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31 results for “nonlinear effects”
Series data for the 22PN nonlinear memory effect in the l=2, m=0 mode.
<p>Dataset associated with the preprint arXiv:2407.19017, “Waveform models for the gravitational-wave memory effect: Extreme mass-ratio limit and final memory offset” by Arwa Elhashash and David A. Nichols. It contains the 22 post-Newtonian-order series data for the l=2, m=0 spin-weighted spherical harmonic mode of the gravitational-wave memory signal from an extreme-mass ratio inspiral with nonspinning black holes.</p>
Experimental Verification of Nonlinear Effects in Peak Limiting Current Mode Controlled Boost Converter
<p>Compressed file contains results of experimental verification of nonlinear effects in peak limiting current mode controlled boost converter. Circuit diagrams are given in files <strong>power.pdf</strong> and <strong>control.pdf</strong> in the root directory. Directories</p> <p><strong>M-2019-05-02-8kHz-12V</strong></p> <p><strong>M-2019-05-02-8kHz-17V</strong></p> <p><strong>M-2019-05-02-8kHz-24V</strong></p> <p><strong>M-2019-05-02-8kHz-29V</strong></p> <p>contain the experimental results for Vout=12V, Vout=17V, Vout=24V, and Vout=29V. Each directory contains .npy files with numerical data, figures recorded by the oscilloscope, figure that depicts measured output current of the converter as it depends on the assigned control voltage, and programs that controlled the measurements. Out of 15009 files in each directory 15003 files are oscilloscope recordings, <strong>Iout.npy</strong> contains measured output current data, <strong>Vg.npy</strong> contains assigned control voltage data, <strong>figure.py</strong> is a Python 2 program used to plot Iout(Vg), which is stored in <strong>figure-xy.pdf</strong> (xy stands for the actual output voltage), while <strong>oscusb.py</strong> and <strong>overjm.py</strong> are programs used to control the experiment. Experiments are controlled by a computer running under GNU/Linux operating system.</p>
Analysis of Nonlinear Effects in Peak Limiting Current Mode Controlled Converters
<p>This compressed file contains simulation programs, simulation results, figures that depict the results, as well as the animations that illustrate nonlinear effects in peak limiting current mode controlled converters. The simulations are performed using Python 2 programming language using pylab environment (numpy, scipy, matplotlib, ipython) under Ubuntu 18.04 operating system.</p>
Data from: Deconstruction of a plant-arthropod community reveals influential plant traits with nonlinear effects on arthropod assemblages
1. Studies of herbivores and secondary consumer communities rarely incorporate a comprehensive characterization of primary producer trait variation, thus limiting our understanding of how plants mediate community assembly of consumers. 2. We took advantage of recent technological developments for efficient generation of phytochemical, microbial, and genomic data to characterize individual alfalfa plants (Medicago sativa; Fabaceae) growing in an old-field, semi-naturalized state for 770 traits (including 753 chemical features). Using random forest modeling, we investigated the effect of variation in these traits on arthropod and fungal assemblages while accounting for plant genetic structure. 3. We found that traits indicative of plant vigor, including size, percentage of flowering stems, and leaf area, were positively associated with arthropod richness and abundance. Most phytochemicals were, by comparison, poor predictors, although phytochemical diversity and several individual phenolic compounds were important. Plants with a higher proportion of flowering stems were hotspots of inter-trophic interactions with higher species richness of secondary consumers. The effects of many traits on plant-associated assemblages were best modeled as nonlinear functions, often incorporating threshold effects. Foliar fungal richness was not well predicted by our models, suggesting we have much to learn regarding the role of plant traits on phyllosphere fungi at small spatial scales. 4. Our results support the need for characterization of multiple axes of plant phenotypes in studies of plant-arthropod-microbe communities, and demonstrate the value of modern analytical techniques for understanding the nonlinear ways in which plant traits mediate the structure of associated biotic communities.
