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849 results for “linear”
AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.
<p>Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article.<br> <br> Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.</p>
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
QuaLiKiz-v2.6.2 linear instability spectra based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/spectrum", and "/wavenumber". The '/input' key contains the inputs used for the QuaLiKiz evaluations, representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. The "/spectrum" key contains the linear growth rate and frequency spectra corresponding to the 2 most dominant microinstabilities determined by the calculation (s0 = dominant, s1 = sub-dominant). The "/wavenumber" key contains an array representing the standard set of 18 wavenumbers (<span class="math-tex">\(k_y \rho_s\)</span>) was used to generate the spectra (k0 = lowest wavenumber, k17 = highest wavenumber).</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>
Experimental data aquisition of the U-LEAF Linear Fresnel Collector of the Cyprus Institute
<p>The file contains the experimental data for 50+ (non continuous) days of operation of the <a title="Linear Fresnel Reflector of the Cyprus Institute" href="https://energy.cyi.ac.cy/facilities/fresnel/">LFR</a> at the Cyprus Institute. This is a csv file type. The data set is divided in several columns: </p> <ul> <li><strong>Date</strong>: Year, Month, Day, Hours, Minutes, Seconds <em>as per the aquisition time-steps of the Master PLC of the Linear Fresnel Collector<br></em></li> <li><strong>Solar position</strong>: Azimuth of the sun (Azimuth =0 at South), Elevation of the sun (0 from the horizon) <em>calculated based on NOAA algorithm fitting the Master PLC aquisition time-steps for the location of the Linear Fresnel collector <br></em></li> <li><strong>IAM (Incidence Angle Modifiers)</strong>: calculated from ray tracing software (Tonatiuh) <em> based on PLC aquisition steps</em></li> <li><strong>DNI (Direct Normal Irradiance):</strong> as measured by the pyrheliometer (LP Pyhre 16 AC with EKO STR 21G tracker)</li> <li><strong>Weather station data</strong>: Ambient Temperature, Pressure, GHI, Humidity, Wind velocity (<em>Davis Vantage pro 2</em>)</li> <li><strong>Reflectometry data</strong> as measured several time per week and interpolated in between (<em>D&S Portable Specular Reflectometer Model 15R-USB</em>)</li> <li><strong>Absorber measurements</strong>: measured inlet, measured outlet and average calculated temperatures <em>in the absorber as the the aquisition of the PLCs (TC MISURE E CONTROLLI PT100, Class 1/3)</em></li> <li><strong>Heat transfer Fluid characteristics</strong>: Cp and Density, Massflow obtained from the volumetric flow (Prowirl F200, 7F2B25, DN25 1"), Power absorbed <em>as calculated based on the Duratherm 450 datasheet</em></li> </ul> <p>The date is set by the aquisition by the master PLC that registers the date, the volumetric flowrate of the HTF, the inlet temperature and the outlet temperature. </p> <p>They are 4 data base merged together:</p> <ul> <li>The master PLC </li> <li>The weather station</li> <li>DNI</li> <li>Reflectometer</li> </ul> <p>The available data matches periods of time when the 4 database had data registered. Time-steps vary between 15 seconds and 30 seconds. </p> <p>For more information you can send an email to a.montenon@cyi.ac.cy</p>
Data for the manuscript "Enhanced microscopic dynamics in mucus gels under a mechanical load in the linear viscoelastic regime" (PNAS).
