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COVID-CAWI WAVES 1 and 2
<p>The <strong>COVID-CAWI</strong> survey was initiated, developed and conducted as part of a collective research project by Gabriela Yordanova and Ekaterina Markova: "Challenges to Quantitative Empirical Research in Social Emergencies: The Case of Covid-19" at the Institute of Philosophy and Sociology at the Bulgarian Academy of Sciences (IPS-BAS). The <strong>COVID-CAWI </strong>was conducted using a <strong>C</strong>omputer-<strong>A</strong>ssisted <strong>W</strong>eb <strong>I</strong>nterview (CAWI) in <strong>two waves</strong>, which coincided with the periods of restrictive measures introduced in Bulgaria due to the Covid-19 pandemic. In both waves, the <strong>sampling procedure is without replacement </strong>– controlled through the CAWI instrument design, and further verified through a data cleaning procedure.</p> <p>Both datafiles include fully anonymised row data, <strong>user friendly lists</strong> <strong>of variable labels</strong> in Bulgarian and English for data processing. <strong>Weighting </strong>variable is also included at both datasets.</p> <p>The release guide is available at the billingual publication <strong>Маркова, Е., & Йорданова, Г. </strong>(2024). <em>Кризисната ситуация заради пандемията от Коронавирус в България: социално-икономически, здравни и образователни ефекти. Методическо ръководство и инструментариум: COVID-CAWI 1&2</em>. Издателство на БАН "Проф. Марин Дринов", https://doi.org/10.7546/TPCCB.2024 (<strong>Markova, E., Yordanova, G. 2024. </strong>The pandemic coronavirus crisis in Bulgaria: socio-economic, health and educational effects. Release Guide and Questionnaires: COVID-CAWI Waves 1&2, Sofia: Prof. Marin Drinov Publishing House of Bulgarian Academy of Sciences, ISBN 978-619-245-419-7) with open access, https://doi.org/10.7546/TPCCB.2024. Both <strong>questionnaires </strong>(wave 1 and 2) are available in Annex - in Bulgarian and English language.</p> <div> <p><strong>Data citation requirements</strong></p> <p>It is mandatory that users of <strong>COVID-CAWI</strong> waves 1&2 datasets cite the following:</p> <p>1)<strong> Маркова, Е., & Йорданова, Г. (2024). </strong><em>Кризисната ситуация заради пандемията от Коронавирус в България: социално-икономически, здравни и образователни ефекти. Методическо ръководство и инструментариум: COVID-CAWI 1&2.</em> Издателство на БАН "Проф. Марин Дринов", https://doi.org/10.7546/TPCCB.2024.</p> <p>2)<strong> </strong><strong>Markova, E., Yordanova, G., Tosheva, E. 2023. </strong>Online Survey Data on Economic Effects of Lockdowns and Post-Stratification Data Adjustment: Evidence from Bulgaria. <em>Economic Alternatives, </em>Issue 1, pp. 5-25, https://doi.org/10.37075/EA.2023.1.01.</p> <p>3) <strong>Yordanova, G., Markova, E. 2024. </strong>COVID-CAWI Wave 1&2, Release version: 0.1., Data set. DOI 10.5281/zenodo.10777379.</p> </div>
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Momentum space wave functions for the linear potential
<p>Normalized momentum space wave functions for the linear potential. The Schrödinger equation was solved with the methods described in "A simple high-accuracy method for solving bound-state equations with the Cornell potential in momentum space", Alfred Stadler, Elmar P. Biernat, Vasco Valverde. </p> <table> <tbody> <tr> <td><a href="https://arxiv.org/abs/2407.21789">arXiv:2407.21789</a> [hep-ph]</td> </tr> </tbody> </table> <p>(to be pulished in Physical Review D)</p> <p>The wave functions correspond to the energie eigenvalues shown in Table VI of this work.</p> <p>The name of each file indicates the orbital angular momentum and which eigenstates it contains. For instance, wf_n1-5_l=0_np=1000_NL=5.txt contains the wave functions of the states n=1, 2, 3, 4, 5 for l=0, and wf_n6-10_l=3_np=1000.txt the wave functions of the states n=6, 7, 8, 9, 10 for l=3. Furthermore, np=1000 means that 1000 momentum integration points were used for the solution of the Schrödinger equation, and NL=5 or NL=15 means that 5 or 15 points were used for the Lagrange interpolations.</p> <p>Each data file in text format contains 6 columns and 1000 lines. Column 1 ist the momentum (GeV), columns 2-6 the wave functions. The momenta were generated according to Eq. (4.3) of the article, with p_0=1.</p> <p> </p>
