Find research datasets worth reusing

opencc-by-4.0May 2024View details →

Data to accompany the outlier-waveform-detection Github repository (internal globus pallidus, GPi)

This repository contains data based on neuronal recordings from two monkeys (G and I, in the pre- and post-MPTP states) that serve as input to the code provided at <a href="https://github.com/turner-lab-pitt/outlier-waveform-detection">https://github.com/turner-lab-pitt/outlier-waveform-detection</a>. Text files located within that Github repository provide detailed instructions on how these data may be used with that code.  As described in those text files, extra data are provided for Monkey G, in the pre-MPTP state. The data-description.txt file provides detailed information regarding the contents of each zipped tar archive. Briefly, the most important components of the files are the "snips" (individual spike waveforms) from the two monkeys and MPTP states, as extracted for each of a series of single sorted units from the internal globus pallidus (GPi).  The additional G-Pre data provides examples of the high-pass filtered voltage signals from which these snips were extracted.  All data are stored in the Matlab .mat format. All zipped files can be decompressed with 7-zip: <a href="https://www.7-zip.org/" target="_blank" rel="noopener">https://www.7-zip.org/</a> These data and the associated Github code were used for analyses reported in an in-preparation manuscript (Kase et al., "Movement-related activity in the internal globus pallidus of the parkinsonian macaque"), and also with a preprint that is currently under review: <div> <div>Detecting rhythmic spiking through the power spectra of point process model residuals</div> </div> <div>Karin M. Cox, Daisuke Kase, Taieb Znati, Robert S. Turner</div> <div>bioRxiv 2023.09.08.556120; doi: <a href="https://doi.org/10.1101/2023.09.08.556120" target="_blank" rel="noopener">https://doi.org/10.1101/2023.09.08.556120</a></div> <div> </div> This research was funded in part by Aligning Science Across Parkinson's [ASAP-020519] through the Michael J. Fox Foundation for Parkinson's Research (MJFF). For the purpose of open access, the authors have applied a Creative Commons Attribution 4.0 International (CC BY) public copyright license to this dataset.

Fig. 7. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)

Fig. 7. Waveform a) and spectrogram b) of simple chirps produced by a male (first and third chirps) and female (second and fourth chirps) when paired together in gallery. The spectral profile c) was taken at the center time of the first chirp, highlighted in a and b. Center time is the point during a sample about which energy is divided equally.

Fig. 8. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)

Fig. 8. Waveform a) and spectrogram b) of overlapping chirps produced by female and male D. approximatus. The first chirp in the sequence was produced by the female and is repeated approximately every 0.5 s. The female chirp overlaps with male chirps between 2 and 5 s in the recording. Sound occurring just past 4 s was not produced by either beetle, but rather was accidental noise produced by the recorder.

Fig. 6. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)

Fig. 6. Waveform a), and spectrogram b) of a male interrupted chirp showing individual syllables within each chirp.

Fig. 5. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)

Fig. 5. Waveform a), and spectrogram b) of female simple chirp in a disturbance context. The waveform shows the relative amplitude in generic units and the spectrogram shows frequency over time with darker shades indicating higher relative energy.

Fig. 4. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)

Fig. 4. Waveform a), spectrogram b), and spectral profile c) of male simple chirp in a disturbance context. The waveform shows the relative amplitude in generic units and the spectrogram shows frequency over time with darker shades indicating higher relative energy. The spectral profile was taken at the midpoint of the first chirp, highlighted in a and b.

opencc-by-4.0Jul 2024View details →

Dataset for the "Modulation waveform determination in sinusoidal frequency modulated interferometers"

This dataset supports the findings of the study presented in the paper "Modulation waveform determination in sinusoidal frequency modulated interferometers". The acquired data is saved in Python NumPy array format (.npy) and divided into four folders: <ul> <li> Method_verification_with_PTA: This folder contains two measurements for the verification of the method proposed in the paper, as described in Section 4.1. </li> <li> Amplitude_sweep and Frequency_sweep: These folders contain data on the characterization of laser optical frequency modulation with different modulation amplitudes and frequencies. The results of analyzing this data are presented in Section 4.2 of the paper. </li> <li> Intensity_modulation_correction_test: This folder contains measurements for additional investigation on the influence of intensity modulation correction on the RRI method, as described in Section 4.3. </li> </ul>

Surrogate waveform model data for black hole binary systems computed in point-particle black hole perturbation theory

