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.
372
datasets available to search
ShareScore release 0.7.1
Dataset results
372 results for “Waveforms”
Teleseismic waveforms of OBS in Japan Basin
<p>The dataset includes the original teleseismic S waveforms and surface waveforms used in Receiver function and Rayleigh wave H/V ratio calculations, which are presented in the manuscript "Layered evolution of the oceanic lithosphere beneath the Japan Basin, the Sea of Japan" submitted to Journal of Geophysical Research - Solid Earth.</p> <p>Contact information: Sanxi Ai (aisanxi@cug.edu.cn) & Takeshi Akuhara (akuhara@eri.u-tokyo.ac.jp)</p>
Supplementary material "SASSIER22: Full-waveform tomography of the eastern Indonesian region that includes topography, bathymetry and the fluid ocean
<p><strong>Supplementary material “</strong><em>SASSIER22</em><strong>: Full-waveform tomography of the eastern Indonesian region that includes surface topography and the fluid ocean” by Wehner, D., Rawlinson, N., Greenfield, T., Daryono, Miller, M.S., Supendi, P., Lü, ChuanChuan and Widiyantoro, S., for publication in Geochemistry, Geophysics, Geosystems.</strong></p> <p>The material contains: </p> <p>Folder 1 <em>MODELS</em></p> <ul> <li>Absolute values of the starting model for the <em>SASSIER22</em> inversion and both final models (ocean layer — <em>SASSIER22</em> — and ocean load inversion) as NetCDFs and HDF5 files, with the former being readable by e.g. xarray and the latter suitable for viewing with ParaView and interaction with Salvus.</li> </ul> <p>Folder 2 <em>DATA</em></p> <ul> <li>The observed data (filtered and windowed) in MSEED format. Note that this data is provided for reproduction purposes only.</li> </ul> <p>The following additional information is provided in the Zenodo repository of Wehner et al. (2021), which can be found <a href="https://zenodo.org/record/5573139#.YxG4Bi8w1Hd%C2%A0">here</a>.</p> <ul> <li>A Jupyter Notebook (<em>MWE_data_processing.ipynb</em>) that demonstrates the processing steps for the observed waveforms of an event used in the <em>SASSY21</em> inversion (<em>20181229_033914</em>).</li> <li>A Jupyter Notebook (<em>embrace_the_sass.ipynb</em>) with a minimum working example on how to interact with the file formats mentioned above.</li> </ul>
Waveform data in SAC format
<p>This file contains the wavefrom data in SAC format for the P- and S-waves analyzed in '<strong>Deep geophysical anomalies beneath the Changbaishan Volcano</strong>' (Li et al.).</p> <p>The original data source is the Data Management Centre of China National Seismic Network at Institute of Geophysics (SEISDMC, doi:10.11998/SeisDmc/SN, http://www.seisdmc.ac.cn), also see Zheng et al., (2020), DOI: 10.1785/0120090257. </p>
Primary data of the receiver functions waveforms
Open the record for dataset details and reuse information.
Current Full-Waveform Inversion of the Return Stroke Channel based on Single-Station Electric Field Observations
<p>In manuscript entitled "Current Full-Waveform inversion of the Return Stroke Channel Based on Single-Station Electric Field Oberbations", the data of rocket-triggered flash o901 was obtained during the SHATLE was used. The data of our results are including in the Data- for- figrue-x.fig. These files can be opened later. The data supports the aforementioned manuscript and can bue used freely for scientific purposed with appropriate citation.</p>
Waveform data for Gravitational waveforms for high spin and high mass-ratio binary black holes: A synergistic use of numerical-relativity codes
<p>This dataset consists of binary black hole waveforms for three different configurations</p> <p>* q=3, chi1=chi2=0.9<br> * q=4, chi1=chi2=0.9<br> * q=5, chi1=chi2=0.9</p> <p>where q = m1/m2 is the ratio of the black hole masses, and chi1 and chi2 are the dimensionless spins of the black holes, oriented parallel to and in the direction of the orbital angular momentum.</p> <p>The waveforms are computed using the 3-dimensional numerical relativity simulation codes</p> <p> SpEC (SPectral Einstein Code -- https://www.black-holes.org/code/SpEC.html)<br> ET (Einstein Toolkit -- https://einsteintoolkit.org)</p> <p>SpEC is computationally efficient and accurate, and produces long waveforms, but in these cases, the simulations crash at the merger. The ET is less computationally efficient and accurate, but the simulations complete successfully. We therefore combine long SpEC inspirals with short ET mergers, blending the waveforms to produce hybrid waveforms.