<p>Data files for the figures published in</p> <p>D. Larobina, A. Pommella, A.-M. Philippe, M. Y. Nagazi, and L. Cipelletti, <em>Enhanced Microscopic Dynamics in Mucus Gels under a Mechanical Load in the Linear Viscoelastic Regime</em>, Proc Natl Acad Sci USA <strong>118</strong>, e2103995118 (2021).</p> <p>DOI: 10.1073/pnas.2103995118</p> <p>Each data set is available as a plain text file (description in the file __README__DataDescription.txt), and as an Excel file.<br> The Excel files typically contain the data sets of several panels of a given figure, as separated sheets. See the description<br> provided in the "GeneralInfo" sheet of each Excel file.</p>
Data supplement to 'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150'
<p>This is a data supplement to <strong>'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150</strong>'. It presents a global-scale Vertical Land Motion (VLM) reconstruction that resolves height changes in the period 1995-2020. It is based on the joint probabilistic analysis of an extensive network of more than 11,000 GNSS stations, tide gauges, and satellite altimetry. The approach used to derive this reconstruction is described in the paper. The dataset variables are explained in the .pdf file.</p>
CSM08 Small mammal host-parasite sampling data for 16 linear trapping transects located in 8 LTER burn treatment watersheds at Konza Prairie
Data set contains summaries (summer) of the number of individuals of each species of small mammal captured (relative abundance) on each transect. Each record contains date, treatment, transect, trap station, species, specimen number, recapture status, specimen disposition, external body measurements (where applicable), reproductive information, and miscellaneous associated comments. These sampling records are based on nightly captures during one 4-night trapping period in summer (June through August) for each of 16 permanent transects established on eight fire treatments (two transects per treatment). These treatments include two seasonal burn watersheds (SpB, SuB), two reversal burn watersheds (R1A, R20A), one annual burn watershed (1D), two 4-year burn watersheds (4B, 4F, and one 20-year burn watershed (20B). None of these treatments implement bison grazing.
Investigating terrestrial isopod abundance in sandplain grassland using a multiple linear regression
<p>Most North American species of terrestrial isopod (Isopoda) have been introduced from Europe. Sandplain grassland is a globally rare habitat that is abundant on Nantucket Island, Massachusetts and the abundance of terrestrial isopods in the habitat has never been studied. The objective of this project was to develop a model to explain isopod abundance based on vegetation characteristics within Sandplain grassland and use this model to test for land management effects (prescribed burning and mowing) on isopod abundance. I counted terrestrial isopods from 175 pitfall traps set for one week and used multiple linear regression with several selection algorithms to select the best model. The vegetation characteristics I used as regressors do not appear to explain terrestrial abundance well and the final model only contains the percent grass coverage as a regressor. The model suggests that terrestrial isopods decrease in abundance with increasing grass coverage and it explains 29 percent of the data. When management effects are incorporated, the model suggests that mowing significantly increases isopod abundance.</p> <p>Funding for this project came from the Nantucket Islands Land Bank, Nantucket Land Council, and the Nantucket Biodiversity Initiative.</p> <p>Associated vegetation data is in the published "Effects of Sandplain Grassland Management on Spider Richness and Abundance on Nantucket Island" dataset. Sampling methods are in the thesis linked from that dataset.</p> <p>allisopodData.csv - isopod counts by trap<br> dataDictionary.csv - descriptions of variables<br> mckenna-foster_2009.pdf - a report submitted to NBI and used as part of a statistics class at the University of Wisconsin-Green Bay</p> <p> </p>
Dataset for "Helicity proxies from linear polarisation of solar active regions"
<p>The <span class="math-tex">\(\alpha\)</span> effect is believed to play a key role in the generation of the solar magnetic field. A fundamental test for its significance in the solar dynamo is to look for magnetic helicity of opposite signs in the two hemispheres, and at small and large scales. However, measuring magnetic helicity is compromised by the inability to fully infer the magnetic field vector from observations of solar spectra, caused by what is known as the <span class="math-tex">\(\pi\)</span> ambiguity of spectropolarimetric observations. We decompose linear polarisation into parity-even and parity-odd <em>E</em> and <em>B</em> polarisations, which are not affected by the <span class="math-tex">\(\pi \)</span> ambiguity. Furthermore, we study whether the correlations of spatial Fourier spectra of <em>B</em> and parity-even quantities such as <em>E </em>or temperature <em>T</em> are a robust proxy for magnetic helicity of solar magnetic fields. We analyse polarisation