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307 results for “nonlinearity”
Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design
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Reproducing nonlinear ground response and pore pressure variations using in-situ soil properties
<p>The zip file named 'Seismic and pore water pressure data.zip' contains all the seismic and pore water pressure data in our study.</p><p>The zip file named 'WLA model.zip' contains the numerical models used in our study.</p>
Global mixing and transport of solar wind and magnetospheric ions in the nonlinear Kelvin-Helmholtz region across the terrestrial dayside magnetopause
<p>Data of figs 1-6 of the manuscript "Global mixing and transport of solar wind and magnetospheric ions in the nonlinear Kelvin-Helmholtz region across the terrestrial dayside magnetopause".</p>
Linear and Nonlinear refractive index of ITO(n&k)
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Nonlinear Dataset
<p>The nonlinear dataset was created to evaluate the performance of machine learning algorithms in identifying complex nonlinear patterns. This dataset was derived by regenerating the Slovak exact cropped dataset into a new nonlinear form.</p> <p>The Slovak exact cropped dataset comprises 527 images of four tree species: European beech (Fagus sylvatica L.), Sessile oak (Quercus petraea (Matt.) Liebl.), Norway spruce (Picea abies (L.) H. Karst.), and European silver fir (Abies alba Mill.). Of these, 321 images were selected to generate the nonlinear dataset, including 80 images from each of the first three species and 81 images from the last. These images were captured in Včelien, Banskobystrický, Slovakia, in June 2022.</p> <p>The dataset features high-resolution images taken from a distance of approximately half a meter from the trees. Each tree is represented by 15-22 images, taken from various ground angles to provide a comprehensive view of the bark. Consecutive images maintain a minimum of 60% overlap, ensuring a robust representation of each tree's bark texture.</p>
Effects of nonlinear observation operators for visible and infrared radiances in ensemble data assimilation
<p><br>Description</p> <p>Dataset to accompany the first revision of the manuscript "Effects of nonlinear observation operators for visible and infrared radiances in ensemble data assimilation" for the QJRMS.</p> <p>Contains <br>- experiments (one folder per experiment)<br> each contains <br> - diagnostics (DART format) for each assimilation time<br> - obs_seq.final: assimilation output <br> - obs_seq.final-linear: contains linear posterior as "prior" ("evaluate" at +1s after analysis)<br> - obs_seq.final-evaluate: at analysis time (includes DART clamping), at 1s after analysis time: "nonlinear posterior"<br> - config file DART-WRF for each experiment (python format)</p> <p>- code<br> assim_tools_mod.f90: DART code modification to compute the linear posterior</p> <p>- other data: see v1 of this repository!<br> nature run initial conditions (WRF format)<br> forecast ensemble initial conditions (WRF format)<br> script to read "obs_seq.final"-files into pandas.DataFrame (python format)</p> <p><br>Experiment naming:<br>VIS ... visible reflectance 0.6 micrometer assimilation<br>WV73 ... infrared 7.3 micrometer assimilation<br>obs10 ... observation density: 1 observation per 10x10 km of domain area<br>obs30 ... 1 observation per 30x30 km<br>loc10 ... localization radius: 10 km<br>inf0 ... no prior nor posterior covariance inflation in the assimilation<br>sec0 ... no sampling error correction<br>reject ... not assimilating observations with a small first-guess departure <= 0.03</p> <p><br>Experiment names used in Figures</p> <p>Section 3.1<br>- Figure 2a: exp_v1.23_P2_rr+1_VIS_obsi211_loc20_inf0<br>- Figure 2b: exp_v1.23_P2_rr+1_VIS_obsi360_loc20_inf0</p> <p>Section 3.2.1 <br>- Figure 3: exp_v1.23_P2_rr+1_VIS_obs30_loc14_inf0</p> <p>Section 3.2.2<br>- Figure 4: exp_v1.23_P2_rr+1_VIS_obs10_loc20, exp_v1.23_P2_rr+1_VIS_obs10_loc20_reject</p> <p>Section 3.3<br>- Figures 5a,c: exp_v1.23_P2_rr+1_T2M_obs10_loc20_inf0_sec0<br>- Figures 5b,5d,7a: exp_v1.23_P2_rr+1_VIS_obs10_loc20_inf0_sec0<br>- Figure 6a,6b,7b: exp_v1.23_P2_rr+1_WV73_obs10_loc20_inf0_sec0</p> <p> </p>
Enhancement of the Ensemble Nonlinear Least Squares Algorithm for i4DVar
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Simulation data of Nonlinear Signatures of VLF-Triggered Emissions: A Simulation Study
<p>This dataset is obtained from <a href="http://space.rish.kyoto-u.ac.jp/software/">KEMPO1</a> code with minor modifications.</p>
Dataset of "Nonlinear Wave-Particle Interaction is Suppressed by Realistic Properties of Chorus Waves"
<p>Dataset of "Nonlinear Wave-Particle Interaction is Suppressed by Realistic Properties of Chorus Waves", including waveforms of the input PIC-generated chorus wave field (WF_{PIC}) recorded at certain magnetic latitudes, and the evolution of electrons' equatorial pitch angle with magnetic latitude.</p>
Data for "Higher fidelity simulations of nonlinear Breit-Wheeler pair creation in intense laser pulses"
<p>Data required to reproduce figures and analysis in "Higher fidelity simulations of nonlinear Breit-Wheeler pair creation in intense laser pulses", <a href="https://arxiv.org/abs/2108.10883">arXiv:2108.10883</a> [hep-ph] (2021)</p>
Nonlinear time series analysis of the interaction between the citrus whitefly and the whitefly-specialist ladybird
