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638 results for “oscillations”
X-ray diffraction images recorded for Aumonier et al., (2022) Slow protein dynamics probed by time-resolved oscillation crystallography at room temperature, IUCrJ
<p>The present repository contains diffraction images corresponding to 27 distinct datasets collected at room temperature on the ESRF beamline ID30A-3 using an Eiger X 4M detector.</p> <p>Datasets have been uploaded with their original names to maintain the metadata integrity. The two following tables match the original names with those attributed in the supplementary table S1 of Aumonier et al., IUCrJ (2022) (https://doi.org/10.1107/S2052252522009150).</p> <table> <tbody> <tr> <td> <p>Data set name on Zenodo</p> </td> <td> <p>X06_01</p> </td> <td> <p>X12_05</p> </td> <td> <p>X07_02_</p> </td> <td> <p>X06_08</p> </td> <td> <p>X14_06</p> </td> <td> <p>X13_03</p> </td> <td> <p>X08_06</p> </td> <td> <p>X11_05</p> </td> <td> <p>X13_05</p> </td> <td> <p>X06_02</p> </td> <td> <p>X11_01</p> </td> <td> <p>X08_01</p> </td> <td> <p>X14_01</p> </td> <td> <p>X13_01</p> </td> <td> <p>X06_03</p> </td> </tr> <tr> <td> <p>Data set in Aumonier et al. 2022</p> </td> <td> <p>Dark</p> </td> <td> <p>PS2</p> </td> <td> <p>PS2</p> </td> <td> <p>PS3</p> </td> <td> <p>PS4</p> </td> <td> <p>PS5</p> </td> <td> <p>PS6</p> </td> <td> <p>PS7</p> </td> <td> <p>R<sub>2”</sub></p> </td> <td> <p>R<sub>3”</sub></p> </td> <td> <p>R<sub>7”</sub></p> </td> <td> <p>R<sub>10”</sub></p> </td> <td> <p>R<sub>13”</sub></p> </td> <td> <p>R<sub>21”</sub></p> </td> <td> <p>R<sub>35”</sub></p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p>Data set on Zenodo</p> </td> <td> <p>X08_02</p> </td> <td> <p>X11_02</p> </td> <td> <p>X12_02</p> </td> <td> <p>X14_02</p> </td> <td> <p>X13_04</p> </td> <td> <p>X13_02</p> </td> <td> <p>X12_06</p> </td> <td> <p>X06_09</p> </td> <td> <p>X09_04</p> </td> <td> <p>X12_04</p> </td> <td> <p>X06_07</p> </td> <td> <p>X13_07</p> </td> </tr> <tr> <td> <p>Data set in Aumonier et al. 2022</p> </td> <td> <p>R<sub>51”</sub></p> </td> <td> <p>R<sub>62”</sub></p> </td> <td> <p>R<sub>62”</sub></p> </td> <td> <p>R<sub>67”</sub></p> </td> <td> <p>R<sub>72”</sub></p> </td> <td> <p>R<sub>80”</sub></p> </td> <td> <p>R<sub>90”</sub></p> </td> <td> <p>R<sub>130”</sub></p> </td> <td> <p>R<sub>166”</sub></p> </td> <td> <p>R<sub>258”</sub></p> </td> <td> <p>R<sub>630”</sub></p> </td> <td> <p>R<sub>1620”</sub></p> </td> </tr> </tbody> </table> <p>One dataset consists of a master file, four data files and two metadata files.</p>
On the role of the ocean in the Atlantic multidecadal oscillation
<p>Atlantic multidecadal Oscillation (AMO) index and its surface-forced and ocean dynamics-forced components in a fully-coupled and partially-coupled experiments</p>
Vortex shedding topology for oscillating heavy spherical pendulums underwater
<p>Relevant data of vortex shedding topology for oscillating heavy spherical pendulums underwater to reproduce the major findings of the article "Dynamics of heavy subaqueous spherical pendulums" published in Journal of Fluid Mechanics. The article has been published as open access: <a href="https://doi.org/10.1017/jfm.2023.170">https://doi.org/10.1063/5.0086557</a></p>
Exceptional electronic transport and quantum oscillations in thin bismuth crystals grown inside van der Waals materials
<p>Confining materials to two-dimensional forms changes the behavior of electrons and enables new devices. However, most materials are challenging to produce as uniform thin crystals. Here, we present a synthesis approach where thin crystals are grown in a nanoscale mold defined by atomically-flat van der Waals (vdW) materials. By heating and compressing bismuth in a vdW-mold made of hexagonal boron nitride (hBN), we grow ultraflat bismuth crystals less than 10 nanometers thick. Due to quantum confinement, the bismuth bulk states are gapped, isolating intrinsic Rashba surface states for transport studies. The vdW-molded bismuth shows exceptional electronic transport, enabling the observation of Shubnikov–de Haas quantum oscillations originating from the (111) surface state Landau levels. By measuring the gate-dependent magnetoresistance, we observe multi-carrier quantum oscillations and Landau level splitting, with features originating from both the top and bottom surfaces. Our vdW-mold growth technique establishes a platform for electronic studies and control of bismuth’s Rashba surface states and topological boundary modes. Beyond bismuth, the vdW-molding approach provides a low-cost way to synthesize ultrathin crystals and directly integrate them into a vdW heterostructure. </p>
