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21 results for “Global Sensitivity Analysis”
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling - Supplementary Material
<p>Supplementary material for the manuscript "Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling".</p> <p>This deposit contains all data and visualization scripts needed to replicate results in the manuscript.This includes user created figures, model input files, model output files, configuration files for running the workflow, and all scripts needed to process results.</p> <p>In addition to the European Commission, we acknowledge that Trevor Barnes' contribution to this paper was funded via a Mitacs Globalink Research Award, grant number IT2569</p>
A Global Sensitivity Analysis of Parameter Uncertainty in the CLASSIC Model
<p>Input scripts, datasets and outputs used for the GSA methods. Please read the README and workflow files.</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>
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>
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>
Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study
<p>Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ± 40 % variation range for the UK emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub>, from which regression coefficients for each input-output combination and each model grid (>10,000 across the UK) were calculated. Surface concentrations of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO<sub>3</sub> and particulate SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (± 4 %, ± 10 % and ± 20 % respectively). The uncertainties in the surface concentrations of NH<sub>3</sub> and NO<sub>x</sub> and the depositions of NH<sub>x</sub> and NO<sub>y</sub> were dominated by the uncertainties in emissions of NH<sub>3</sub>, and NO<sub>x</sub> respectively, whilst concentrations of SO<sub>2</sub> and deposition of SO<sub>y</sub> were affected by the uncertainties in both SO<sub>2</sub> and NH<sub>3</sub> emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and HNO<sub>3</sub>) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.</p> <p>The dataset contains model outputs used for the sensitivity and uncertainty analyses.</p>
A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow
Open the record for dataset details and reuse information.
Tree germination sensitivity to increasing temperatures: a global meta-analysis across biomes, species and populations.
<p>The dataset contains the files used for the meta-analysis on the role of temperature increases on the germination of tree species from different biomes around the world.</p> <p>This meta-analysis is accepted for publication in Global Ecology and Biography (MS reference number: GEB-2024-0273.R1 ; Article DOI: 10.1111/geb.13921).</p> <p>Files S6 and S7 gather data of germination percentage and time, respectively, at population scale. File S5 is a summary of the publications used as data sources for the meta-analysis. The whole dataset comprises 50 papers addressing 63 species and 250 populations, it covers boreal, temperate, Mediterranean and tropical-subtropical biomes, and a time period between 1996 and 2024.</p>
Dataset for "Global Sensitivity Analysis of Nitric Oxide-Related Chemical Reaction Rates in the Global Ionosphere Thermosphere Model"
<p>This repository contains all the datasets and scripts used to generate the figures in the paper tilted "Global Sensitivity Analysis of Nitric Oxide-Related Chemical Reaction Rates in the Global Ionosphere Thermosphere Model". The data and scripts are organized according to the figure numbers to facilitate ease of use and reproducibility.</p> <p><strong>Directory Structure</strong><br>Each figure has its own dedicated folder. Inside each folder, you will find:</p> <p><strong>Data files: </strong>These files contain the dataset used to generate the corresponding figure.<br><strong>Scripts: </strong>Python or other relevant scripts needed to process the data and create the figure.<br><strong>README.txt: </strong>Each figure folder includes a separate README.txt file that provides detailed instructions on how to run the scripts.<br><strong>How to Use</strong><br>Navigate to a Figure's Folder: Locate the folder corresponding to the figure number you are interested in (e.g., Fig_01, Fig_02, etc.).</p> <p>Read the README.txt: Open the README.txt file in that folder. It contains specific instructions on how to execute the code and generate the figure, along with any necessary setup details.</p> <p><strong>Run the Scripts:</strong> Follow the instructions in the README.txt file to run the script(s) and generate the figure.</p>
Code and data for Global sensitivity analysis can unveil the hidden universe of uncertainty in multiverse studies
Open the record for dataset details and reuse information.
