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.
2,650
datasets available to search
ShareScore release 0.9.0
Dataset results
2,650 results for “waves”
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Data to generate the figures of: "Symmetry breaking of azimuthal waves: Slow-flow dynamics on the Bloch sphere"
<p>The folder contains the data and scripts to generate all the figures of the paper, with detailed instructions.</p> <p>No experimental data was used for this article.</p>
Data for: From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent
<p>Centroid data used for the analysis in Roos et al. Eco Lett.</p> <p>Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys).</p> <p>The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated.</p> <p>A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of <em>N</em> observed). The final dataset consisted of 3,751 observations.</p>
Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"
<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>
Destructive potential of planetary meteotsunami waves: ATAL ocean model results
<p> Based on the well-documented Hunga Tonga–Hunga Ha'apai volcano explosive eruption on 15 January 2022, we developed the "Atmospheric Tsunami Associated with Lamb waves" or ATAL ocean model and performed 12 realistic and process oriented numerical simulations to assess the sea-level hazards posed by planetary meteotsunami waves. Here, we provide the ATAL model results of maximum sea-levels during day 1 and day 2 after the eruption for:</p> <p>- the baseline simulation: trying to reproduce the event as realistically as possible</p> <p>- the 10 resonance simulations (_r_): trying to derive the speed of the Lamb waves which will generate the maximum resonance (i.e., Proudman resonance) in the ocean basins by dividing the baseline speed by r = 1.25, 1.40, 1.50, 1.60, 1.65, 1.75, 2.00, 3.00, 4.00, 5.00. The full Proudman resonance was obtained for r = 1.50</p> <p>- the amplification simulation (_amp_): trying to match the Proudman resonance amplification by multiplying by 10 the Lamb waves amplitudes</p>
Matlab tools to solve the viscous Taylor Goldstein equation for both instabilities and waves
<p><strong>Matlab tools to solve the viscous Taylor Goldstein equation for both instabilities and waves</strong><br> Fourier-Galerkin method is faster and more accurate than the previous finite-difference version.<br> <strong>File list:</strong><br> wave_analysis_FG.m : code to solve demonstration problem (internal gravity waves on the Columbia River plume; data courtesy J. Nash)<br> nash_data.txt : observational data for demonstration problem<br> vTG_FG.m : the main subroutine<br> BaryL.m : auxiliary subroutine<br> vTG_FGprep.m : auxiliary subroutine (call before vTG_FG)<br> Lian_Smyth_Liu20.pdf : Paper to reference for description and testing of code.<br> The underlying theory is described in<em> Instability in Geophysical Flows,</em> by W.D. Smyth and J.C. Carpenter, Cambridge University Press: Available from <a href="https://www.amazon.com/Instability-Geophysical-Flows-William-Smyth-dp-1108703011/dp/1108703011/ref=mt_other?_encoding=UTF8&me=&qid=1573683499">Amazon</a> and others.</p>
Dataset for Surface waves prediction based on acoustic backscattering
<p>Underwater acoustic measurements dataset is represented by three types of files “.raw", “.mat", ".dat" as follows:<br> * “.raw" format also represented by three types of data.<br> - “...search.raw" files contain complex envelop from all hydrophones calculated at four emitted frequencies<br> - “...chan.raw" files is a signal in a wide band from one of the hydrophones - for control and noise analysis.<br> - the largest files are the raw wideband signal from all hydrophones. One such file was saved per eight-hour sound emission cycle.<br> * Spectrogram files are saved in MATLAB format “.mat” v7 . Phasing of the antenna array (all-round view) and calculation of window spectra near each emitted pulse was carried out.<br> * ".dat" files contain features of the average spectrum of the backscattered signal.<br> * Direct measurements of surface wave characteristics, made by a Datawell DWR-G4 wave-rider buoy, accompanied the acoustic measurements. This data is included too.</p> <p>In this archive, we upload all available files of the "dat" and "mat" type and a limited number of "raw" files. You may unpack all ".tar.gz" files into one folder, preserving the directory tree, existing in the archives.</p> <p>Users should refer to the included “.pdf” file for the data format description and to a published preprint for a description of the experimental conditions and instrumentation characteristics. See [arXiv:arXiv:2204.10153] via <a href="https://arxiv.org/abs/2204.10153">https://arxiv.org/abs/2204.10153</a> (Also check when the link is updated to the journal paper) </p> <p>The authors are grateful to their colleges, who helped during the expedition. Data acquisition would be impossible without their contribution. This research was supported by the Russian Science Foundation, grant number 20-77-10081 (the expedition and motivation for study) and the State Contract with the Ministry of Education and Science of the Russian Federation, grant number 0030-2021-0017 (the instruments for underwater acoustic measurements).</p>
Data from "Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography"
<p>Data presented in the Science Advances manuscript "<em>Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography</em>" by Yushchenko M., Sarracanie M., Salameh N.</p> <p>See further details in <em>Description.txt.</em></p> <p>The 3D wave datasets acquired in humans at 0.1 T can be used for elastogram reconstruction with appropriate methods.<br> <br> </p>
Memory reactivation in slow wave sleep enhances relational learning
<p>EEG raw files in Brain Product format (3 files per participant). Sleep EEG recordings from healthy volunters under TMR stimulation. </p> <p>Two conditions: up-going slow oscillation stimulation / down-going oscillation stimulations</p> <p>Two type of sounds: experimental (they hear them before) and control (completely new)</p> <p>Triggers: 100-112 up experimental sounds / >112 up control sounds / 1-12 down experimetal sounds / 12-100 down control</p> <p>Please cite:</p> <p>"Memory reactivation in slow wave sleep enhances relational learning" ( https://doi.org/10.1101/2022.03.29.486197 )</p> <p> </p>