Tracking the neurodevelopmental trajectory of beta band oscillations with OPM-MEG
<p>Optically pumped magnetometer magnetoencephalography (OPM-MEG) data were acquired during a somatosensory task using a 192-channel OPM-MEG device which is adaptable to head size and robust to participant movement.</p> <p>This dataset contains data from individuals aged between 2 and 34 years.</p> <p>Analyses and descriptions of the dataset were published in eLife (https://doi.org/10.7554/eLife.94561.1)</p> <p>Defaced, T1-weighted MR images are provided for each participant. These were generated using an individualized template anatomy approach where age-matched template MRIs were warped to an optical 3D scan of each individual's head-shape.</p> <p>Data were compressed using zip on Windows.</p> <p>Matlab (2022b) scripts used for data loading and analysis can be found on (https://github.com/LukasRier/RierRhodes_2024_Neurodevelopmental_OPMMEG)</p>
High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range - data
<div> <p>Experimental and simulation data from the paper "High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range".</p> <p> </p> </div>
Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations (3rd version)
<p><strong>Summary</strong></p> <p>This package contains data and processing tools for replicating the research presented in the paper "Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations" (2018, ApJ, DOI: <a href="https://doi.org/10.3847/1538-4357/aac88f">10.3847/1538-4357/aac88f</a>, <a href="https://arxiv.org/abs/1604.04631">arXiv:1604.04631</a>).</p> <p>The compressed archive file "ddmc-nosample-v3.1.tar.xz" contains only the compressed SNIa data, the BAO measurements, and 3rd-party data files used in this work. The random samples can be re-created by the tools included in the package. This is the file suitable for low-speed download.</p> <p>The file "ddmc-v3.1.tar.xz" contains the full set of random sample output files and analysis results in addition to those in the "ddmc-nosample-v3.1.tar.xz" file. This is the archive containing all the data and figure files used directly in the paper.</p> <p>To uncompress the files, the XZ Utils software package is required.</p> <p>The file "CHECKSUM.asc" is a GPG-clearsigned text file containing the SHA-512 checksum values for file integrity verification. The text file itself is signed with the GPG key 0xE977A6E990102402 available from keyservers.</p> <p>Please read the README files in each package for more details and instructions.</p> <p><strong>Release notes for version 3.1</strong></p> <p>Version 3.1 is a minor revision with the addition of some alternative input parameter distributions.</p> <p><strong>Release notes for version 3</strong></p> <p>This is the 3rd version representing a re-written analysis of the distance-duality test. This new version updated and renamed the complementary parameter (CP) sets to match the ones used in the paper. New results concerning the interpretation of results as a diagnostics of distance measurement systematics are presented. Also included are updated utility scripts, new tests for Gaussian approximation to the results, and new data-visualization scripts.</p> <p><strong>Earlier versions</strong></p> <p>Earlier versions are available from Zenodo. Links: <a href="https://doi.org/10.5281/zenodo.49825">v1</a>, <a href="https://doi.org/10.5281/zenodo.57982">v2</a>.</p>
New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.