This repository contains all publicly available surrogate data for gravitational waveforms produced within the point-particle black hole perturbation theory framework and calibrated to numerical relativity simulations performed with the Spectral Einstein Code (SpEC).  Several surrogate models are currently available in this catalog: <ol> <li>BHPTNRSur2dq1e3, for aligned spin black hole binary systems with mass-ratios varying from 3 to 1000 and spins from −0.8≤χ1≤0.8 on the larger black hole. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with calibration to numerical relativity (NR) data. The waveforms include all spin-weighted spherical harmonic modes up to ℓ=4 except the (4,1) and m=0 modes. Model details can be found in <a href="https://arxiv.org/abs/2407.18319">Rink et al. 2024</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/blob/main/tutorials/BHPTNRSur2dq1e3.ipynb">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li>BHPTNRSur1dq1e4, an updated version of the EMRISur1dq1e4 model described below. The updated version includes better calibration to NR, a smoother transition to plunge model, and more harmonic modes. Model details can be found in <a href="https://arxiv.org/abs/2204.01972">Islam et al. 2022</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/tree/main/tutorials/BHPTNRSur1dq1e4">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li>EMRISur1dq1e4, for non-spinning black hole binary systems with mass-ratios varying from 3 to 10000. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT), with the total mass rescaling parameter tuned to NR simulations. Available modes are [(2,2), (2,1), (3,3), (3,2), (3,1), (4,4), (4,3), (4,2), (5,5), (5,4), (5,3)]. The m<0 modes are deduced from the m>0 modes. Model details can be found in <a href="https://arxiv.org/abs/1910.10473">Rifat et al. 2019</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="http://github.com/BlackHolePerturbationToolkit/EMRISurrogate">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/EMRISurrogate/blob/master/EMRISur1dq1e4.ipynb">tutorial</a>) or the GWSurrogate Python package (Jupyter notebook <a href="https://github.com/sxs-collaboration/gwsurrogate/blob/master/tutorial/notebooks/nonspinning_nr_emri.ipynb">tutorial</a>), which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a>.</li> </ol>

A model of P-wave velocity beneath the greater Alpine region from teleseismic full P-waveform inversion

The dataset provides values of P-wave velocity in a 3D spherical chunk beneath the greater Alpine region as they resulted from a teleseismic full waveform inversion of AlpArray data.  Please find a description of the dataset in the accompanying README file.

Bridge force waveforms obtained by systematically bowing various cello G-string models

This dataset provides the waveforms of the bridge forces used in the research article titled “An experimental approach for comparing the influence of cello string type on bowed attack response” (see JASA Express Letter https://doi.org/10.1121/10.0034330 ). The data consists of audio recordings containing the force signals that the string exerts at the bridge (bridge force) during a systematic bowing action. The recordings capture bridge force signals at different bow forces and bow accelerations. Various cello G2-strings (98 Hz)  models were used, as the main goal of this study was to investigate the influence of string properties on attack playability (how quickly the string responds to a bowing action). Details on the experimental procedure and methodology can be found in the related work. In general, this dataset offers resources for researchers interested in: <ul> <li>Investigating bow-string interaction phenomena</li> <li>Analysing characteristics of bowed-string transients</li> <li>Developing or validating models of bowed-string instruments</li> </ul> Data Organization: The folders of this repository are organised as follows: <ul> <li>waveforms This folder contains all the waveforms <ul> <li>Model_X_Y The folder refers to a tested string. The cello string model is named X, from A to D, and the sample is numbered with Y: 1 or 2. For example, Model_A_1 is the folder containing the recording of the string model A and sample number 1. In the related work, only the samples numbered 2 were taken into account for the playability analysis.  <ul> <li>yyyy-mm-dd(_r) Each subfolder refers to a measurement session in which the string was bowed at different bow forces and bow acceleration to populate a Guettler diagram. The day of the measurement session is indicated in the folder name: yyyy is the year, mm the month, and dd the day. Additionally, some measurements were performed reverting the order of measurements, and they are indicated with _r at the end of the name. The numbering used for indicating the Guettler diagrams in the related work, referred to it as “repetition n. 1, 2, …”, follows the chronological order reported in the name of these subfolders. <ul> <li>Fb_nnn_a_mmm.wav The recording are single channel wav files sampled at 50kHz at a 64 bit rate. The bow force Fb and the bow acceleration a at which the bowing action was performed, is indicated in the file name. For example, a file named Fb_3.76_a_1.96.wav contains the waveform of the bridge force of the string bowed with an average bow force of 3.76 Newton and a constant acceleration of 2.96 m/s^2. </li> </ul> </li> </ul> </li> </ul> </li> </ul> Notes: <ul> <li>To obtain the bridge force in Newton, it is sufficient to multiply the signals by 10.</li> <li>Signals' length vary depending on the bow acceleration. Since the recordings capture the bridge force at a constant length bow stroke, higher accelerations lead to shorter recordings.  Short recordings are around 0.22 seconds long, while longs last for about 0.9 seconds. </li> </ul>

opencc-by-4.0Mar 2023View details →

Global canopy top height estimates from GEDI LIDAR waveforms for 2020

Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data from April-July 2020. The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. See also the repository for the data from April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a>. This repository also contains the file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>with more information on how to parse and load the prediction orbit files. GEDI mission website: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>. Citation: Use of these data require citation of this dataset: Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2020 (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7737869 Original research article: Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268, 112760.

