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49 results for “Wave model data”
Data associated with manuscript "Predicting wave-induced sediment resuspension at the perimeter of lakes using a steady-state spectral wave model"
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Data release for "Consistent eccentricities for gravitational wave astronomy: Resolving discrepancies between astrophysical simulations and waveform models"
<div> <p>This is a data release to accompany <a href="https://arxiv.org/abs/2402.07892">arXiv:2402.07892</a> "Consistent eccentricities for gravitational wave astronomy: Resolving discrepancies between astrophysical simulations and waveform models".</p> <p>See also <a href="../doi/10.5281/zenodo.10974974">https://zenodo.org/doi/10.5281/zenodo.10974974</a>. Please cite the paper (<a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240207892V/abstract">https://ui.adsabs.harvard.edu/abs/2024arXiv240207892V/abstract</a>) if you use this in a publication or any other scientific work.</p> <p><br>The file can be read in using<br><br>```<br>import pandas</p> <p>data = pandas.read_hdf("CMC_eccentricities_standardized.hdf5")<br>```<br><br>The description of the different keys in the file are as follows:<br><br>- `m1`: source-frame mass of the primary object in the binary in units of Msun<br>- `m2`: source-frame mass of the secondary object in the binary in units of Msun<br>- `chi1`: dimensionless spin of the primary object in the binary<br>- `chi2`: dimensionless spin of the secondary object in the binary<br>- `z`: cosmological redshift<br>- `a0`: Initial separation in AU. This typically corresponds to the stopping criterion in astrophysical simulations.<br>- `e0`: The eccentricity at the initial separation `a0`<br>- `f0`: 22 mode frequency corresponding to the initial separation.<br>- `channel`: Takes values between 1 and 5. <br> - 1: Ejected mergers <br> - 2: In-cluster (two-body) mergers<br> - 3: Binary-Single Encounters<br> - 4: Binary-Binary Encounters<br> - 5: Single-Single Encounters<br>- `cluster_weight`: Weight given to each binary based on cluster properties (mass and metallicity), assuming some initial mass function for the cluster and metallicity evolution as a function of redshift.<br>- `cosmo_weight`: Weight given to each binary based on the redshift, to account for cosmological volume.<br>- `e_W03_*Hz`: Eccentricities extracted from the Wen 2003 prescription at the reference peak frequency specified.<br>- `e_t_2PN_10Hz`: Eccentricities extracted from the prescription in Vijaykumar et. al. 2024 at reference 22 mode frequency of 10 Hz. <br>- `e_t_2PN_Mf_1000HzMsun`: Eccentricities extracted from the prescription in Vijaykumar et. al. 2024 at reference 22 mode frequency corresponding to `M \times f = 1000 Hz Msun`. This ensures that eccentricity is defined at a fixed number of cycles before merger, independent of the cosmological redshift.<strong> We strongly recommend that all eccentricity estimates from astrophysical simulations are quoted at a reference frequency corresponding to fixed `M \times f`.</strong><br>- `total_weight`: `cosmo_weight \times cluster_weight`<br><br><strong>NOTE</strong>: For mergers belonging to `channel=1`, we straightaway set the `e_t_*` estimates to zero. This is because mergers from this channel will not be eccentric at `f>10 Hz`, and due to its large initial separation is computationally intensive to evolve using the PN evolution equations.</p> <p> </p> </div>
Data for the 1-D P-wave velocity model
<p>Data for the 1D P-wave velocity model</p>
Model code and Data for manuscript 'The Role of Wave-induced Mixing in Spring Phytoplankton Bloom in the South Yellow Sea'
<p>This repository contains four files:</p> <p>(1) Wave-induced mixing data for China seas;</p> <p>(2) Water transparency data for the South Yellow Sea;</p> <p>(3) Observation data used in this study; and</p> <p>(4) Model code for our ecological model and model experiments, including configuration data file. </p>
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
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.