<p>A comprehensive understanding of the top-down effects of natural enemies on agricultural pests is essential for achieving effective biological control in integrated pest management. However, it is typically difficult to identify causal effects between the interacting species from time series data, which have often been monitored for pest forecasting purposes in agricultural ecosystems, as it is likely to involve nonlinear (state-dependent) population dynamics. In this study, we applied a recently developed framework of nonlinear time series analysis (empirical dynamic modeling) to determine top-down and bottom-up effects between the citrus whitefly <i>Dialeurodes citri</i> and the whitefly-specialist ladybird <i>Serangium japonicum</i>. We used weekly monitoring data for the two species collected over 4 years in pesticide-free citrus groves located in Shizuoka Prefecture, central Japan. Although we were able to identify time-delayed positive effects of <i>D. citri </i>abundance on <i>S. japonicum</i> abundance, we failed to detect any significant causal effects of <i>S. japonicum</i> abundance on <i>D. citri</i> abundance. Moreover, weather variables (temperature and rainfall) were found to have only a negligible effect on the population dynamics of the two species. Our findings indicate that bottom-up rather than top-down effects predominate in the weekly dynamics of this predator–prey system. On the basis of these observations, we discuss whether <i>S. japonicum</i> would be an effective agent for the biological control of <i>D. citri</i> in open field systems.</p>
Results from "The Effects of Nonlinear Signal on Expression-Based Prediction Performance"
<p>This zipped archive contains the result files from running the pipeline described in the manuscript "The Effects of Nonlinear Signal on Expression-Based Prediction Performance".</p>
Input data from "The Effects of Nonlinear Signal on Expression-Based Prediction Performance"
<p>These files are a 1GB fragments of a compressed archive containing the data used in the manuscript "The Effects of Nonlinear Signal on Expression-Based Prediction Performance".</p> <p>To simplify the uploading process, the file was split into chunks using the `split` utility in Linux. They can be joined back together with the command `cat input_data* > input_data.tar.gz`</p> <p>All data used is either publicly available or generated by this project. The subsets of the Recount3 and GTEx that we used are not owned by us, so putting them in this creative commons repository should not be construed as re-licensing them. </p> <p> </p>
Model weights from "The Effects of Nonlinear Signal on Expression-Based Prediction Performance"
<p>This file stores a representative set of saved models from the manuscript "The Effects of Nonlinear Signal on Expression-Based Prediction Performance". </p> <p>To simplify the uploading process, the file was split into chunks using the `split` utility in Linux. They can be joined back together with the command `cat model_weights* > model_weights.tar.gz`</p> <p>These saved files correspond to the weights and optimizer state of models trained on various biological tasks. They can be "rehydrated" using the `load_model` function of the associated model class from this repo: https://github.com/greenelab/linear_signal</p>
Nonlinear Development of Ecosystem Suppressed by Seasonal Synchronization in the North Pacific
<p>The dataset is to produce the main figures and tables of the work Nonlinear Development of Ecosystem Suppressed by Seasonal Synchronization in the North Pacific</p>
Supplementary material - Label-free multimodal nonlinear optical microscopy reveals hallmarks of bone composition in pathophysiological conditions
<p>The repository features Excel worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: </p> <ul> <li>Figure 4. SHG intensity values measured from WT and KO murine spines counterparts, both in parallel and orthogonal light polarization with respect to the craniocaudal axis of the vertebrae.</li> <li>Figure 5A. Quantization of ColIa1 gene expression level after mRNA extraction from WT and Dpp3 KO mice.</li> <li>Figure 5B. Absorbance values at 540 nm after Sirius Red staining of primary osteoblast isolated from WT and Dpp3 KO neonatal calvaria, used to assess and compare the in-vitro collagen production of the two murine models.</li> <li>Figure 7. SRS intensity ratios measured between bone and bone marrow regions of WT and Dpp3 KO models, both at the 2850 cm-1 Raman mode of CH2 stretching in lipids and at the 2920 cm-1 Raman mode of CH3 stretching in proteins.</li> <li>Figure 8. Data of gene expression analysis of selected genes relevant for lipid transport, uptake, and metabolism (i.e., CD 36, Fabp4, Lrp1, Fatp1, PGC1, CPT1, Pex7 and Glut1), in the flushed bone of WT and Dpp3 KO mice.</li> </ul>
Supporting data of A versatile mass-sensing platform with tunable nonlinear self-excited microcantilevers
<p><span>Supporting data for publication "</span>A versatile mass-sensing platform with tunable nonlinear self-excited microcantilevers"</p>
Dataset for the manuscript: Nonlinear Coupling of Kinetic Alfven Waves and Ion Acoustic waves in the Inner Heliosphere
<ol> <li>The file ef00 is the magnetic field profile data written for every t=0.5 starting from 0 to 200 with 128 by 128 grid point in space. It is used to produce Figure 1 of the paper plotted by using matlab m file filaments.m.</li> <li>The file dx00 is the density data taken at x=0 at different z. Figure 2 of the paper is plotted by matlab file density.m</li> <li>The data file esz0 is uesd in wavenumber.m to plot figure 3 of the paper</li> <li>The data file ekx0 is used to plot figure 4 in fullspectra.m </li> <li>The data file ekz0 is used to plot figure 5 in matlab m file bkspectkx.m</li> <li>The file eekz0 is used to plot figure 6 in the matlab m file spectrakx.m</li> <li>The matlab code heat.m is used to plot figure 7 of the paper</li> </ol>
Data and code for "Combining ultrahigh index with exceptional nonlinearity in resonant transition metal dichalcogenide nanodisks."
