; 40–43) S. rileyi, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
Figs. 29–36 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 29–36. Habitus images and genitalia illustrations of Selenophorus species. 29) S. chaparralus, dorsal aspect; 30) S. opalinus, dorsal aspect; 31) S. fabricii, dorsal aspect; 32) S. trepidus, dorsal aspect; 33–36) S. undatus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
Figs. 15–18 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 15–18. Habitus images of Selenophorus species, dorsal aspect. 15) S. gagatinus; 16) S. concinnus; 17) S. semirufus; 18) S. schaefferi.
Figs. 8–14 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 8–14. Habitus images and genitalia illustrations of Selenophorus species. 8–11) S. pumilus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views; 12) S. seriatoporus, dorsal aspect; 13) S. discopunctatus, dorsal aspect; 14) S. fossulatus, dorsal aspect.
Figs. 1–7 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 1–7. Habitus images and genitalia illustrations of Selenophorus species. 1) S. contractus, dorsal aspect; 2) S. ellipticus, dorsal aspect; 3) S. granarius, dorsal aspect; 4–7) S. nonellipticus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
CommRad RF: A dataset of communication radio signals for detection, identification and classification
<p>In this age of information, data is the most valuable commodity. In today's world it is easier to acquire millions of standard/known and even non- standard/unknown RF signals. Perhaps, having such huge amount of data can help people to develop novel deep learning techniques. This dataset focuses on communication radios baseband signals. It includes RF signals from multiple radios using different frequencies, in different environments. Over 2700 RF signals from 27 different radios are included in the dataset. An RF receiver was used to capture the dataset, which automatically detects RF signals and records them until they disappear. In order to create a signal library, the receiver is connected to a laptop that pre-processes and stores RF signatures from different emitters.</p>
Amor Radio Diora
Amor Radio Diora Muzeum Miejskie Dzierżoniowa Diora 3D Source: Objaverse 1.0 / Sketchfab
Radio Tower
Radio tower perfect for background environment scenes. Great realistic details from the distance. Source: Objaverse 1.0 / Sketchfab
Entrevista a Pedro R. Moya Maleno: Congreso «Las fiestas de Cruces y Mayos en el siglo XXI. Tradición, pervivencia y adaptación» 27-28 de abril de 2024 (OndaCero Radio, 13/03/2024)
<p>Entrevista realizada a Pedro R. Moya Maleno con motivo del del Congreso «Las fiestas de Cruces y Mayos en el siglo XXI. Tradición, pervivencia y adaptación» 27-28 de abril de 2024, como Director Científico del Centro de Estudios del Campo de Montiel y miembro del Comité Científico del evento. Realizada en OndaCero Radio (desconexión Valdepeñas), el 13/03/2024. </p> <p>Noticia y audios orginales en: www.ondacero.es/emisoras/castilla-la-mancha/valdepenas/audios-podcast/mas-de-uno/entrevistas/centro-estudios-campo-montiel-prepara-congreso-fiestas-cruces-mayos_2024031365f1b182d3310300014aeed1.html</p> <p>Con motivo del 25 aniversario de la declaración de la Fiesta de Cruces y Mayos de Villanueva de los Infantes (Ciudad Real, España) Bien de Interés Turístico Regional por la Junta de Comunidades de Castilla-La Mancha, el Centro de Estudios del Campo de Montiel (CECM) con la colaboración del M.I. Ayuntamiento y la Diputación de Ciudad Real, organizan dicho congreso para abordar de forma multidisciplinar el estudio de los orígenes, manifestaciones y situación en la que se encuentran dos de las fiestas más significativas dentro del calendario festivo tradicional, los Mayos y las Cruces.</p>
CommRad RF: A dataset of communication radio signals for detection, identification and classification
<div> <div> <div> <div> <p>In today’s world, data is one of the most valuable resources. It’s now easier than ever to collect millions of radio frequency (RF) signals, both well-known and unusual. With such a large amount of data, researchers can create advanced deep learning techniques. This dataset focuses on baseband signals from communication radios. It includes RF signals captured from 27 different radios, using various frequencies and recorded in different environments. Altogether, there are over 2,700 signals in this collection. The signals were captured using an RF receiver that automatically detects and records signals until they disappear. To organize these signals into a library, the receiver was connected to a laptop, which processed and saved the unique RF fingerprints from different devices.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Radio Galaxy Zoo Data Release 1