measurements of active regions observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics observatory. Theory predicts the magnetic helicity of active regions to have, statistically, opposite signs in the two hemispheres. We then compute the parity-odd <em>EB</em> and <em>TB</em> correlations, and test for systematic preference of their sign based on the hemisphere of the active regions. We find that: (i) <em>EB</em> and <em>TB</em> correlations are a reliable proxy for magnetic helicity, when computed from linear polarisation measurements away from spectral line cores, and (ii) <em>E</em> polarisation reverses its sign close to the line core. Our analysis reveals Faraday rotation to not have a significant influence on the computed parity-odd correlations. The <em>EB</em> decomposition of linear polarisation appears to be a good proxy for magnetic helicity independent of the <span class="math-tex">\(\pi\)</span> ambiguity. This allows us to routinely infer magnetic helicity directly from polarisation measurements.</p> <p>The full article can be found at https://arxiv.org/abs/2001.10884</p>
Linear Transformation
<p>This dataset is a csv file resulted from a linear transformation y = 3*x+6 of 1000 randomly generated number between 0 - 100. It was generated by applying a linear transformation on 1000 data points generated from random.randint() function.</p>
Binaural room impulse responses recorded with KEMAR of a 19-channel linear loudspeaker array
<p>BRIRs for 19 different loudspeakers placed in room Calypso at the Telefunken-building of TU Berlin were measured. The room is a studio listening room. The 19 loudspeakers constituted a linear loudspeaker array with a inter-loudspeaker distance of roughly 15cm. The measurement was done with the KEMAR (type 45BA) with the corresponding large ears (type KB0065 and KB0066) and Fostex PM0.4 loudspeakers. The dummy head was rotated from −90° to 90° in 1° steps. The measurement was repeated with the head wearing AKG K601 open headphones.</p>
An efficient not-only-linear correlation coefficient based on clustering: Supplementary Files
<p>Supplementary Files for the manuscript "An efficient not-only-linear correlation coefficient based on machine learning" available at https://doi.org/10.1101/2022.06.15.496326</p> <ul> <li>Supplementary File 1: All pairwise gene correlations using Pearson, Spearman and CCC among the top 5,000 genes in GTEx’s whole blood with the largest variance. Columns indicates whether the gene pair was categorized in the top or bottom 30% of each coefficient, the correlation value, and the significance of the association. Significance is only present for the top 10 gene pairs of each intersection in the “Disagreements” group (Figure 3a, right) where CCC disagrees with Pearson, Spearman or both.</li> <li>Supplementary File 2: Percentiles of the coefficient values for the top 5,000 genes in GTEx’s whole blood.</li> <li>Supplementary File 3: Pearson, Spearman and CCC correlations values and their significance for two gene pairs (<em>UTY</em> - <em>KDM6A</em> and <em>DDX3Y</em> - <em>KDM6A</em>) across all tissues in GTEx.</li> </ul>
Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars: Stationary solutions and modeling potential
<p>This repository contains the material available online that accompanies <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> (ArXiv link). </p> <p>It contains zipped archives that contain inlists and final data products for the MESA stellar evolution code\(^1\) (version 15140), the GYRE stellar pulsation/oscillation code\(^2\) (version 6.0.1) and the AESolver stellar oscillation mode coupling code\(^3\).</p> <p>In the technical information section below you may find a description of the contents of this repository. The abstract of <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> is also available below.</p> <p> </p> <p><em>Footnotes :</em></p> <p><em>\(^1\): see <a href="https://docs.mesastar.org/en/r15140/" target="_blank" rel="noopener">https://docs.mesastar.org/en/r15140/</a> for additional details about the MESA stellar evolution code.</em></p> <p><em>\(^2\): see <a href="https://gyre.readthedocs.io/en/v6.0.1/">https://gyre.readthedocs.io/en/v6.0.1/</a> for additional details about the GYRE stellar pulsation/oscillation code.</em></p> <p><em>\(^3\): the AESolver code can be downloaded from its Github repository: <a href="https://github.com/JVB11/AESolver" target="_blank" rel="noopener">https://github.com/JVB11/AESolver</a>; its documentation may be consulted at <a href="https://jvb11.github.io/AESolver/" target="_blank" rel="noopener">https://jvb11.github.io/AESolver/</a>.</em></p>
Numerical Data Set Belonging to: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'