<p>This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.</p>
A synthetic sample of short-cadence solar-like oscillators for TESS
<p>These are the simulated lightcurves for a synthetic sample of short-cadence solar-like oscillators as they might be observed by the Transiting Exoplanet Survey Satellite (TESS), as presented by Ball et al. (2018), "A synthetic sample of short-cadence solar-like oscillators for TESS". </p> <p>Each zip file contains the FITS lightcurves and mode data for stars observed in one sector, starting in the southern ecliptic hemisphere. The CSV file contains a selection of data contained in the FITS headers.</p> <p>For more detail, read the paper on <a href="https://arxiv.org/abs/1809.09108">arXiv</a>.</p> <p>Note that the FITS headers incorrectly report the units of the white noise level as <span class="math-tex">\(\mathrm{ppm}\)</span> rather than <span class="math-tex">\(\mathrm{ppm}\cdot\mathrm{hr}^{1/2}\)</span>. The white noise level excludes any systematic component, which should be added in quadrature if desired.</p>
The Impact of White Dwarf Luminosity Profiles on Oscillation Frequencies
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018ApJ...867L..30T/abstract">The Impact of White Dwarf Luminosity Profiles on Oscillation Frequencies</a></p> <p> </p>
The wavelength-dependent complex refractive index of hygroscopic aerosol particles and other aqueous media: an effective oscillator model
<p>Using cavity-enhanced Raman spectroscopy of single, optically trapped droplets, we measure both the real and imaginary parts of the refractive index of NaCl, NaNO<sub>3</sub>, (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>, MgSO<sub>4</sub>, citric acid and a 1:1 molar ratio NaCl:NaNO<sub>3</sub> aqueous solutions at a range of water activities. </p>
Tributary bay oscillations generated by diurnal discharge regulation in Three Gorges Reservoir
<p>The dataset include absolute water level (in m.a.s.l.) from six gauging stations along Three Gorges Reservoir and discharge data for the period January to November 2018. And the relative water level and vertical profiles of flow velocity were measured in the middle (Xiakou) and upper reach (Pingyikou) of Xiangxi bay by two RDI ADCP from September 16 - October 12, 2018. For water level, water depth and flow velocity, the respective time series were high-pass filtered with cut-off frequencies corresponding to periods of 36 h and 4 h (see excel files), respectively (half-power frequencies of a zero-phase, 20-pole Butterworth filter). Power spectra were calculated using Welch’s method. Horizontal current velocities were measured in earth coordinates and rotated into longitudinal (along the river channel) and transversal velocity components by rotation into the respective mean (depth and temporarily averaged) flow direction at the sampling sites (see 'long_trans_vel.mat'). </p>
Buoyancy Waves in Earth's Nightside Magnetosphere: Normal-Mode Oscillations of Thin Filaments
<ul> <li>A theory has been developed for small oscillations of a thin filament in the magnetosphere.</li> <li>For the lowest-frequency even modes, the eigenfunctions are essentially buoyancy waves in the plasma sheet, but they are more like slow modes in the inner magnetosphere.</li> <li>For the lowest-frequency even modes, the eigenfrequencies (radians/s) have peak values of approximately 0.07 s<sup>-1</sup> between the inner plasma sheet and plasmapause, for an average magnetospheric field. </li> <li>This includes software and model data used in this paper.</li> <li>Submitted to JGR for review</li> </ul>
Surrogate Modeling Benchmark - Damped Oscillator
<p>This dataset is related to the damped oscillator benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-damped-oscillator/" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-damped-oscillator/</a>.</p> <p>The experimental designs include datasets with 400, 800, 1200, 1600, and 2000 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.</p> <h2>Description of the dataset file</h2> <p>The dataset file includes two variables:</p> <ul> <li><em>ExpDesigns</em>, and</li> <li><em>ValidationSet</em>.</li> </ul> <p>Both variables are Matlab structures with fields <em>X</em>, <em>Y</em>, and <em>nSamples</em>. Variable <em>ExpDesigns</em> is a non-scalar structure sized according to the number of experimental design groups. Each field of <em>X</em> for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the <em>ValidationSet</em> variable, except it contains only one dataset per benchmark case.</p> <p>The structure can be summarized as follows:</p> <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> <p>The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.</p> <p>For <strong>Polynomial Chaos Expansions (PCE)</strong>, we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).</p> <p><strong>Polynomial Chaos Kriging (PCK)</strong> employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.</p> <p>We benchmarked both linear and ordinary <strong>Kriging</strong>, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.</p> <p>For further details on the settings, please refer to the competitors.m file and UQLab user manuals:</p> <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> <p>The results file contains one variable: <em>Metrics</em>. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:</p> <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l)<br> <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> <p>The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>.</p> <h2>Additional files</h2> <p>We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called <em>working_with_datafiles.*</em> (the extension depends on the selected language).</p> <h2>Acknowledgment</h2> <p>This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.</p>