LIGO/Virgo/KAGRA Gravitational Wave O4-O5 simulations
<p>Realistic simulations of BNS and NSBH gravitational wave merger events with: </p> <p>a) LIGO at O4 sensitivity (HLO4)</p> <p>b) LIGO at O4, Virgo at O3 sensitivity (HLO4_VO3)</p> <p>c) LIGO, Virgo, KAGRA at O5 sensitivities (HLVKO5)</p> <p>mass and spin distributions are drawn from the PDB model from <em>Amanda Farah et al 2022 ApJ <strong>931</strong> 108.</em></p>
Two-bubble simulation and gravitational wave spectrum codes and data
<p><span>Code and data used in the paper with title</span><a href="https://doi.org/10.1103/PhysRevD.104.075039"><span> <em>Vacuum bubble collisions: from microphysics to gravitational waves </em>by Oliver Gould, Satumaaria Sukuvaara, and David Weir</span></a><span> [</span><a href="https://arxiv.org/abs/2107.05657"><span>arXiv:2107.05657</span></a><span>]. </span></p> <p><span>The field simulation and gravitational wave spectrum calculation codes are based on Gravitational radiation from colliding vacuum bubbles by Arthur Kosowsky, Michael S. Turner and Richard Watkins [</span><a href="https://inspirehep.net/literature/324187"><span>Inspire</span></a><span>].</span></p> <p><span>Contains files:</span></p> <ul> <li> <p><span>two_bubbles_code-v1.0.1.zip is a snapshot of a</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/"><span> git repository</span></a><span>, corresponding to</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/-/tree/v1.0.1?ref_type=tags"><span> commit v1.0.1</span></a><span>. Contains the codes with which the majority of the data was produced.</span><span><br><br></span></p> </li> <li> <p><span>two_bubbles_data-v1.0.1.zip is a snapshot of a</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/"><span> git repository</span></a><span>, corresponding to</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/-/tree/v1.0.1?ref_type=tags"><span> commit v1.0.1</span></a><span>. It contains the majority of data used in the paper. Note however that the simulation pickle files are examples run on a coarser lattice due to Zenodo file size restrictions. Apart from few exceptions, the data in this file was produced by the codes in two_bubbles_code-v1.0.1.zip.</span><span><br><br></span></p> </li> </ul> <p><span>README.md files, specifying and explaining the contents and usage, are included within. The v1.0.1 of</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/-/blob/v1.0.1/README.md?ref_type=tags"><span> </span><span>code README.md</span></a><span> and the</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/-/blob/v1.0.1/README.md?ref_type=tags"><span> </span><span>data README.md</span></a><span> can be found from the repositories as well.</span></p> <p><span>The update v1.0.1 updates the README and fixes a small error in the calculation of the gravitational wave spectrum. We thank Toby Opferkuch for pointing this out. The error in the code does not affect the results in two_bubbles_data-v1.0.0.zip or the paper as they were produced with a slightly earlier version of the code, before the appearance of this error. The version two_bubbles_data-v1.0.1 updates the README, clarifying some points.</span></p>
Observations of the Bottom Boundary Layer beneath the World's Largest Internal Solitary Waves_for JGR submission
<p>This folder contains preprocessed data, data processing scripts, Reynolds Averaged Navier Stokes (RANS) simulation scripts, and plotting scripts that produce the results in the manuscript entitled, "Observations of the Bottom Boundary Layer beneath the World's Largest Internal Solitary Waves," for submission to Journal of Geophysical Research Oceans by Trowbridge, Helfrich, Reeder, Medley, Chang, Jan, Ramp, and Yang.</p> <p> </p>
DATA SET: Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial
<p>This repository contains the data sets related to the publication:</p> <p>Cortese, L.; Zanoletti, M.; Karadeniz, U.; Pagliazzi, M.; Yaqub, M.A.; Busch, D.R.; Mesquida, J.; Durduran, T. Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 6957. https://doi.org/10.3390/s21216957</p>
Coupled atmosphere-wave-ocean simulation of Hurricane Dorian (2019)
