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49 results for “Wave Dynamics”
Circulation dynamics: currents, waves, temperature measurements from moorings in lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing
Starting August 2018, five moorings deployed on the seafloor of multiple lagoons in the Beaufort Sea will record currents, waves, temperature, and pressure. Moorings are retrieved and re-deployed each August. This data is being collected to better understand the multi-seasonal circulation dynamics between the Beaufort Sea and coastal lagoons. Two moorings are deployed in Elson Lagoon, one in Stefansson Sound, one in Jago Lagoon, and one in Kaktovik Lagoon. Each mooring contains two data loggers: RBRduo3 T.D wave loggers and Lowell Instruments LLC TCM-1 tilt current meters. The RBR instruments measure temperature, pressure, and derived wave energy, average wave period, average wave height, maximum wave period, maximum wave height, 1/10 wave period, 1/10 wave height, significant wave period, significant wave height, tidal slope, depth, and sea pressure. The Lowell LLC TML-1 tilt current meters measure water velocity, heading, and temperature.
Code, data and scripts to study wave dynamics in asymmetric material
<p>Supplementary data for [1] Vladislav A. Yastrebov. "Wave propagation through an elastically-asymmetric architected material", 2021, https://arxiv.org/abs/1712.06294v2</p> <p>See "Readme.md" and indivual "Readme.md" files in folders: "data", "src", "fig"</p>
Spinning test-body orbiting around Schwarzschild black hole: circular dynamics and gravitational-wave fluxes
<p>We release gravitational wave fluxes at null-infinity from a spinning test-body in circular equatorial orbits around a Schwarzschild black hole. Four different prescriptions are used for the dynamics: the Mathisson-Papapetrou formalism under the Tulczyjew (TUL) spin-supplementary-condition (SSC), the Pirani (PIR) SSC and the Ohashi-Kyrian-Semerak (OKS) SSC, and the spinning particle limit of the effective-one-body Hamiltonian (HAM) of [Phys.~Rev.~D.90,~044018(2014)]. For more details see xxxx .</p> <p>The multipolar fluxes are given for l=2,3 m=1,2,3 at the Boyer-Lindquist radii</p> <p> r = 4 5 6 7 8 10 12 15 20 30 ,</p> <p>in cases they were not computed the data contains a "42". Note that the fluxes in these data files are assumed to contain both the +m and -m contributions, since they are identical for equatorial orbits and aligned spins. <br /> Additionally, the data files contain the key numbers describing the circular dynamics (see paper).</p> <p>Units <span class="math-tex"><em>c</em>=<em>G</em>=1.</span></p>
Figures datasets for "Wave momentum shaping for moving objects in heterogeneous and dynamic media"
<p>Source data for Figures used in the manuscript "Wave momentum shaping for moving objects in heterogeneous and dynamic media".</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>
Classifying the generation and formation channels of dynamically-formed gravitational-wave events
<p>This dataset contains all the simulations of dynamically-formed binaries performed with the software <a href="https://github.com/Kkritos/Rapster">rapster</a>, together with trained machine-learning classification models from (Antonelli, Kritos et al, in prep.), see <a href="https://github.com/aantonelli94/TheBHClassifier">the public codes online</a>.</p> <p>All items starting with "mergers_*" are simulations of clusters and they follow the structure reported in the documentation of <a href="https://github.com/Kkritos/Rapster">rapster</a>. The simulations differ in the choice of the hyperparameters for the distribution of the cluster mass, half-mass radius and initial spin distribution for the binaries.</p> <p>All items starting from "RFClassifier_*" are machine-learning classification models that use a Random Forest Classifier and that are trained with the simulations above. The models ending with "*_gen" predict the generation of the black holes, those with "*_form" predict their formation channels. </p> <p> </p>
Research Data - Collective Spin-Wave Dynamics in Gyroid Ferromagnetic Nanostructures