Surrogate Modeling Benchmark - Undamped Oscillator
<p>This dataset is related to the undamped oscillator benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-undamped-oscillator/" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-undamped-oscillator/</a>.</p> <p>The experimental designs include datasets with 40, 80, 120, 160, and 200 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.</p> <h2>Description of the dataset file</h2> <p>The dataset file includes two variables:</p> <ul> <li><em>ExpDesigns</em>, and</li> <li><em>ValidationSet</em>.</li> </ul> <p>Both variables are Matlab structures with fields <em>X</em>, <em>Y</em>, and <em>nSamples</em>. Variable <em>ExpDesigns</em> is a non-scalar structure sized according to the number of experimental design groups. Each field of <em>X</em> for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the <em>ValidationSet</em> variable, except it contains only one dataset per benchmark case.</p> <p>The structure can be summarized as follows:</p> <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> <p>The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.</p> <p>For <strong>Polynomial Chaos Expansions (PCE)</strong>, we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).</p> <p><strong>Polynomial Chaos Kriging (PCK)</strong> employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.</p> <p>We benchmarked both linear and ordinary <strong>Kriging</strong>, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.</p> <p>For further details on the settings, please refer to the competitors.m file and UQLab user manuals:</p> <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> <p>The results file contains one variable: <em>Metrics</em>. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:</p> <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l)<br> <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> <p>The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>.</p> <h2>Additional files</h2> <p>We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called <em>working_with_datafiles.*</em> (the extension depends on the selected language).</p> <h2>Acknowledgment</h2> <p>This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.</p>
Data from: Low-frequency oscillations in the magnetic nozzle of a Helicon Plasma Thruster (10 sccm case)
<p>- Data from: Low-frequency oscillations in the magnetic nozzle of a Helicon Plasma Thruster (10 sccm case)</p> <p>- Authors: Davide Maddaloni, Borja Bayón-Buján, Jaume Navarro-Cavallé, Mario Merino, Filippo Terragni</p> <p>- Contact email: <a href="mailto:dmaddalo@ing.uc3m.es">dmaddalo@ing.uc3m.es</a></p> <p>- Date: 2024-09-13</p> <p>- Version: 1.0.0</p> <p>- License: This dataset is made available under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a></p> <h2>Abstract</h2> <p>This dataset contains the raw experimental data employed in:</p> <p>Davide Maddaloni, Borja Bayón-Buján, Jaume Navarro-Cavallé, Mario Merino, Filippo Terragni, "Low-frequency oscillations in the magnetic nozzle of a Helicon Plasma Thruster", Plasma Sources Science and Technology, DOI: <a href="https://iopscience.iop.org/article/10.1088/1361-6595/adc40d/meta">10.1088/1361-6595/adc40d</a>.</p> <p>The data within this dataset collect only the 10 sccm case due to file size limits. A companion dataset is available at <a href="https://zenodo.org/records/13913729">10.5281/zenodo.13758358</a>, collecting the data for the 5 sccm case.</p> <h2>Dataset description</h2> <p>The experimental data is gathered by means of three distinct floating Langmuir Probes (LPs).</p> <p>The time-resolved data is provided separately for every spatial position inspected. The data is collected by means of an AC-coupled Yokogawa DLM5058 Mixed Signal Oscilloscope, resolving frequencies up to 50 MHz. The time-averaged I-V curve data obtained by sweeping the LPs is postprocessed according to the routine illustrated in <a href="https://arc.aiaa.org/doi/10.2514/1.B35531">Lobbia et al</a>.</p> <p>Please refer to the corresponding article for further details regarding the data collection (Section 3).</p> <h2>Data files</h2> <p>The data files are in standard Matlab .mat format. A recent version of <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> is recommended.