<p><strong>Description</strong></p> <p>This dataset provides the output of the coupled atmosphere-wave-ocean simulation of Hurricane Dorian from August 29 to September 7, 2019. The simulation is a composite of two separate simulations:</p> <ol> <li>From 00 UTC August 29 to 00 UTC September 1, 2019</li> <li>From 00 UTC September 1 to 00 UTC September 7, 2019</li> </ol> <p>The first simulation serves as "spin-up" for the hurricane and its environment prior to landfall. The second simulation is initialized from the output of the first simulation, while relocating the Dorian vortex to its correct position on September 1. Due to the size of the dataset only the surface fields are made available.</p> <p><strong>Model configuration</strong></p> <ul> <li><strong>Atmosphere</strong>: Weather Research and Forecasting (WRF, https://github.com/wrf-model/WRF) model v4.2.2, with the Advanced Research WRF (ARW) dynamical core. The model has a 3-km resolution grid over the parent domain and a 1-km resolution nest over the Bahamas region (September 1-7 only), both with 45 vertical layers. Initial and boundary conditions are based on 6-hourly ERA-5 dataset.</li> <li><strong>Ocean Waves</strong>: University of Miami Wave Model (UMWM, https://umwm.org). The model is configured at the same 3-km as the atmosphere model, and has 36 directional bins and 37 frequency bins that are logarithmically spaced from 0.0313 to 2 Hz.</li> <li><strong>Ocean Circulation</strong>: HYbrid Coordinate Ocean Model (HYCOM, https://github.com/HYCOM) v2.3.01, configured at 0.01 degree resolution and 41 vertical layers. Initial and boundary conditions are based on daily GOFS 3.1 41-layer HYCOM + NCODA Global 1/12° Analysis, daily. K-Profile Parameterization for vertical mixing.</li> <li><strong>Coupling</strong>: Earth System Modeling Framework (ESMF, https://github.com/esmf-org/esmf) v8.0.1</li> </ul> <p><strong>File Description</strong></p> <ul> <li>blkdat.input - HYCOM (ocean circulation) configuration file</li> <li>dorian2019_atmosphere_1km_2019090100.nc - Atmosphere at 1-km resolution dataset</li> <li>dorian2019_atmosphere_waves_3km_2019082900.nc - Atmosphere and waves at 3-km resolution dataset, Aug 29 - Sep 1.</li> <li>dorian2019_atmosphere_waves_3km_2019090100.nc - Atmosphere and waves at 3-km resolution dataset, Sep 1-7</li> <li>dorian2019_ocean_1km_2019082900.nc - Ocean circulation at 1-km resolution dataset</li> <li>main.nml - UMWM (waves) configuration file</li> <li>namelist.input - WRF (atmosphere) configuration file</li> <li>regional.depth.[ab] - HYCOM (ocean circulation) bathymetry files</li> <li>regional.grid.[ab] - HYCOM (ocean circulation) grid files</li> <li>umwm.gridtopo - UMWM (waves) grid and bathymetry file</li> <li>wrfbdy_d01 - WRF (atmosphere) boundary conditions file</li> <li>wrfinput_d01.2019082900 - WRF (atmosphere) initial conditions file for parent domain on Aug 29</li> <li>wrfinput_d01.2019090100 - WRF (atmosphere) initial conditions file for parent domain on Sep 1</li> <li>wrfinput_d02.2019090100 - WRF (atmosphere) initial conditions file for inner nest on Sep 1</li> </ul> <p><strong>Coupled model source code</strong></p> <p>The model source code has not yet been released. We plan to open source it upon publication of the paper describing the simulation. When the source code is released, we will add the link to this repository.</p>
Dataset for Observations of gravity wave refraction and its causes and consequences
<p>Dataset for the publication submitted to Journal of Geophysical Research: Atmospheres. The title of the publication is:</p> <p>Observations of gravity wave refraction and its causes and consequences</p>
Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions
<p>Dataset from the publication: <strong>Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions</strong>. DOI: <a href="https://doi.org/10.5194/egusphere-2022-230">10.5194/egusphere-2022-230</a></p> <p> </p> <p>Includes: <em><strong>EGFs, Dispersion Curves measurements, RFs, Vs3dmodel and Moho depths</strong></em></p> <p> </p>
The population of merging compact binaries inferred using gravitational waves through GWTC-3 - Data release
<p>Data associated with Figures, Tables, and population parameter samples associated with <br><strong>The population of merging compact binaries inferred using gravitational waves through GWTC-3 , </strong><br><strong><a href="https://dcc.ligo.org/LIGO-P2100239/public">LIGO DCC</a>, <a href="https://arxiv.org/abs/2111.03634">arXiv</a>, <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.13.011048">PRX</a>. </strong><br>This is v3, superseding v2. Please see the README.md for more information.</p>