<p>Source data from ferromagnetic resonance experiments and micromagnetic simulations in <em>tetmag</em> software (<a href="https://github.com/R-Hertel/tetmag">https://github.com/R-Hertel/tetmag</a>), used in the paper "Collective Spin-Wave Dynamics in Gyroid Ferromagnetic Nanostructures"<em> </em>in <em>ACS Applied Materials & Interfaces </em>(<a href="https://doi.org/10.1021/acsami.4c02366">https://doi.org/10.1021/acsami.4c02366</a>).</p>
Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
<p>In this repository we have compiled 1-RDMs on a time grid obtained from various methods, namely, time-dependent full configuration interaction (TD-FCI), time-dependent coupled cluster (TD-CC), time-dpendent Hartree-Fock (TD-HF), Kadanoff-Baym Equations, and generalized Kadanoff-Baym approximation (GKBA). We have evaluated the 1-RDMs from Hubbard model in presence of an external drive. We have included a PySCF script to generate the integrals with a specific choice for various parameters. One can reproduce the HF results from that script. The Python script to evaluate various observables that we have analyzed in our article, namely, time-dependent dipole moment, Von-Neumann entropy are also added. </p>
Dynamics of Collective Modes in an unconventional Charge Density Wave system BaNi2As2 - Raw Data
<p>This repository includes two datasets included in the study "Dynamics of Collective Modes in an unconventional<br> Charge Density Wave system BaNi2As2". Two datasets are included:</p> <p>Temperature_dependent_reflectivity_changes.dat</p> <p>Fluence_dependent_reflectivity_changes_at 10K.dat</p> <p>Temperature_dependent_reflectivity_changes.dat contain photoinduced reflectivity transients, recorder on BaNi2As2 for sample temperatures between 13 K and 149K. The first column is time-delay, other columns are the corresponding photoinduced reflectivity traces recorded at respective temperatures (constant fluence of 0.4 mJ cm<sup>−2</sup>).</p> <p> </p> <p>Fluence_dependent_reflectivity_changes_at 10K.dat contain photoinduced reflectivity transients, recorder on BaNi2As2 at 10 K. The first column is time-delay, other columns are the corresponding photoinduced reflectivity traces recorded at respective fluences. Each signal has been normalized to the respective fluence.</p> <p> </p>
Fig. 4 in Parasite community dynamics in an invasive vole ‾ From focal introduction to wave front
Fig. 4. Statistical associations between ectoparasites and endoparasites infecting bank voles, Myodes glareolus. All associations are due to the presence of a coinfecting species, positive relationships are shown in black and negative relationships are in grey. The predicted absolute percentage change (see Methods for definition) of the dependent variable is given for each interaction. Parentheses identify whether the relationship is based on body mass (BM), host sex (S) or at the mid-point along the invasion wave (WM). Lice had a prevalence of less than 10% so were excluded from analyses.
Fig. 1 in Parasite community dynamics in an invasive vole ‾ From focal introduction to wave front
Fig. 1. Sampling sites of the invasive bank vole, Myodes glareolus, across the Republic of Ireland. Curraghchase and Adare are the focal point of introduction, Gort, Nenagh and Cashel are mid-wave, and Tuam, Birr and New Ross represent the invasion front. For all trapping sites along the invasion gradient (black = focal point of introduction, grey = mid-wave, and white = invasion front), bar charts depict the prevalence of each parasite and boxplots indicate the mean parasite abundance (log x +1), where boxes indicate the mean, and lines the 95% confidence intervals.
Fig. 3 in Parasite community dynamics in an invasive vole ‾ From focal introduction to wave front
Fig. 3. Parasite interactivity index from eight sampling sites at three points along an invasion gradient (black = focal point of introduction, grey = mid-wave, and white = invasion front) in the Republic of Ireland. Boxes represent upper and lower quartile, with median indicated, with bars representing maximum and minimum range.
Fig. 2 in Parasite community dynamics in an invasive vole ‾ From focal introduction to wave front
Fig. 2. Parasite abundance of the invasive bank vole, Myodes glareolus, from eight locations at three points along an invasion gradient (black = focal point of introduction, grey = mid-wave, and white = invasion front) in the Republic of Ireland. Boxes represent upper and lower quartile, with median indicated, with bars representing maximum and minimum range.