</p> <p>The waveform data directories are named as follows:</p> <p>q=3,chi1=chi2=0.9:<br> SpEC: q3_0.9_0.9_r200<br> ET: ceas_22_e5_2<br> Hybrid: q3_0.9_0.9_r200_hyb_ceas_22_e5_2</p> <p>q=4,chi1=chi2=0.9:<br> SpEC: q4_0.9_0.9_EccNew<br> ET: ceas_20_5_e7<br> Hybrid: q4_0.9_0.9_EccNew_hyb_ceas_20_5_e7</p> <p>q=5,chi1=chi2=0.9:<br> SpEC: q5.0_s0_0_0.9_s0_0_0.9_r200<br> ET: ceas_23_e5_2<br> Hybrid: q5.0_s0_0_0.9_s0_0_0.9_r200_hyb_ceas_23_e5_2</p> <p>The data is in the SXS simulation format (https://data.black-holes.org/waveforms/documentation.html), though only a subset of the SXS-format data is present.</p> <p>Also included is a Mathematica notebook, HybridizeWaveforms.nb, showing how the hybrid can be computed from the separate inspiral and merger waveforms using the open source SimulationTools code (https://simulationtools.org).</p> <p>See the paper</p> <p> I. Hinder, S. Ossokine, H. Pfeiffer and A. Buonanno -- Gravitational waveforms for high spin and high mass-ratio binary black holes: A synergistic use of numerical-relativity codes</p> <p>for more details.</p>
Processed Chinese waveforms
<p>This supporting information provides the processed waveforms data recorded in Chinese stations. The origin seismic data recorded at the vertical components of the dense regional broadband seismic stations in China were from the Data backup center of Institute of Geophysics, China Earthquake Administration (IGCEA). In back-projection procedure, we eliminate some noisy data by setting a coefficient threshold of 0.4.</p> <p>In the compressed package with format of ZIP, there are processed waveforms data of all M ≥ 7.0 shallow earthquakes that occurred in and around Japan from 2008 to 2016. The waveforms are named as *<strong>.</strong>????<strong>.</strong>V (‘*’ and ‘????’ represent name of network and station, respectively. ‘.V’ is postfix form of data.).</p>
sS waveforms
<p>This file contains the data for source-side sS splitting analysis. A total of 37 sS waveforms in horizontal components are included.</p>
waveform data of the Empirical Green's Functions (EGFs) and earthquakes for the manuscript "Unraveling the Mantle Dynamics in Central Asia with Full Waveform Inversion Tomography"
<p>The dataset uploaded contains the original ASDF files and the SAC files (with '_sac') converted from ASDF</p> <p>For the ASDF files:</p> <p>The ASDF data file could be read through python module pyasdf and obspy after decompressing. </p> <p>The earthquake information could be accessed by the following command</p> <p>import pyasdf</p> <p>ds=pyasdf.ASDFDataSet(ASDFfile, mode='r')</p> <p>event=ds.events[0].preferred_origin()</p> <p>print(event)</p> <p>the waveforms coul dbe accessed through</p> <p>stream=ds.waveforms[net.station].raw_recording</p>
A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms
<p>The dataset for "A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms" can be divided into training and validation sets at any desired ratio.</p> <p> </p> <p>The code has been published on GitHub: <a href="https://github.com/cqtian-kk/Lightning_Detection_Location">Lightning_Detection_Location</a> or <a href="https://zenodo.org/records/14048427">DOI 10.5281/zenodo.13350849</a></p>
sopP waveform data
<p>Each waveform trace contains the direct P. and depth phases of pP and sP as P coda named sopP.</p>
Data from: Characterizing and comparing the seasonality of influenza-like illnesses and invasive pneumococcal diseases using seasonal waveforms
The seasonalities of influenza-like illnesses (ILIs) and invasive pneumococcal diseases (IPDs) remain incompletely understood. Experimental evidence indicates that influenza-virus infection predisposes to pneumococcal disease, so that a correspondence in the seasonal patterns of ILIs and IPDs might exist at the population level. We developed a method to characterize seasonality by means of easily interpretable summary statistics of seasonal shape—or seasonal waveforms. Non-linear mixed-effects models were used to estimate those waveforms based on weekly case reports of ILIs and IPDs in five regions spanning continental France from July 2000 to June 2014. We found high variability of ILI seasonality, with marked fluctuations of peak amplitudes and peak times, but a more conserved epidemic duration. In contrast, IPD seasonality was best modeled by a markedly regular seasonal baseline, punctuated by two winter peaks in late December–early January and January–February. Comparing ILI and IPD seasonal waveforms, we found indication of a small, positive correlation. Direct models regressing IPDs on ILIs provided comparable results, even though they estimated moderately larger associations. The method proposed is broadly applicable to diseases with unambiguous seasonality and is well-suited to analyze spatially or temporally grouped data, which are common in epidemiology.