</p> <p>For the time-resolved results, data is subdivided according to the spatial position inspected and the xenon injected mass flow rate. The nomenclature of the files follows the structure "WaveData_[injected mass flow rate]_[axial position]_[angular position]". The file collects the raw waveform data file, labeled "WaveData", and the corresponding time array, labeled "t". For each saptial position within the plume, and for each probe, a total of 3 different waveforms were collected, and each waveform includes 50'000'000 points. The time array also contains 50'000'000 points and is the same for each waveform and/or probe. </p> <p>Each WaveData variable is structured in a 3-dimensional matrix, where:</p> <ul> <li>The first dimension represents the time evolution of the waveform (size 50'000'000)</li> <li>The second dimension represents a different waveform (size 3)</li> <li>The third dimension represents each a different probe (size 3) <ul> <li>Probe 1 along the first position</li> <li>Probe 2 along the second position</li> <li>Probe 3 along the third position</li> </ul> </li> </ul> <p>Please refer to the corresponding article for details regarding the probe labeling and their configuration (Section 3).</p> <p>The time-resolved data is provided in a structure array, containing several fields of two-dimensional matrices with dimensions 21x11 of relevant plasma parameters, as follows:</p> <ul> <li>n: plasma density (in m^-3)</li> <li>Vp: plasma potential (in V)</li> <li>Bx: component along X of the magnetic field (in T)</li> <li>By: component along Y of the magnetic field (in T)</li> <li>X: axial meshgrid coordinate (in mm)</li> <li>Y: radial meshgrid coordinate (in mm)</li> </ul> <p>The electron temperature is not included, since it is considered to be constant within the corresponding article. For the 10 sccm case, it corresponds to 7 eV.</p> <h2>Citation</h2> <p>Works using this dataset or any part of it in any form shall cite it as follows.</p> <p>The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available.</p> <p>Optionally, the dataset may be cited directly by referencing the corresponding DOI: 10.5281/zenodo.13915163.</p> <h2>Acknowledgments</h2> <p>This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project ERC-STG ZARATHUSTRA, grant agreement No 950466). Additionally, F. Terragni was also supported by the FEDER / Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (grant agreement No PID2020-112796RB-C22), while B. Bayón-Buján enjoyed a grant from the Consejería de Educación, Universidades, Ciencia y Portavocía of the Community of Madrid (grant PEJ-2021-AI/TIC-23158).</p>
Data from: Low-frequency oscillations in the magnetic nozzle of a Helicon Plasma Thruster (5 sccm case)
<p>- Data from: Low-frequency oscillations in the magnetic nozzle of a Helicon Plasma Thruster (5 sccm case)</p> <p>- Authors: Davide Maddaloni, Borja Bayón-Buján, Jaume Navarro-Cavallé, Mario Merino, Filippo Terragni</p> <p>- Contact email: <a href="mailto:dmaddalo@ing.uc3m.es">dmaddalo@ing.uc3m.es</a></p> <p>- Date: 2024-09-13</p> <p>- Version: 1.1.0</p> <p>- License: This dataset is made available under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a></p> <h2>Abstract</h2> <p>This dataset contains the raw experimental data employed in:</p> <p>Davide Maddaloni, Borja Bayón-Buján, Jaume Navarro-Cavallé, Mario Merino, Filippo Terragni, "Low-frequency oscillations in the magnetic nozzle of a Helicon Plasma Thruster", Plasma Sources Science and Technology, DOI: <a href="https://iopscience.iop.org/article/10.1088/1361-6595/adc40d/meta">10.1088/1361-6595/adc40d</a>.</p> <p>The data within this dataset collect only the 5 sccm case due to file size limits. A companion dataset is available at <a href="https://doi.org/10.5281/zenodo.13915163">10.5281/zenodo.13915162</a>, collecting the data for the 10 sccm case.</p> <h2>Dataset description</h2> <p>The experimental data is gathered by means of three distinct floating Langmuir Probes (LPs).</p> <p>The time-resolved data is provided separately for every spatial position inspected. The data is collected by means of an AC-coupled Yokogawa DLM5058 Mixed Signal Oscilloscope, resolving frequencies up to 50 MHz. The time-averaged I-V curve data obtained by sweeping the LPs is postprocessed according to the routine illustrated in <a href="https://arc.aiaa.org/doi/10.2514/1.B35531">Lobbia et al</a>.</p> <p>Please refer to the corresponding article for further details regarding the data collection (Section 3).</p> <h2>Data files</h2> <p>The data files are in standard Matlab .mat format. A recent version of <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> is recommended.</p> <p>For the time-resolved results, data is subdivided according to the spatial position inspected and the xenon injected mass flow rate. The nomenclature of the files follows the structure "WaveData_[injected mass flow rate]_[axial position]_[angular position]". The file collects the raw waveform data file, labeled "WaveData", and the corresponding time array, labeled "t". For each saptial position within the plume, and for each probe, a total of 3 different waveforms were collected, and each waveform includes 50'000'000 points. The time array also contains 50'000'000 points and is the same for each waveform and/or probe. </p> <p>Each WaveData variable is structured in a 3-dimensional matrix, where:</p> <ul> <li>The first dimension represents the time evolution of the waveform (size 50'000'000)</li> <li>The second dimension represents a different waveform (size 3)</li> <li>The third dimension represents each a different probe (size 3) <ul> <li>Probe 1 along the first position</li> <li>Probe 2 along the second position</li> <li>Probe 3 along the third position</li> </ul> </li> </ul> <p>Please refer to the corresponding article for details regarding the probe labeling and their configuration (Section 3).