<p>This is the numerical data set belonging to the Nureth conference paper entitled: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'. </p> <p>The files 'Stefan_singlePhase_Tfield.dat' and 'Stefan_singlePhase_interface.dat' represent the raw data belonging to figure 1 in the paper and contain the solution to the one-phase Stefan problem for the temperature field and interface position (section 3.1 in the paper). The files 'Stefan_singlePhase_error.dat' and 'Stefan_twoPhase_error.dat' represent the raw data belonging to figure 2 in the paper and contain the L2 norm of the relative difference between the numerical and analytical solution for the single and two phase Stefan problem respectively. </p> <p>The files 'Gau_1140s_lf_50x50_3D', 'Gau_1140s_lf_100x100_3D', 'Gau_1140s_lf_200x200_3D' feature the raw OpenFOAM(v7) data containing the numerical solution to the liquid fraction of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at 1140s of simulation time. These data were used for the mesh convergence study (figure 3, section 3.2). </p> <p>The files 'Gau_120s_U_200x200_3D', 'Gau_360s_U_200x200_3D', 'Gau_750s_U_200x200_3D', 'Gau_1140s_U_200x200_3D' feature the raw OpenFOAM(v7) data containing the 3-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at respectively 120s, 360s, 750s and 1140s of simulation time. These data underly the velocity colours shown in figure 4 and figure 6 (section 3.2).</p> <p>Likewise, the files 'Gau_120s_U_200x200_2D' and 'Gau_360s_U_200x200_2D' feature the raw OpenFOAM(v7) data containing the 2-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem. These data underly the velocity colours shown in figure 5 (section 3.2).</p> <p> </p> <p> </p>
Strain path change characterization of dual-phase steel (DP780) using non-linear strain path experiments: true strain-stress and equivalent strain-stress data
<p>Strain path change characterization of dual-phase steel (DP780) using non-linear strain path experiments: true strain-stress and equivalent strain-stress data.</p>
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
<p>This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data:</p> <ul> <li>adaptscore_acute.csv <ul> <li>A csv file that contains the estimated adaptation scores for the acute data set with HLA I model.</li> </ul> </li> <li>adaptscore_leftout.csv <ul> <li>A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training.csv <ul> <li>A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training_hla1_without_clin.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation)</li> </ul> </li> <li>adaptscore_training_seed2.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed</li> </ul> </li> </ul>
Datasets for the paper "A linear parameter-varying modelling approach for dielectric elastomer loudspeakers"
<p>This upload contains the numerical datasets used for the numerical plots reported in the paper "A linear parameter-varying modelling approach for dielectric elastomer loudspeakers" by G. Moretti et al., In <em>IFAC-PapersOnLine Volume 55, Issue 20, 2022</em> (<a href="https://doi.org/10.1016/j.ifacol.2022.09.150">https://doi.org/10.1016/j.ifacol.2022.09.150</a>).</p> <p>Please refer to the readme file for information on the files structure and content. </p>
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load('file.npy', allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> </li> </ul> </li> <li>processed_data: <ul> <li>'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>'Data_final.npy': file containing all computed quantities</li> <li>'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'.</li> </ul> </li> </ul> <p> Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>'lat': latitude, in degree</li> <li>'lon': longitude, in degree</li> <li>'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors</li> <li>'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity</li> <li>'direction': measured wind direction, in [degrees]</li> <li>'velocity': measured wind velocity, in [m/s]</li> <li>'orientaion': dune pattern orientation, [deg]</li> <li>'wavelength': dune pattern wavelength, [km]</li> <li>'z0_insitu': chosen hydrodynamic roughness for the considered station.</li> <li>'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction</li> <li>'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set</li> <li>'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset</li> <li>'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset</li> <li>'Temperature', 't': Temperature from the pressure levels ERA5 dataset</li> <li>'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset</li> <li>'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset</li> <li>'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Density': Density calculated from the pressure levels ERA5 dataset</li> <li>'height': Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>'theta_ground': Averaged virtual potential temperature within the ABL.</li> <li>'delta_theta': Virtual potential temperature at the ABL.</li> <li>'gradient_free_atm': Virtual potential temperature gradient in the FA.</li> <li>'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>'kH': time series of the number 'kH'</li> <li>'kLB': time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<br> </p>
VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic grids for interpolation
<p>VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic interpolation grids in ASCII format. The generation of these grids is described in http://doi.org/10.1007/s00190-015-0871-8</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.