<p>We present the first data release of Radio Galaxy Zoo, an online citizen science project that enlists the help of citizen scientists to cross-match extended radio sources from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) surveys, often with complex structure, to host galaxies in $3.6\,\mu$m infrared images from the {\em{Wide-field Infrared Survey Explorer}} (WISE) and the {\em{Spitzer Space Telescope}}. This first data release consists of 100,185 classifications for 98,559 radio sources from the FIRST survey and 582 radio sources from the ATLAS survey. As such, there are radio sources for which more than one classification is listed. We include two tables for each of the FIRST and ATLAS surveys: 1) the identification of all components making up each radio source; and 2) the cross-matched host galaxies. These classifications have an average reliability of 0.83 based on the weighted consensus levels of our citizen scientists. Please refer to the RGZ Data Release 1 paper by Wong et al 2024 for more details.</p>
Dataset for "Extended scenarios for solar radio emissions with downshifted electron beam plasma excitations"
<p>Input data for the PIC simulation</p>
Supporting material for Petricca et al. (2024), "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets
<p>This archive contains the supplementary material for the paper "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets</p> <p>Content of the dataset:</p> <ol> <li>Synthetic gravity fields for Ariel and Titania generated in the study</li> <li>SPICE kernels of the trajectory of the Uranus Orbiter and Probe designed at JPL</li> </ol> <p> </p> <p>---------------------------------------------------------------</p> <p>Synthetic gravity fields</p> <p>---------------------------------------------------------------</p> <p>The gravity fields are generated following the procedures described in Section 2.1.2 of the main paper. The hydrosphere thickness is assumed to be 190 km and 220 km for Ariel and Titania, respectively. The ocean density is fixed at 1050 kg/m^3. The syntethic topography is generated with pyshtools (Wieczorek and Meschede, 2018). The label of the file indicates the amplitude of the topography of each interface (ice shell or ocean floor) and the maximum degree of the spherical harmonics expansion. The files are formatted according to the Spherical Harmonics ASCII Data Record (SHADR) standard.</p> <p>The header of each file contains: reference radius (km), GM (km^3 / s^2), uncertatinty in the GM (not used and set to zero), maximum degree <em>l </em>of the<em> </em>expansion, maximum order<em> m </em>of the expansion, normalization (0 for unnormalized, 1 for 4pi normalization), reference latitude, reference longitude</p> <p>The columns contain: degree <em>l</em>, order <em>m</em>, coefficient C_<em>lm</em>, coefficient S_<em>lm</em></p> <p>---------------------------------------------------------------</p> <p>UOP trajectories</p> <p>---------------------------------------------------------------</p> <p>The reference positions and velocities of the UOP were generated by Damon Landau (JPL) as part of an internal study at JPL. These initial positions and velocities were numerically integrated by Flavio Petricca (JPL) using the dynamical models described in the main paper. For this reason, the trajectories only cover +- 8 hours from closest approach with each moon and not the entire tour.</p> <p>The ID of the spacecraft is set to -999. The simple text kernel provided here (id_name_map.txt) can be loaded in the kernel pool to associate the ID code with the SPICE names 'URANUS ORBITER PROBE' and 'UOP' for a more explicit and user-friendly access to the trajectories.</p>
Annotated data of simultaneous broadband radio and optical emission of meteor trains imaged by LOFAR / AARTFAAC and CAMS
<p>This data set contains simultaneous 30 - 60 MHz LOFAR / AARTFAAC12 radio observations and CAMS low-light video observations of +4 to -10 magnitude meteors at the peak of the Perseid meteor shower on August 12/13, 2020. 204 meteor trains were imaged in both the radio and optical domain.</p>
Burst timescales and luminosities as links between young pulsars and fast radio bursts Dataset
<p>The dataset to reproduce the results and plots in Nimmo et al. 2021b (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210511446N/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210511446N/abstract</a>). The scripts to produce the plots can be found here: <a href="https://github.com/KenzieNimmo/FRB20200120E_timescales">https://github.com/KenzieNimmo/FRB20200120E_timescales</a></p> <p>The software used to make the data products are:</p> <ul> <li>SFXC (Keimpema et al. 2015;<a href="https://github.com/aardk/sfxc/"> https://github.com/aardk/sfxc/</a>) </li> <li>DSPSR (van Straten & Bailes 2011; <a href="http://dspsr.sourceforge.net/">http://dspsr.sourceforge.net/</a>)</li> <li>PSRCHIVE (Hotan et al. 2004; <a href="http://psrchive.sourceforge.net">http://psrchive.sourceforge.net</a>)</li> <li>numpy (Harris et al. 2020; <a href="https://numpy.org/install/">https://numpy.org/install/</a>)</li> </ul> <p>A complete list of the data products:</p> <ul> <li>8us/125kHz full polarisation archive files (dspsr) for all 5 bursts presented in the work (coherently and incoherently dedispersed to 87.75pc/cc). Created from filterbank data made using SFXC. Use load_file.load_archive from the above github link to load data into python as a numpy array. <ul> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr158a_corr_no0017_8us_125kHz_FullPol_FullDedisp_dm87.75.ar.calib</li> </ul> </li> <li>31.25ns/16MHz Stokes I filterbank data for B2, B3 and B4. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using SFXC. <ul> <li>pr141a_corr_no0069_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor2_Ef.fil</li> <li>pr143a_corr_no0015_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> <li>pr143a_corr_no0057_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> </ul> </li> <li>1us/500kHz Stokes I filterbank data for B2, B3 and B4. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using SFXC. <ul> <li>pr141a_corr_no0069_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor2_Ef.fil</li> <li>pr143a_corr_no0015_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> <li>pr143a_corr_no0057_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> </ul> </li> <li>125ns/4MHz full pol archive file of burst B3. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using dspsr from a filterbank file made by SFXC. <ul> <li>pr143a_corr_no0015_125ns_4000kHz_FullPol_FullDedisp_dm87.7527_SFXC.cor_Ef.calib</li> </ul> </li> </ul> <p>We also provide a number of numpy files containing analysis products for ease of reproducing the figures. </p> <ul> <li>2D autocorrelation functions of the dynamic spectra of all 5 bursts. Additionally we give the 2D Gaussian fits to the ACFs and the Lorentzian fits to the frequency ACFs (for measuring the scintillation bandwidth) <ul> <li>ACF_b*_8us_f8.npy</li> <li>fitACF_b*_8us_f8.npy</li> </ul> </li> <li>Peak signal-to-noise ratio (S/N) of burst B3 profile at 1us resolution as a function of dispersion measure (DM) with a Gaussian fit to the result <ul> <li>pr143a_corr_no0015_500ns_1000kHz_StokesI_FullDedisp_dm87.7527_DS.npy (500ns dynamic spectrum)</li> <li>DM_vs_peakSN_sfxc_IF1-11.npy</li> <li>DM_vs_peakSN_sfxc_IF1-11_fit.npy</li> </ul> </li> <li>Power spectrum (PS) of 31.25ns profile of B2, B3 and B4 with the power law (PL) fits and power law+lorentzian (PL_lor) fit for B3 <ul> <li>PS_B2_IF4678_31.25ns.npy</li> <li>PS_B3_IF3568_31.25ns.f8.npy (downsampled by a factor of 8)</li> <li>PS_B3_IF3568_31.25ns.npy</li> <li>PS_B4_IF67910_31.25ns.npy</li> <li>PL_fit_B2.npy</li> <li>PL_fit_B3.npy</li> <li>PL_fit_B4.npy</li> <li>PL_lor_fit_B3.npy</li> </ul> </li> <li>Faraday spectra of B1, B2, B3, B4 <ul> <li>no0015_8us_125kHz_faradayspec.npy</li> <li>no0057_8us_125kHz_faradayspec.npy</li> <li>no0069_8us_125kHz_faradayspec.npy</li> <li>no0069_8us_125kHz_faradayspec_2.npy</li> </ul> </li> <li>Stokes Q and U spectra for B1, B2, B3, B4 and the corresponding joint QU fits <ul> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib_QUdata.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib_QUfit.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> </ul> </li> </ul> <p> </p> <p> </p>
1864 radio variable galaxies with classifications for activity types.
<p>1864 radio variable galaxies with classifications for activity types.</p>
High time resolution search for prompt radio emission from the long GRB 210419A with the Murchison Widefield Array
<p>The time series of Stokes parameters in the PSRFITS format formed from the MWA data at the position of GRB 210419A.</p>
Measuring intraoperative surgical instrument use with radio-frequency identification
<p><strong>Objective:</strong> Surgical instrument oversupply drives cost, confusion, and workload in the operating room (OR). With an estimated 78-87% of instruments being unused, many health systems have recognized the need for supply refinement. By manually recording instrument use and tasking surgeons to review instrument trays, previous quality improvement initiatives have achieved an average 52% reduction in supply. While demonstrating the degree of instrument oversupply, previous methods for identifying required instruments are qualitative, expensive, lack scalability and sustainability, and are prone to human error. In this work, we aim to develop and evaluate an automated system for measuring surgical instrument use.</p> <p><strong>Materials and Methods:</strong> We present the first system to our knowledge that automates the collection of real-time instrument use data with radio-frequency identification (RFID). Over 15 breast surgeries, ten carpometacarpal (CMC) arthroplasties, and four craniotomies, instrument use was tracked by both a trained observer manually recording instrument use and the RFID system.</p> <p><strong>Results:</strong> The average Cohen's Kappa agreement between the system and the observer was 0.81 (near perfect agreement), and the system enabled a supply reduction of 50.8% in breast and orthopedic surgery. Over 10 monitored breast surgeries and one CMC arthroplasty with reduced trays, no eliminated instruments were requested, and both trays continue to be used as the supplied standard. Setup time in breast surgery decreased from 23 minutes to 17 minutes with the reduced supply.</p> <p><strong>Conclusion:</strong> The RFID system presented herein achieves a novel data stream that enables accurate instrument supply optimization.</p>
CTLA-4 and PD-1 expressed in EMT6 mouse mammary carcinoma cell line acquired radio-resistance to gamma-ray irradiation.
<p>Supplementary File of manuscript entitled CTLA-4 and PD-1 expressed in EMT6 mouse mammary carcinoma cell line acquired radio-resistance to gamma-ray irradiation.</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.