opencc-by-4.0Apr 2023View details →

Dataset for 'Comparing Adjoint Waveform Tomography Models of California Using Different Starting Models'

This dataset includes the three final models for 'Comparing Adjoint Waveform Tomography Models of California Using Different Starting Models.' Each model is an adjoint waveform tomography model of California and Nevada; the models begin from three separate starting models: <ul> <li>CANV_CS begins with CSEM_NA (Krischer et al., 2018; <a href="https://doi.org/10.1029/2017JB015289">https://doi.org/10.1029/2017JB015289</a>)</li> <li>CANV_SP begins with SPiRaL (Simmons et al., 2021; <a href="https://doi.org/10.1093/gji/ggab277">https://doi.org/10.1093/gji/ggab277</a>)</li> <li>CANV_WUS begins with WUS256 (Rodgers et al., 2022; <a href="https://doi.org/10.1029/2022JB024549">https://doi.org/10.1029/2022JB024549</a>)</li> </ul> The models are provided in both NetCDF format and HDF5 format. Figure generation codes and EventXML files for both the inversion and validation event sets can be found <a href="https://doi.org/10.5281/zenodo.7839070">here</a>.   This effort was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008.  This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

openother-openApr 2023View details →

Examining the Effects of Resolution Bandwidth when Measuring Compound Radar Waveforms Data

Data used by NTIA/ITS TR-23-566 Examining the Effects of Resolution Bandwidth when Measuring Compound Radar Waveforms.

opencc-by-4.0Aug 2023View details →

NASA's Airborne Topographic Mapper (ATM) airborne waveform and ground calibration data for the Arctic Spring campaign 2016

The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors. In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM airborne waveform data from the T2 lidar, as well as the ground test data and true ranges for the Arctic Spring campaign 2016. A collection of MATLAB® functions to read ground test waveform and airborne waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a> See also:  <ul> <li>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></li> <li>User guide for NASA's Airborne Topographic Mapper HDF5 waveform data products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></li> <li>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></li> <li>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></li> <li>All ATM data products are freely available at the National Snow and Ice Data Center (NSIDC) at <a href="https://nsidc.org/data/icebridge">https://nsidc.org/data/icebridge</a> and can also be downloaded from the NASA Earthdata portal at <a href="https://earthdata.nasa.gov/">https://earthdata.nasa.gov/</a></li> <li>The ILATMW1B airborne waveform data is available at NSIDC: <a href="https://nsidc.org/data/ILNSAW1B/versions/1">https://nsidc.org/data/ILNSAW1B/versions/1</a> (narrow swath) <a href="https://nsidc.org/data/ILATMW1B/versions/1">https://nsidc.org/data/ILATMW1B/versions/1</a> (wide swath)</li> </ul>

opencc-by-4.0Jun 2017View details →

Gathered datasets of cardiorespiratory activity by using the inductive coupling with single coil together with reference waveforms of ECG and spirometer

Hereby the datasets, that were gathered during the experiments of monitoring the cardiorespiratory activity by using the inductive coupling together with the reference waveforms of ECG and spirometer, are published.: Waveforms of inductive monitoring.zip Reference waveforms of ECG and spirometer.zip A single volunteer participated in the experiments: healthy male, 33 years old, height of 183 cm and weight of 70 kg. The results are gathered from 12 positions lying around the thorax in imaginary horizontal level, approximately 10 mm below xiphisternal joint. The central position on front side of thorax is designated according to xiphisternum. The central position on front side of thorax is designated according to backbone. The datasets are available in .txt format, and are in separate .zip packages for inductive monitoring and reference waveforms. The correcponding dataset for every position can de identified by position number in both datasets. The signals are raw signals, not processed in any way. In the case of inductive monitoring the change variation of equivalent parallel resonance impedance (Rp) is shown. Concerning the reference waveforms, in the case of ECG, the result is shown in voltages (V). The reference data of respiration (gathered by using spirometer) is available in the form of two sets of digital pulses (0-s and 1-s). These pulses are representing the inhalation and exhalation i.e. either the rotor of the spirometer turns in one way or another. According to the frequency of the pulses, depending on the air flowing speed, the respiratory waveform can be constructed.

opencc-by-4.0Dec 2022View details →

Dataset for "CANVAS: An adjoint waveform tomography model of California and Nevada"