Architectural effects regulate resource allocation within the inflorescences with nonlinear blooming patterns
<p>These data were generated to investigate the resource allocation pattern within the inflorescence of <em>Salvia przewalskii</em>, a perennial herb with 4-ouvle ovary flowers and flowering sequence-floral position decoupled inflorescences. Spatial and temporal resource allocations within inflorescences have been well-studied in many plants based on flowering sequence or floral position. However, there have been few attempts to investigate architectural effects and resource competition in species where the blooming pattern does not follow a linear positional pattern within the inflorescence. Moreover, most flowering plants show female-biased sex allocation in early or basal flowers, but it is unclear in species with inherent and changeless ovule production. The data demonstrated that pollen production and dry mass deceased from bottom to top flowers but didn't significantly differ following flowering sequence, resulting in male-biased sex allocation in basal flowers. The seed production, fruit set, bud developmental exhibited significant declining trends from proximal to distal positions regardless of the thinning and pollen treatments. Meanwhile, the seed production, fruit set, bud developmental success did not significant difference when thinning conducted according to flowering sequence. Thus, architectural effects plays a crucial role in resource allocation within decoupled flowering inflorescences. Moreover, the inherent floral traits, such as changeless ovule production, may modify architectural effects on sex allocation. </p>
Data that are used to Explain the Forcing Efficacy with Pattern Effect and Feedback Nonlinearity"
<p> This dataset is for the draft "Explaining the Forcing Efficacy with Pattern Effect and Feedback Nonlinearity".</p> <p> The netcdf file "last150yravg_picontrol.nc" represents the time-average fields for the last 150 years of PI-control experiment. Netcdf files beginning with "last20yravg" represent the time-average fields for the last 20 years (Year131-150) of abrupt forcing experiments. Note that "0p5co2" in the file name denotes 0.5xCO2, "4psolar" denotes +4% solar radiation, and "m2psolar" denotes -2% solar radiation.</p> <p> The netcdf files beginning with "fixedsst" represent the time-average fields for fixed-SST experiments, where the file "fixedsst_control.nc" denotes the fixed SST control experiment.</p> <p> The netcdf files beginning with "uni" represent the time-average fields for uniform warming/cooling experiments, where the magnitude of SST change is indicated by the file names (m2K denotes -2K).</p> <p> The text file "GFA_partialR_over_partial_SST" denote the value of partial_R over partial_SST for each grid, and its grid (96x144) is same as other netcdf files in this datasets .</p> <p> </p>
Trial Comparing the Effects of Linear Versus Nonlinear Aerobic Training in Women With Operable Breast Cancer
ClinicalTrials.gov study NCT01186367. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Architectural effects regulate resource allocation within the inflorescences with nonlinear blooming patterns
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Disentangling the nonlinear effects of habitat complexity on functional responses
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Data from: Enhancement of the linear and nonlinear planar Hall effect by altermagnets on the surface of topological insulators
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Data from: Deconstruction of a plant-arthropod community reveals influential plant traits with nonlinear effects on arthropod assemblages
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Data from: Nonlinear effects of intraspecific competition alter landscape-wide scaling up of ecosystem function
A major focus of ecology is to understand and predict ecosystem function across scales. Many ecosystem functions are only measured at local scales, while their effects occur at a landscape level. Here, we investigate how landscape-scale predictions of ecosystem function depend on intraspecific competition, a fine-scale process, by manipulating intraspecific density of shredding macroinvertebrates and examining effects on leaf litter decomposition, a key function in freshwater ecosystems. For two species, we found that per-capita leaf processing rates declined with increasing density following power functions with negative exponents, likely due to interference competition. To demonstrate consequences of this nonlinearity, we scaled up estimates of leaf litter processing from shredder abundance surveys in 10 replicated headwater streams. In accordance with Jensen's inequality, applying density-dependent consumption rates reduced estimates of catchment-scale leaf consumption by an order of magnitude relative to density-independent rates. Density-dependent consumption estimates aligned closely with metabolic requirements in catchments with large, but not small, shredder populations. Importantly, shredder abundance was not limited by leaf litter availability and catchment-level leaf litter supply was much higher than estimated consumption. Thus leaf litter processing was not limited by resource supply. Our work highlights the need for scaling-up which accounts for intraspecific interactions.
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>
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>
Multi-level warming alters energy flux by nonlinear effects on soil nematode trophic groups in a mature forest
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Data for "Nonlinear and non-monotonic effect of ocean tidal mixing on exoplanet climates and habitability"
<p>Dataset analysed in order to obtain the results published in "Nonlinear and non-monotonic effect of ocean tidal mixing on exoplanet climates and habitability":</p> <ul> <li>IGCM_data: atmospheric data obtained by using the flux programme on the outcome of the standard FORTE2.0 climate simulations (instellation = 1.00 insolation)</li> <li>MOMA_standard_runs: oceaninc data of the standard FORTE2.0 climate simulations (instellation = 1.00 insolation)</li> <li>MOMA_reduced_runs: oceaninc data of the reduced FORTE2.0 climate simulations (instellation = 0.90, 0.85, 0.80 insolation)</li> </ul> <p>The FORTE2.0 code, compilation instructions and example run scripts, together with all necessary ancillary files, are accessible at <a href="https://doi.org/10.5281/zenodo.4108373">doi.org/10.5281/zenodo.4108373</a> (<a href="https://gmd.copernicus.org/articles/14/275/2021/#bib1.bibx6">Blaker et al.</a>, <a href="https://gmd.copernicus.org/articles/14/275/2021/#bib1.bibx6">2020</a>). </p>
Data and scripts from: Experimental evidence of size-selective harvest and environmental stochasticity effects on population demography, fluctuations, and nonlinearity
<p class="MsoNormal">Theory and analyses of fisheries datasets indicate that harvesting can alter population structure and destabilize nonlinear processes, which increases population fluctuations. We conducted a factorial experiment on the population dynamics of <em>Daphnia magna</em> in relation to size-selective harvesting and stochasticity of food supply. Harvesting and stochasticity treatments both increased population fluctuations. Timeseries analysis indicated that fluctuations in control populations were nonlinear, and nonlinearity increased substantially in response to harvesting. Both harvesting and stochasticity induced population juvenescence, but harvesting did so via depletion of adults whereas stochasticity increased the abundance of juveniles. A fitted fisheries model indicated that harvesting shifted populations towards higher reproductive rates and larger-magnitude damped oscillations that amplify demographic noise. These findings provide experimental evidence that harvesting increases nonlinearity of population fluctuations and that both harvesting and stochasticity increase population variability and juvenescence.</p>
Linear-to-Circular Polarization Conversion with Full-Silica Meta-Optics to Reduce Nonlinear Effects in High-Energy Lasers - Nature Com - Dataset
<p>Data from the Nature Communication paper entitled "Linear-to-Circular Polarization Conversion with Full-Silica Meta-Optics to Reduce<br> Nonlinear Effects in High-Energy Lasers"</p> <p>.OPJU files can be opened with ORIGINLab</p> <p>. MAT files can be opened with MATLAB/SCILAB</p>
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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.