Temperature-dependent Lamb wave signals in highly anisotropic CFRP
<p>The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data.</p> <p>The following parameters were used during measurements:</p> <ul> <li>Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50];</li> <li>Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3];</li> <li>Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250];</li> <li>Number of averages: 50;</li> <li>Sampling frequency: 10 MHz.</li> </ul> <p>The following equipment was used in the experiment:</p> <ul> <li>Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C;</li> <li>National Instruments waveform generator PXIe-5413;</li> <li>Krohn-Hite voltage amplifier model 7500;</li> <li>Cedrat Technologies LWDS amplifier (used as a charge amplifier);</li> <li>National Instruments oscilloscope PXIe-5105.</li> </ul> <p>Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.</p>
Idealized wave data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from idealized solutions reported by Romero 2019</p> <p>Data are in Netcdf format and include metadata.</p> <p>Each file corresponds to a duration-limited solution with constant wind speed as indicated in the filename</p> <p>(e.g. 10mps means 10 m/s winds)</p> <p> </p>
Level 2 Winter data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from solutions of a nested configuration off the coast of California. These data are a subset of the solutions reported by Romero et al. 2020.</p> <p>These data are Level 2 of the nested configuration with a horizontal resolution of 270 m. Also included in this repository are the Level 2 and Level 3 grid files.</p> <p>Data are in Netcdf format including the metadata.</p> <p>The two simulation periods are December 2006 and Spring 2007.</p> <p>List of files:</p> <p>L2_Dec2006.nc -- December control solution only forced by winds</p> <p>L2_cew_Dec2006.nc -- December solution including forcing by both winds and currents.</p> <p>L2_grid.nc -- Level 2 grid</p> <p>L3_grid.nc -- Level 3 grid</p> <p> </p> <p> </p> <p> </p> <p> </p>
Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland – a multicentre cohort study
<p>For details, see publication</p>
Data from "Source characterization of the declared North Korean Nuclear Tests from regional distance coda wave spectral ratios"
<p>Results of the coda spectral ratio analysis presented in Delbridge et al. (2022).</p> <p>network_average_ratios.csv - a csv file which contains each of the network average ratios calculated from all channels and stations for each event pair and component.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Greece
<p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>contains the Greek questionnaire data set for Wave 1 of Work Pachage 7 of Catch-EyoU project (Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions; funded by the European Commission under the Horizon 2020 Programme; GA number: 649538; 2015-2018; http://www.catcheyou.eu/). This dataset contains the data underlying the publications presented in the References section (see below). </p> <p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>corresponds to the responses of participants to the questionnaire study for the purposes of Catch-EyoU project, WP7, Wave 1, collected in December 2016. These were entered and coded as either numeric or alphanumeric variables. Detailed information on variable names and labels is included in the portable file itself and it is also provided in the accompanying README file.</p>
Dataset: scattering of acoustic waves by vortices
<p>This dataset contains the data associated with the following paper: V. Clair & G. Gabard, Spectral broadening of acoustics waves by convected vortices, <em>Journal of Fluid Mechanics</em>, 841, pp. 50-80, 2018.</p>
Comparing recent PTA results on the nanohertz stochastic gravitational wave background - full noise and GWB parameter comparison plots
<p>A full collection of plots comparing the noise properties of individual pulsars and gravitational wave background parameters discussed in the companion paper <em>Comparing recent PTA results on the nanohertz stochastic gravitational wave background</em> (IPTA 2024).</p> <p><code>Section4_GWB_comparison.zip</code> supplements and expands section 4.1, "Comparing the published GWB measurements," of IPTA (2024). It contains parameter difference distributions for GWB model parameters. There are four different models included. The HD correlated powerlaw (PL) model make up the basis for Figure 2. Additionally, there are three comparisons not included in IPTA (2024). First, comparisons the common uncorrelated red noise (CURN) PL model are included. Finally, comparisons of two free spectral (FS) models (HD and CURN) are included. These comparisons fit the HD and CURN FS posteriors using the <code>ceffyl</code> software package, and then compare the parameters of the resulting powerlaw fits.</p> <p><code>Section5_Noise_comparison.zip</code> supplements section 5, "Comparing Pulsar Noice Properties," of IPTA (2024). It contains plots for 27 pulsars timed by more than one PTA collaboration, including the plots for PSR J1012+5307, which are presented in Figure 7. The plots include noise parameter posteriors, time domain GP realizations, TOA residuals, and TOA radio frequency.</p>
IODP Expedition 379 P-wave velocity bayonet (section)
P-wave velocity data were measured on undisturbed section halves using pairs of piezoelectric transducers mounted in bayonets that are inserted into soft sediment along the JRSO-defined y-axis and/or z-axis. Report includes P-wave velocity in y and/or z direction, bayonet separation, traveltime between transducers, and first arrival picks.
IODP Expedition 379 P-wave velocity caliper (section/discrete)
P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
IODP Expedition 379 P-wave velocity logger (whole round)
P-wave velocity data were measured on whole-round sections on the Whole-Round Multisensor Logger (WRMSL) using pairs of piezoelectric transducers mounted on a caliper system. Measurements may be affected by degassing of pore fluid and microfracturing during core recovery. Report includes P-wave velocity in x-y plane and distance and traveltime between transducers.
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.