Dataset for "Impact of surface anisotropy on the spin-wave dynamics in thin ferromagnetic film"
<p>The dataset consist of the data used to prepare the figures for the manusript entitled <em>Impact of surface anisotropy on the spin-wave dynamics in thin ferromagnetic film.</em> </p> <p>Please read README.txt file to see the description of the data in the files.</p>
Wave Dynamics and Fluid Stresses in Vegetated and Unvegetated Coastal Lagoons in Virginia, 2010-2011
Wave, turbulence and wind measurements were performed within a Zostra marina seagrass (eelgrass) meadow in South Bay, Virginia, USA a coastal bay within the Virginia Coast Reserve where ongoing seagrass restoration efforts are being performed, and in adjacent areas without seagrass. These datasets were published in: Hansen J.C.R. and Reidenbach M.A., 2013, Seasonal growth and senescence of a Zostera marina seagrass meadow alters wave-dominated flow and sediment suspension within a coastal bay, Estuaries and Coasts, 36, 1099-1114.
Rapid Outer Radiation Belt Flux Dropouts and Fast Acceleration during the March 2015 and 2013 Storms: The Role of ULF Wave Ttansport From a Dynamic Outer Boundary
<p>Duplicate copy of the electron phase space density provided for the Geospace Environment Modeling (GEM) challenge event in March 2013 selected by the <em>Quantitative Assessment of Radiation Belt Modeling</em> focus group. The original copy of the data is available from <a href="https://drive.google.com/drive/u/0/folders/0ByNhSbWkAgdfaGt6TnJMcElhUTg">https://drive.google.com/drive/u/0/folders/0ByNhSbWkAgdfaGt6TnJMcElhUTg</a></p> <p> </p> <p>Data Providers:<br> Michael G. Henderson (LANL; mghenderson@lanl.gov)<br> Steven K. Morley (LANL; smorley@lanl.gov)</p> <p>This data product provides electron phase space density from the Van Allen Probes<br> ECT suite of instruments. The data are calculated similarly to the method described<br> in Morley et al. (2013), with some differences that are noted below.</p> <p>The files are provided in HDF5 format, so the files are self-describing and contain<br> ISTP-style metadata. The files should be directly readable with:<br> - SpacePy (http://sourceforge.net/p/spacepy)<br> - import the spacepy.datamodel module, use the function fromHDF5 to read the data<br> - Autoplot (http://autoplot.org)<br> - MatLab and IDL provide convience routines for reading HDF5</p> <p>Method<br> ------<br> Starting with directional differential flux data from HOPE, MagEIS and REPT, we<br> calculate the PSD as a function of energy, pitch angle, position and time.<br> Following the same basic method given by Morley et al., we transform this to phase <br> space density as a function of the three adiabatic invariants (M, K, L*); note that<br> where Morley et al. used a relativistic Maxwellian fit to the flux spectrum, these<br> data use a smoothing spline fit so that more complex spectral shapes can be<br> represented. Note also that Morley et al. only used REPT, where these files represent<br> the energy ranges of MagEIS and REPT, but also use HOPE to constrain the fit at low<br> energies.</p> <p>While the pitch angles are determined using the EMFISIS data, all three adiabatic <br> invariants are derived from a magnetic field model. These PSD data files use the<br> Tsyganenko and Sitnov (2005) model (aka TS04, T05 or TS05). The models were run using<br> the "definitive" Qin-Denton data files provided by the RBSP ECT-SOC. These files<br> should be made available through the QARBM google drive. </p> <p><br> Caveats<br> -------<br> These data should be considered preliminary. They have undergone a limited amount of<br> verification and prior to publication the data providers should be contacted. New<br> versions of these data may be generated at some point - we do not expect noticeable <br> changes to the data present.<br> Some gaps may be present in the files that are due to calculation of the adiabatic <br> invariants failing. The issues causing these gaps have been resolved in the underlying <br> software, but the data have not yet been regenerated.</p> <p><br> References<br> ----------<br> Morley, S. K., M. G. Henderson, G. D. Reeves, R. H. W. Friedel, and D. N. Baker (2013), <br> Phase Space Density matching of relativistic electrons using the Van Allen Probes: REPT results,<br> Geophys. Res. Lett., 40, 4798-4802, doi:10.1002/grl.50909.