Parker Solar Probe Filtered Ion Scale Wave Activity for Encounters 8 to 16
<p>The following datasets are the result of filtering algorithm applied to a wave analysis of Parker Solar Probe data from Encounters 8 to 16. The wave analysis was conducted by Kristoff Paulson using a Short-Time Fourier Transform (STFT) approach based on polarization techniques derived by Means, 1972 (DOI: <a href="http://doi.org/10.1029/JA077i028p05551">10.1029/JA077i028p055511135</a>). Included is a jupyter notebook containing the filtering algorithm, the results of the filtering, and a demonstration of how to best open the files. The dataset for each encounter contains 9 columns that correspond with:</p> <ol> <li>Date in CDF epoch</li> <li>Left-handed (LH) Integrated Wave Power (nT^2) where integration is over frequency space (0-32 Hz) of filtered activity</li> <li>Right-handed (RH) Integrated Wave Power (nT^2)</li> <li>LH median ellipticity where median is over frequency space</li> <li>RH median ellipticity</li> <li>LH median coherency</li> <li>RH median coherency</li> <li>LH median wave normal angle (deg)</li> <li>RH median wave normal angle (deg)</li> </ol> <p>In all cases, ellipticity is measured in the Parker Solar Probe spacecraft frame. Ellipticity measures the ellipticity of the polarization ellipse and takes on values between -1 and 1. Values of 1 correspond with RH circular polarization and -1 with LH circular polarization. Coherency takes on values between 0 and 1. It measures how interrelated fluctuations are where 0 represents noise and 1 represents coherent fluctuations. The wave normal angle is the angle between the wave vector, k, and the local mean magnetic field, B. Since there are inherent ambiguities in the direction of the wave vector for single spacecraft measurements, the wave normal angle is calculated such that it takes on angles from 0 to 90 degrees. The filtering algorithm selects activity in which coherency is above 0.8, absolute value of ellipticity is above 0.5, and wave normal angle is below 45 degrees such that coherent, circularly polarized, near parallel propagating wave activity on ion scales is selected. <strong>If wave power for a given time has value of 0.0, then no fluctuations in the magnetic field data passed the required filters at that time.</strong></p> <p>:</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Czech Republic
<p>This dataset was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. Work Package 7 of this project aims to test the processes in youth’s construction of active EU citizenship on various social and psychological levels. The main file contains quantitative data from the first wave of the longitudinal survey on adolescents and young adults (age 15-26). Data collection was carried out in the Czech Republic (regions Prague, South Moravian, Moravian-Silesian, Pardubicky, Vysocina) from October to December 2016. The supplementary files contain national translations of the questionnaire for the younger (15-19) and the older (20-26) subgroups.</p>
DS_Wave_Mutriku: Wave resource at Mutriku (Spain)
<p>Data obtained from the RBR virtuoso pressure sensor deployed in Mutriku: i) winter 2016-2017; ii) Spring 2018.</p> <p>Sensor is located at 10 m water depth, 200 m off the shoreline plant (43º18'52"N, 2º22'34").</p> <p> </p>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
FLOATECH WP3 experimental dataset : wave-tank hybrid testing of a 10 MW turbine based on a spar platform (ECN)
<p>This dataset presents the experimental measurements made in the Hydrodynamic and Ocean Engineering wave tank of Ecole Centrale de Nantes, in France, with the model of a 10 MW turbine supported by a spar platform at a scale 1:40. </p> <p>The tests were performed using a real-time hybrid testing method (or software-in-the-loop) called SoftWind presented and published in Ocean Engineering (the paper is available at this <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). </p> <p> </p> <p><strong>Presentation of the experimental model:</strong></p> <p>The model is presented in details in the provided Excel file "FLOATECH_C3_Project data and model description.xlsx". </p> <p> </p> <p><strong>In the dataset:</strong></p> <p>The measurement files of the tests are gathered in folders by "series", and each test file has a test number. The series and the test conditions of each run are detailed in the provided Excel file "FLOATECH_C3_Database_Matrix.xlsx". </p> <p>Decay tests, pull-out tests and hammer tests were performed and are given in the dataset. </p> <p> </p> <p><strong>Real-time simulation models</strong></p> <p>The numerical models used in the real-time OpenFAST simulations are also provided in the compressed file "RT Simulations files.zip". </p> <p> </p> <p><strong>Data used in the published paper:</strong></p> <p>Some of the tests were used in the paper (see <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). The corresponding test numbers are given in the table below. </p> <table> <tbody> <tr> <td><strong>Load cases</strong></td> <td><strong>Hs (m)</strong></td> <td><strong>Tp (s)</strong></td> <td><strong>Uhub (m/s)</strong></td> <td><strong>TI (%)</strong></td> <td><strong>Wave dir. (°)</strong></td> <td><strong>Wind dir(°)</strong></td> <td><strong>TestNum 1C</strong></td> <td><strong>TestNum 3C</strong></td> <td><strong>TestNum 5C</strong></td> </tr> <tr> <td>1.2</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>0</td> <td>269</td> <td>268</td> <td>270</td> </tr> <tr> <td>2.1</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>25</td> <td>275</td> <td>307</td> <td>281</td> </tr> </tbody> </table> <p> </p> <p> </p>
Perturbative gravitational wave predictions for the real scalar extended Standard Model, dataset
<p>This deposit contains data from a perturbative study of cosmological phase transitions in the real singlet scalar extension of the Standard Model (xSM). The data relates to the paper "Perturbative gravitational wave predictions for the real scalar extended Standard Model". Everything is contained within the archive file <em>xsm_results.tar.gz</em>, a tarball compressed with Gzip.</p> <p>The data covers phase transition properties for a scan of 100,000 parameter points in the xSM. Further details on the contents of the dataset are explained in the <em>README.md</em> within the tarball.</p>
Datasets for ``Simulations of helical inflationary magnetogenesis and gravitational waves''
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Simulations of helical inflationary magnetogenesis and gravitational waves" by Axel Brandenburg, Yutong He, and Ramkishor Sharma. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Equatorial wave filtering during May-September 2020 in the ECMWF OSE experiments with and without Aeolus data
<p>These are files with the global analyses produced by the observing system experiment with and without Aeolus data, and decomposed using the MODES software. Every file contain the zonal and meridional wind components and the pseudo-geopotential. Note that the paper makes use of the zonal winds only. Files including "hel1" in their titles belong to the OSE without Aeolus winds whereas the files with "hel4" in their names are from OSE including Aeolus winds. </p> <p>File names starting with KW belong to the Kelvin waves, file names starting with All contain the total fields, IGMRG denoted the non-Rossby modes whereas Rot belongs to file names containing only the signal associated with the Rossby modes. The Kelvin wave analyses are updated for the period from 1 May to 30 September, whereas other files are available for the periods discussed in the paper.</p> <p>The two movies are named MODES_KW_May2Sep2020.gif and MODES_BalancedUwind_May2Sep2020.gif for the Kelvin and balanced (Rossby modes) zonal winds averaged within 15 degrees N and 15 degrees S, respectively. Individual figures which constitute the movies are available at https://modes.cen.uni-hamburg.de.</p> <p> </p> <p> </p>
Avoided crossing in gravitational wave spectra from protoneutron star
<p>The data of the gravitational wavefroms of core-collapse supernovae, which are used in Sotani and Takiwaki (2020), Monthly Notices of the Royal Astronomical Society, Volume 498, Issue 3, pp.3503-3512.</p> <p>Data Format:</p> <p>The data are in ASCII format and the two columns are1:time time since bounce in sec</p> <p>2:hplus plus polarization of the GW amplitude. We assume the source distance of 10 kpc.</p> <p>The data are sampled at ~10 kHz, but, the sampling is not uniform in time. Therefore resampling might be necessary.</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.