This dataset includes the model files for the California-Nevada Adjoint Simulations (CANVAS) adjoint waveform tomography model. The model is provided in NetCDF format. The HDF5 file provided is for importing the model into Salvus (Afanasiev et al., 2019; <a href="mondaic.com">mondaic.com</a>) An EventXML file is also provided with information on the events used to calculate the model.   Information on the construction of the model can be found in the accompanying publication: Doody, C., Rodgers, A., Afanasiev, M., Boehm, C., Krischer, L., Chiang, A., & Simmons, N. (2023). CANVAS: An adjoint waveform tomography model of California and Nevada. Journal of Geophysical Research: Solid Earth, 128(12). <a href="https://doi.org/10.1029/2023JB027583">https://doi.org/10.1029/2023JB027583</a>   This effort was support by Lawrence Livermore National Laboratory's Laboratory Directed Research and Development project 20-ERD-008.  This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-856789

opencc-by-4.0Dec 2023View details →

Data Set: Radio Frequency Senser (RFS) example waveforms, altitudes, and density plot

This data set contains data for the paper entitled "Radio Frequency Sensor: radio frequency lightning detection in geostationary orbit", submitted to Radio Science in December 2023. This data set contains three types of RFS data. 1) The first are time domain waveforms of three lightning events, in both the RFS high band (116 – 142 MHz) and the RFS low band (10-60 MHz), sampled at 155 MHz. The waveforms are right-hand circularly polarized waveforms. 2) The second type of data is altitudes and locations of trans-ionospheric pulse pairs (TIPPs) over time. The locations were determined by time coincidence with geolocated World Wide Lightning Location Network strokes. 3) The third type is RFS events per square kilometer per year in latitude and longitude. The RFS event were located by time correlation to Earth Networks Global Lightning Network lightning strokes. Data set 1: Consists of six ASCII files – 3 high band & 3 low band example RFS right-hand circularly polarized waveforms. Each ascii file contains a header with the RFS event time in UTC, the label of “RFS high band (77.5 – 155 MHz)” or “low band (0 – 77.5 MHz)”, and sample rate (155 MHz). Data following the header are time samples of electric field in uV/m sampled at 155 MHz. Filenames are: RFS_waveform_HighBand_20230607_010803.txt RFS_waveform_HighBand_20230607_015553.txt RFS_waveform_HighBand_20230607_034055.txt RFS_waveform_LowBand_20230607_010803.txt RFS_waveform_LowBand_20230607_015553.txt RFS_waveform_LowBand_20230607_034055.txt   Data set 2: Filename = ‘RFS_TIPPs_20230607_0100-0500_UTC.txt’ 1 ASCII comma separated value (CSV) file. Columns are: 1.     UTC date yyyy/mm/dd 2.     UTC seconds of day 3.     WWLLN-determined latitude (degrees, wwlln_latitude in header) 4.     WWLLN-determined longitude (degrees, wwlln_longitude in header) 5.     TIPP-estimated height (km, height in header)   Data set 3: Filename = ‘RFS_map.csv’ 1 CSV file of a 2-dimensional data set. 1.     Row 1, Longitude (degrees, in 0.25-degree steps) 2.     Column 1, Latitude (degrees, in 0.5-degree steps) 3.     2-D grid in latitude and longitude: events per square kilometer per year Notes: Since the RFS coverage range goes across longitude = -180/180 degrees, longitudes go from 147.75 to 180, then start at -180 to -12.75. Latitude range goes from -58.5 to 68.5, as there were no detected RFS events outside these latitudes. The three examples given in data set 1 are those shown in LA-UR-23-32419, Figure 2. The TIPP data in data set 1 is shown in LA-UR-23-32419, Figure 4, and comprises data from 07 June 2023 between 01:00-05:00 UTC. Data set 3 contains RFS event rates per sq. km per year for data from 1 March 2022 – 1 March 2023, with the caveats described in LA-UR-23-32419.

opencc-by-4.0Dec 2023View details →

Data for spectrogram and waveforms

This dataset contains the ascii data for spectrogram and waveforms observed by distributed acoustic sensing with the Muroto cable.

opencc-by-4.0Mar 2024View details →

Dataset related to 'Full-waveform inversion reveals diverse origins of lower mantle positive wave speed anomalies'

This repository contains the global distribution of sources and receivers, tomographic models (netCDF4 format), stacked waveforms from the wavefield modelling (.h5 format), 2D grids of the time-depth correlations (.csv format), and Python scripts required for the full analysis and figures presented in the manuscript.

Three-dimensional crustal channel flows beneath the southeastern Tibetan Plateau revealed by full-waveform ambient noise tomography

This is a new version of Vp and Vs models for paper titled "Three‐Dimensional Crustal Channel Flows Beneath the Southeastern Tibetan Plateau Revealed by Full‐Waveform Ambient Noise Tomography" published in Geophysical Research Letters. Modification history: new Vs model includes from surface downward to 120 km depth. Please ignore the models in version 1 and 2.