<div># Data and code for "Combining ultrahigh index with exceptional nonlinearity in resonant transition metal dichalcogenide nanodisks."</div> <div> </div> <div>## Contents</div> <div> </div> <div>* `chi2-dft`: DFT calculations of the chi(2) nonlinear susceptibility tensor</div> <div>* `shg-exp`: explicit spectra of the SHG from a flake and resonant disks</div> <div> </div> <div>## Description of the data</div> <div> </div> <div>The data are organized as follows:</div> <div> </div> <div>* `chi2-dft/plot_chi2.{py,m}`: plotting scripts for python and matlab</div> <div>* `chi2-dft/shg-3R-MoS2-chi2.*`: binary dataset for plotting chi2 and plot thereof</div> <div>* `chi2-dft/nlo`: folder with scripts for data reproduction and structure file</div> <div>* `chi2-dft/nlo/shg-lg`: folder with chi2 tensor elements, numpy format; used for plotting with python</div> <div> </div> <div>* `shg-exp/disk-specs`: data and matlab script to plot explicit SHG spectra of the disks</div> <div>`shg-exp/disk-specs/D*_***nm_1s_03mW.asc/` - D* - disk number 1,2,3; ***nm - pump wavelength 810-910-1020nm; 1s - signal collection time in seconds;03mW - pump power in mW</div> <div>* `shg-exp/flake-specs`: data and matlab script to plot explicit SHG spectra and log-log power dependence of the thin flake</div> <div>`shg-exp/flake-specs/***nm_*.*mw_0.5s_H8_3R_MoS2.asc/` - ***nm - pump wavelength 800:10:1040nm; *.*mW - pump power in mW; 0.5s - signal collection time in seconds;</div> <div> </div> <div>## Reproduction of the data</div> <div> </div> <div>The DFT calculations were performed using GPAW (https://wiki.fysik.dtu.dk/gpaw/index.html) and the nonlinear optical response approach (https://wiki.fysik.dtu.dk/gpaw/tutorialsexercises/opticalresponse/nlopt/nlopt.html). The scripts are in a form suitable for cluster calculations and use global WALLTIME/STARTTIME parameters. Calculation of the SHG spectrum is set up such that one call of the script in a cluster environment will run one tensor element calculation and then exit. The calculations of the data consist of the following steps:</div> <div> </div> <div>1. Ground-state (gs) calculation:</div> <div> * `chi2-dft/nlo/gs.py` -- plane wave calculations for the `rlx-3R-MoS2-bulk.xyz` structure file using the plane wave mode, 500 eV cutoff, PBE xc-functional. Other parameters are in the `setting.py` file.</div> <div> * Generates the `gs.gpw` file.</div> <div>2. Calculation of unoccupied bands:</div> <div> * Requires finished ground-state calculation.</div> <div> * `chi2-dft/nlo/unocc.py`</div> <div> * This step is optional is the required number of bands calculated in the `gs` step is sufficient.</div> <div> * Generates the `unocc.gpw` file.</div> <div>3. Calculate the matrix elements:</div> <div> * Requires finished ground-state calculation (`gs.gpw`)</div> <div> or</div> <div> * unoccupied bands calculation (`unocc.gpw`)</div> <div> * `chi2-dft/nlo/mommat.py`</div> <div> * Generates the `mml.npz` file.</div> <div>4. Calculation of the SHG spectrum:</div> <div> * Requires the calculated matrix elements (`mml.npz` file).</div> <div> * `chi2-dft/nlo/shg.py` -- computes one chi2 tensor element from the `mml.npz` file. Designed to run in a HPC environment. Will look for the first nonexistent `abc` tensor element in folder and calculate it. Rerun the script to calculate additional ones or rewrite the script to calculate all required elements in one loop.</div> <div> </div>
Virtual Furuta pendulum: linear, nonlinear and AI-based controllers implementation (non-perturbed case)
<p><span>This video shows simulations of the control of the virtual prototype of the Furuta pendulum in a MATLAB/Simulink environment controlled by linear, nonlinear, and AI-based controllers in the absence of external disturbance.</span> </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.