</p> <p>Tsyganenko, N. A., and M. I. Sitnov (2005), <br> Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms, <br> J. Geophys. Res., 110, A03208, doi:10.1029/2004JA010798.</p> <p> </p> <p>Also included is the copy of the LANLgeoMag software used in the paper provided on <a href="https://github.com/drsteve/LANLGeoMag">https://github.com/drsteve/LANLGeoMag</a> </p> <p>Copyright (c) 2014, Los Alamos National Security, LLC All rights reserved. Copyright 2014. Los Alamos National Security, LLC. This software was produced under U.S. Government contract DE-AC52-06NA25396 for Los Alamos National Laboratory (LANL), which is operated by Los Alamos National Security, LLC for the U.S. Department of Energy. The U.S. Government has rights to use, reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR LOS ALAMOS NATIONAL SECURITY, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is modified to produce derivative works, such modified software should be clearly marked, so as not to confuse it with the version available from LANL.</p> <p>Additionally, redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of Los Alamos National Security, LLC, Los Alamos National Laboratory, LANL, the U.S. Government, nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY LOS ALAMOS NATIONAL SECURITY, LLC AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL LOS ALAMOS NATIONAL SECURITY, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p><br> </p>
Data from: Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020
<p>Background: Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (R<sub>t</sub>) of SARS-CoV-2 in Tunisia.</p> <p>Methods: We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of R<sub>t</sub>.</p> <p>Results: 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, w<span>e estimated large changes in </span>R<sub>t</sub><span> in response to the combined lockdown interventions. The </span>R<sub>t</sub><span> moves from </span>3.18 [95% CI 2.73-3.69] <span>to 1.77 [95% CI 1.49-2.08] with </span>curfew<span> prevention measure, and under the epidemic threshold (0.89 </span>[95% CI 0.84-0.94]) by national lockdown measure<span>.</span></p> <p><span>Conclusions: </span>Overall, our findings highlight contribution of <span>interventions</span> to interrupt transmission of SARS-CoV-2 in Tunisia.</p>
Data from: Dynamic models for impact-initiated stress waves through snow columns
<p>The objective of this research is to model snow's response to dynamic, impact loading. Two constitutive relationships are considered: elastic and Maxwell-viscoelastic. These material models are applied to laboratory experiments consisting of 1000 individual impacts across 22 snow column configurations. The columns are 60 cm tall with a 30 cm by 30 cm cross-section. The snow ranges in density from 135-428 kg m<sup>-3</sup> and is loaded with both short-duration (~1 ms) and long-duration (~10 ms) impacts. The Maxwell-viscoelastic model more accurately describes snow's response because it contains a mechanism for energy dissipation, which the elastic model does not. Furthermore, the ascertained model parameters show a clear dependence on impact duration; shorter duration impacts resulted in higher wave speeds and greater damping coefficients. The stress wave's magnitude is amplified when it hits a stiffer material because of the positive interference between incident and reflected waves. This phenomenon is observed in the laboratory and modeled with the governing equations.</p>
Frequency multiplication by collective nanoscale spin wave dynamics
<p>This dataset contains all primary data used in the manuscript.</p>
Dataset for the EPSL article: Structure and dynamics of the Tonga subduction zone: new insight from P-wave anisotropic tomography
<p>The obtained 3-D P-wave anisotropic and isotropic velocity models in the Tonga subduction zone.</p> <p>Please find this article: Z. Yu*, D. Zhao and J. Li*. Structure and dynamics of the Tonga subduction zone: New insight from P-wave anisotropic tomography. Earth and Planetary Science Letters, https://doi.org/10.1016/j.epsl.2022.117844<br> </p>
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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.