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237 results for “sensitivity analysis”
Dataset for "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions"
<p>This dataset is a part of the paper "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions", accepted for publication in the Journal of Advances in Modeling Earth Systems (JAMES).</p> <h2>Contents</h2> <p>This dataset includes:</p> <ul> <li><strong>lhs-gen-190.csv</strong>: Training input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>lhs-gen-50-test.csv</strong>: Test input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>190-elm-samples.csv</strong>: Training input perturbed parameter samples for performing ELM simulations.</li> <li><strong>50-elm-test-samples.csv</strong>: Test input perturbed parameter samples for performing ELM simulations.</li> <li><strong>train_CH-CHA.csv</strong>: Contains the five ELM simulation output flux values for 240 samples (190 train + 50 test).</li> <li><strong>lhsgen.py</strong>: Script for generating Latin Hypercube Samples.</li> <li><strong>gpr-fit-new.py</strong>: Script for fitting Gaussian Process Regression (GPR) models.</li> <li><strong>sobol-new.py</strong>: Script for performing Sobol sensitivity analysis.</li> </ul> <h2>Usage</h2> <ol> <li><strong>lhsgen.py</strong>: <ul> <li>Use this script to generate the Latin Hypercube Samples for parameter sampling.</li> </ul> </li> <li><strong>gpr-fit-new.py</strong>: <ul> <li>This script fits GPR models using the training samples provided in <code>lhs-gen-190.csv</code>.</li> <li>It tests the models using the input testing samples in <code>lhs-gen-50-test.csv</code>.</li> <li>The fitted GPR models are stored as <code>.joblib</code> files in the <code>gpr_models</code> directory.</li> <li>Corresponding cross-validation and R-squared values are stored in <code>.xlsx</code> files.</li> </ul> </li> <li><strong>sobol-new.py</strong>: <ul> <li>This script performs Sobol sensitivity analysis using the fitted GPR models by reading the .joblib files.</li> <li>The Sobol indices are written to <code>.xlsx</code> files in the <code>results</code> directory.</li> </ul> </li> </ol>
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
<p>Result Files for the Paper "A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations" to be published at IEEE Vis 2024</p>
The "social brain" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study
<p><strong>Abstract</strong></p> <p>How the human brain process social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies. Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates. It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by general social information. In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images. Results of 1,000 published fMRI studies containing the keyword of “social” were subject to an automated meta-analysis (neurosynth.org). To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study. The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element. The social – non-social contrast in the independent study showed a strong resemblance of the NeuroSynth map. ROI analyses revealed that a social effect was credible in 8 out of the 11 NeuroSynth regions in the independent dataset. The findings support that the “social brain” is highly sensitive to the mere presence of social information. </p>
Sensitivity analysis of ice wedge temperature to polygonal microtopography
<p>This repository contains input files, model output, and postprocessing scripts from a sensitivity analysis of ice wedge temperature to polygonal rim height and trough depth. Simulations are constructed in Amanzi-ATS (https://github.com/amanzi/ats), v. 0.86. See the included README file for instructions using this content.</p> <p>This analysis is presented in:</p> <ul> <li>Abolt CJ, Young MH, Atchley AL, Harp DR. 2018. Microtopographic control on the ground thermal regime in ice wedge polygons. <em>The Cryosphere</em>, 12, 1957-1968. DOI:10.5194/tc-12-1957-2018.<br> </li> </ul>
Data, analysis scripts, simulations, and schematics files for "Position Sensitive Alpha Detector for an Associate Particle Imaging System"
<p>Measured data and analysis script, as well as, simulated data, input scripts, as well as, schematics for the readout board for our publication "Position Sensitive Alpha Detector for an Associate Particle Imaging System"</p>
Sensitivity Analysis of Gas-Phase Aromatic Chemistry in TMC-1
<p>This repository contains a number of files related to the gas-phase kinetic sensitivity analysis presented and described in Byrne et al. 2024 (in prep). The raw abundances as a function of time for all analyses are contained in 'Sensitivity_raw.hdf5.zip', while the sensitivities are located in 'Sensitivity_data.hdf5.zip'. 'Sens_Walkthrough.ipynb' is a jupyter notebook that explains how to examine this data using functions defined in 'ranking.py' and 'plotting.py'. This notebook also requires the two reactions.in files. Finally, the input files used to generate the data along with the NAUTILUS modeling code can be found in 'Model-Inputs.zip'. These inputs primarily come from the development branch of cixue/BATMAN with minor changes described in a README file.</p>
Data for manuscript: "Understanding lower limb haemodynamics: sensitivity analysis of a 0D model"
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Data and analysis and plotting scripts for Swaminathan et al., "Regional Impacts Poorly Constrained by Climate Sensitivity"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Regional Impacts Poorly Constrained by Climate Sensitivity", by Ranjini Swaminathan, Jacob Schewe, Jeremy Walton, Klaus Zimmermann, Colin Jones, Richard A. Betts, Chantelle Burton, Chris D. Jones, Matthias Mengel, Christopher Reyer, Andrew G. Turner & Katja Weigel, submitted for publication in Earth's Futures. Scripts used for plotting and analysis are also included.</p>
Data for publication of "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning"
<p>The data are made available as part of the paper "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning", submitted to Geoscientific Model Development. This data set incorporates selected post-processed files needed to reproduce the results presented in the paper.</p> <p>The data contains six zip files, that are:</p> <ul> <li>Namelist.input files for the WRF model simulations of ten tropical cyclones</li> <li>WRF model simulation outputs using the default parameter values</li> <li>WRF model simulation outputs using the optimal parameter values (which give minimum RMSE value)</li> <li>IMDAA surface observations and IMERG precipitation data</li> <li>IMD observed tracks of ten tropical cyclones</li> <li>Ipython notebooks of sensitivity analysis and machine learning codes</li> </ul> <p>The remaining files are the ncl scripts that were used to obtain the figures. The ncl scripts used the data in the zip files.</p>
Fig. 3. Sensitivity analysis graphic. Y in Total Evidence, Sequence Alignment, Evolution of Polychrotid Lizards, and a Reclassification of the Iguania (Squamata: Iguania)
Fig. 3. Sensitivity analysis graphic. Yaxis represents the logarithm of the ratio of transversion: transition weights. The xaxis represents the logarithm of the ratio of the indel cost versus the maximal cost of a molecular change. The colors represent the zaxis, which is congruence between the molecular and morphological data partitions. Red is good, blue is bad.
Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
<p>The study associated with this dataset proposes a way of performing age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations. Here, you find simulation code in R to produce figures in the manuscript and matrices reporting demographic data upon which code computations are performed.</p>
Early-life food stress hits females harder than males in insects: a meta-analysis of sex differences in environmental sensitivity
<p><span>Fitness consequences of early-life environmental conditions are often sex-specific, but corresponding evidence for invertebrates remains inconclusive. Here we use meta-analysis to evaluate sex-specific sensitivity to early-life nutritional conditions in insects. Using literature-derived data for 85 species with broad phylogenetic and ecological coverage, we show that </span><span>females are generally more sensitive to food stress than males. Stressful nutritional conditions during development typically lead to female-biased mortality and thus increasingly male-biased sex ratios of emerging adults. We further demonstrate that the general trend of higher sensitivity to food stress in females can primarily be attributed to their typically larger body size in insects and hence higher energy needs during development. By contrast, there is no consistent evidence of sex-biased sensitivity in sexually size-monomorphic species. Drawing conclusions regarding sex-biased sensitivity in species with male-biased size dimorphism remains to wait for the accumulation of relevant data. Our results suggest that environmental conditions leading to elevated juvenile mortality may potentially affect the performance of insect populations further by reducing the proportion of females among individuals reaching reproductive age. Accounting for sex-biased mortality is therefore essential to understanding the dynamics and demography of insect populations, not least importantly in the context of ongoing insect declines.</span></p>
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets
<p>This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.</p> <p>Every folder contains two csv files:</p> <p>- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis</p> <p>- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample</p> <p>In ventricular_cell_model, there are four folders:</p> <p>- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features</p> <p>- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).</p> <p>- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).</p> <p>- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.</p> <p>The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.</p> <p>The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.</p> <p>The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.</p> <p>The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.</p> <p>Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.</p> <p>The details about the model can be found in the original publication.</p>
Sensitivity analysis script for: Why so many polyploids?
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Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
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Early-life food stress hits females harder than males in insects: a meta-analysis of sex differences in environmental sensitivity
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Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM)
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Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach
All data are used in the following manuscript which is in prep Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach Calef et al.
Global Sensitivity Analysis is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design
<p>This Zenodo repository contains all the results generated for the book chapter "Global Sensitivity Analysis is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design".</p>
GSA-GxE: A Framework of Global Sensitivity Analysis of Maize Coupled with Genetics by Environments (GxE) Model
<p>We present the coupled Global Sensitivity Analysis (GSA) and Genetics by Environment model (GxE) framework (Sarzaeim and Muñoz-Arriola, submitted). GSA-GxE uses the sensitivity analysis method PAWN (Pianosi and Wagener, 2015) coupled with the environmental covariance matrix used in GxE modeling (Jarquin et al., 2014). GSA-GxE estimates the relative sensitivity of maize yield predictability to hydroclimate variables that interact with maize genetics from the environmental covariances and genetic marker structures. We include hydroclimate variables like temperature (T), solar radiation (SR), rainfall (R), and relative humidity (RH). The data, codes, and scripts presented here were used to develop and test the GSA-GxE framework. They were built upon an enhanced version of the multi-dimensional Genomes to Fields (G2F) database consisting of maize genetic, phenotypic, environmental, and metadata in 84 field experiments in 2014-2017 across the U.S. and province of Ontario, Canada (Sarzaeim et al., 2020, 2022, 2023). This digital package contains a multi-dimensional Climate and Omics dataset, the GSA-GxE framework created in Python, and the GxE model developed in R.</p> <p><strong>Acknowledgement</strong></p> <p>This work was supported by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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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.