Convolutional-neural-network-based reflection full-waveform inversion
<p>The data is used by the paper "Convolutional-neural-network-based reflection 1 full-waveform inversion"</p>
FIGURE 5. a—Waveform showing the complete 1.5 in Platylomia kohimaensis n. sp.-a new cicada species (Hemiptera: Cicadidae) from the Naga Hills in the Eastern Himalayas
FIGURE 5. a—Waveform showing the complete 1.5 minute call recording, note the middle part where call is paused (inbox—section stretched to reveal waveform pattern), b—a portion of the recorded timbalisation stretched to revel the pattern of waveform in the form of regular echemes repeating at the rate of 8 echemes per second, c—Frequency spectrogram of the call showing frequency components (arrow showing main frequency components in each echeme).
Sample of seismic waveform data for rfmpy tutorial
<p>Sample of seismic waveform data for rfmpy tutorial (<a href="https://github.com/kemichai/rfmpy">https://github.com/kemichai/rfmpy</a>). Sub-set of seismic data from EASI seismic network that are cut around a number of different teleseismic events. The continuous full waveform data from EASI seismic network are available at: the European Integrated Data Archive EIDA; <a href="http://www.orfeus-eu.org/data/eida/">http://www.orfeus-eu.org/data/eida/</a> with the network code <strong>XT</strong>.</p>
Resonance at the front of lightning impulse voltage waveforms caused by the load capacitor
<p>On the front of impulse voltage waveform, the voltage usually increases up to thousands of kV within one microsecond, and the frequency range covers up to several megahertz. The capacitor in the high voltage arm is usually from 400 ‍pF to 1000‍ pF. We calculated the impedance of different capacitors in damped capacitive divider at the frequency range from 100‍ kHz to 1000‍ kHz. As Fig. 3 shows, the impedance ranges from 160‍ ohm to 4000‍ ohm.</p>
Training and test dataset for CNN-LSTM model for GW waveform extraction
<p>This repository contains training and test samples corresponding to the paper, 'Extraction of binary black hole gravitational wave signals from detector data using deep learning', Chatterjee et al., Phys. Rev. D <strong>104</strong>, 064046 (2021).</p>
Multi-scale full waveform inversion based on a convolutional neural network
<p>The research data from this paper are uploaded here and are available for download.</p>
Phenotypic screening using waveform analysis of synchronized calcium oscillations in primary cortical cultures
<p><span>At present, <em>in</em> <em>vitro</em> phenotypic screening methods are widely used for drug discovery. In the field of epilepsy research, measurements of neuronal activities have been utilized for predicting efficacy of anti-epileptic drugs (AEDs). Fluorescence measurements of calcium oscillations in neurons are commonly used for measurement of neuronal activities, and some anti-epileptic drugs have been evaluated using this assay technique. However, changes in waveforms were not quantified in previous reports. Here, we have developed a high-throughput screening system containing a new analysis method for quantifying waveforms, and our method has successfully enabled simultaneous measurement of calcium oscillations in a 96-well plate. Features of waveforms were extracted automatically and allowed the characterization of some anti-epileptic drugs using principal component analysis. Moreover, we have shown that trajectories in accordance with the concentrations of compounds in principal component analysis plots were unique to the mechanism of anti-epileptic drugs. We believe that an approach that focuses on the features of calcium oscillations will lead to better understanding of the characteristics of existing anti-epileptic drugs and allow prediction of the mechanism of action (MoA) of novel drug candidates.</span></p>
General-Relativistic Hydrodynamics Simulation of a Neutron Star — Sub-Solar-Mass Black Hole Merger - Gravitational Waveform
<p>This dataset contains the gravitational waveform for NSbh simulation. See the README.txt file for details.</p> <p>Simulations: Swami Vivekanandji Chaurasia (Stockholm University);</p> <p>Postprocessing: Maximiliano Ujevic (Universidade Federal do ABC) and Adrian Abac (Max Planck Institute for Gravitational Physics);</p> <p>Data release packaging: Ivan Markin (University of Potsdam);</p> <p>Simulations for the project have been performed on the national supercomputer HPE Apollo Hawk at the High Performance Computing (HPC) Center Stuttgart (HLRS) under the grant number GWanalysis/44189, on the GCS Supercomputer SuperMUC NG at the Leibniz Supercomputing Centre (LRZ) [project pn29ba], and on the HPC systems Lise/Emmy of the North German Supercomputing Alliance (HLRN) [project bbp00049] for the final production runs. The particular simulation has been run on HLRN.</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.