</p> <p>The time-resolved data is provided in a structure array, containing several fields of two-dimensional matrices with dimensions 21x11 of relevant plasma parameters, as follows:</p> <ul> <li>n: plasma density (in m^-3)</li> <li>Vp: plasma potential (in V)</li> <li>Bx: component along X of the magnetic field (in T)</li> <li>By: component along Y of the magnetic field (in T)</li> <li>X: axial meshgrid coordinate (in mm)</li> <li>Y: radial meshgrid coordinate (in mm)</li> </ul> <p>The electron temperature is not included, since it is considered to be constant within the corresponding article. For the 5 sccm case, it corresponds to 12.5 eV.</p> <h2>Citation</h2> <p>Works using this dataset or any part of it in any form shall cite it as follows.</p> <p>The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available.</p> <p>Optionally, the dataset may be cited directly by referencing the corresponding DOI: 10.5281/zenodo.13913729.</p> <h2>Acknowledgments</h2> <p>This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project ERC-STG ZARATHUSTRA, grant agreement No 950466). Additionally, F. Terragni was also supported by the FEDER / Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (grant agreement No PID2020-112796RB-C22), while B. Bayón-Buján enjoyed a grant from the Consejería de Educación, Universidades, Ciencia y Portavocía of the Community of Madrid (grant PEJ-2021-AI/TIC-23158).</p>
Dataset for manuscript "El Niño Southern Oscillation and Tropical Basin Interaction in Idealized Worlds"
<p>Dataset accompanying the manuscript<br>Dommenget and Hutchinson, 2025: El Niño Southern Oscillation and Tropical Basin Interaction in Idealized Worlds,<strong> Climate Dynamics, </strong>63, p274 doi: <a href="https://doi.org/10.1007/s00382-025-07759-9">10.1007/s00382-025-07759-9</a></p> <p>This dataset contains the following tar files:<br>control-a55c1.tar<br>coralsea.tar<br>hermanito2.tar<br>inf-trop.tar<br>solo100.tar<br>solo150.tar<br>solo200.tar<br>solo250.tar<br>solo300.tar<br>solo350.tar<br>solo50.tar<br>trio120.tar<br>trio160.tar<br>trio200.tar<br>trio.tar<br>twins.tar<br><br></p> <p>Each tar file is a folder corresponding to an experiment, described in the manuscript. There are 4 subfolders in each experiment:<br>ancil <br>input <br>results <br>scripts</p> <p><br>The `results` folder contains post-processed data which forms the analysis in the manuscript. These are in compressed netcdf form, with self-describing variables.<br>The `scripts` folder contains a `create.ancil.files.*` script, which was used to generate the input boundary conditions, and a `gfdl-run.cold.start.*` script, which was used to run the experiment on Gadi (nci.org.au).<br>The `ancil` and `input` folders contain various input files that are used as inputs to each experiment. These inputs are included for reproducibility, but are by no means easy to understand without expert knowledge of the GFDL model.</p> <p>We anticipate that the `results` folder is self-explanatory, while the `ancil`, `input` and `scripts` folders are not easy to understand unless you have experience with running GFDL CM2.1. Contact the authors if you want information on how to run the experiments using these inputs.</p>
Oscillations of Offshore Wind Turbines undergoing Installation II: Filtered and Integrated data - acceleration, velocity, displacement
<p>This is dataset is based on the raw measurement data from <a href="https://zenodo.org/record/5009061">https://zenodo.org/record/5009061</a></p> <p>The data included in the archives are the resampled and high-pass filtered accelerations as well as the velocity and displacement data.</p>
Laboratory data of measurements conducted on an n-decane saturated limestone sample using the forced-oscillation method
<p>This supporting information provides the numerical results of the laboratory experiments conducted on an n-decane saturated limestone sample with varying dead fluid volume. The supporting information includes: (1) the extensional attenuation, Poisson ratio, elastic moduli and strains in the rock measured at 0.1 Hz with the dead volume varying from 2 ml to 260 ml and also with the open fluid line, and (2) the frequency dependences of the attenuation, elastic moduli, Poisson ratio and strains obtained in the frequency range from 0.1 Hz to 120 Hz.</p>
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International Brain Laboratory public data
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OpenNeuro
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