Implementing Riverine Biogeochemical Inputs in ECCO-Darwin: A Sensitivity Analysis of Terrestrial Fluxes in a Data-Assimilative Global Ocean Biogeochemistry Model
<p>Resolving riverine biogeochemical inputs in ocean biogeochemistry models is pivotal for capturing the spatiotemporal variability of nutrients and carbon in coastal regions and in the global ocean. ECCO-Darwin is a pioneering data-assimilative global-ocean biogeochemistry model, which, to date, has focused on the pelagic zone. In this work, we use an optimized version of ECCO-Darwin to perform a sensitivity analysis to quantify the response of the open ocean and coastal margins to lateral inputs of carbon and nutrients. We generate riverine inputs by combining point-source freshwater discharge from JRA55-do with the Global NEWS 2 watershed model, accounting for lateral inputs from 5171 watersheds worldwide. While adding carbon and nutrients along with freshwater improves biogeochemical skill in river plume regions and coastal waters, the open-ocean response may be overestimated due to an excess of carbon and nutrients advected offshore. This highlights the need for a more nuanced representation of land-to-ocean and nearshore processes for quantifying how global ocean primary production and carbon cycling respond to land-to-ocean inputs.</p>
Advanced methods for uncertainty assessment and global sensitivity analysis of a Eulerian atmospheric chemistry transport model
<p>Atmospheric chemistry transport models (ACTMs) are extensively used to provide scientific support for the development of policies to mitigate against the detrimental effects of air pollution on human health and ecosystems. Therefore, it is essential to quantitatively assess the level of model uncertainty and to identify the model input parameters that contribute the most to the uncertainty. For complex process-based models, such as ACTMs, uncertainty and global sensitivity analyses are still challenging and are often limited by computational constraints due to the requirement of a large number of model runs. In this work, we demonstrate an emulator-based approach to uncertainty quantification and variance-based sensitivity analysis for the EMEP4UK model (regional application of the European Monitoring and Evaluation Programme Meteorological Synthesizing Centre-West). A separate Gaussian process emulator was used to estimate model predictions at unsampled points in the space of the uncertain model inputs for every modelled grid cell. The training points for the emulator were chosen using an optimised Latin hypercube sampling design. The uncertainties in surface concentrations of O<sub>3</sub>, NO<sub>2</sub>, and PM<sub>2.5</sub> were propagated from the uncertainties in the anthropogenic emissions of NO<sub>x</sub>, SO<sub>2</sub>, NH<sub>3</sub>, VOC, and primary PM<sub>2.5</sub> reported by the UK National Atmospheric Emissions Inventory. The results of the EMEP4UK uncertainty analysis for the annually averaged model predictions indicate that modelled surface concentrations of O<sub>3</sub>, NO<sub>2</sub>, and PM<sub>2.5</sub> have the highest level of uncertainty in the grid cells comprising urban areas (up to ± 7%, ± 9%, and ± 9% respectively). The uncertainty in the surface concentrations of O<sub>3 </sub>and NO<sub>2</sub> were dominated by uncertainties in NO<sub>x</sub> emissions combined from non-dominant sectors (i.e. all sectors excluding energy production and road transport) and shipping emissions. Additionally, uncertainty in O<sub>3</sub> was driven by uncertainty VOC emissions combined from sectors excluding solvent use. Uncertainties in the modelled PM<sub>2.5</sub> concentrations were mainly driven by uncertainties in primary PM<sub>2.5</sub> emissions and NH<sub>3</sub> emissions from the agricultural sector. Uncertainty and sensitivity analyses were also performed for five selected grid sells for monthly averaged model predictions to illustrate the seasonal change in the magnitude of uncertainty and change in the contribution of different model inputs to the overall uncertainty. Our study demonstrates the viability of a Gaussian process emulator-based approach for uncertainty and global sensitivity analyses, which can be applied to other ACTMs. Conducting these analyses helps to increase the confidence in model predictions. Additionally, the emulators created for these analyses can be used to predict the ACTM response for any other combination of perturbed input emissions within the ranges set for the original Latin hypercube sampling design without the need to re-run the ACTM, thus allowing fast exploratory assessments at significantly reduced computational costs.</p> <p>The upload contains the uncertainty and sensitivity data together with the analysis scripts.</p>
Global gene expression analysis highlights microgravity sensitive key genes in soleus and EDL of 30 days space flown mice
Microgravity exposure as well as chronic muscle disuse are two of the main causes of physiological adaptive skeletal muscle atrophy in humans and murine animals in physiological condition. The aim of this study was to investigate at both morphological and global gene expression level skeletal muscle adaptation to microgravity in mouse soleus and extensor digitorum longus (EDL). Adult male mice C57BL/N6 were flown aboard the BION-M1 biosatellite for 30 days on orbit (BF) or housed in a replicate flight habitat on Earth (BG) as reference flight control. In this study we investigated for the first time gene expression adaptation to 30 days of microgravity exposure in mouse soleus and EDL highlighting potential new targets for improvement of countermeasures able to ameliorate or even prevent microgravity-induced atrophy in future spaceflights. Overall Design: C57BL/N6 mice were randomly divided in 3 groups: Bion Flown (BF) mice flown aboard the Bion M1 biosatellite in microgravity environment for 30 days; Bion Ground (BG) mice housed in the same habitat of flown animals but exposed to earth gravity; and Flight Control (FC) mice housed in a standard animal facility.
Global gene expression analysis highlights microgravity sensitive key genes in longissimus dorsi and tongue of 30 days space-flown mice
Microgravity as well as chronic muscle disuse are two causes of low back pain originated at least in part from paraspinal muscle deconditioning. At present no study investigated the complexity of the molecular changes in human or mouse paraspinal muscles exposed to microgravity. The aim of this study was to evaluate longissimus dorsi and tongue (as a new potential in-flight negative control) adaptation to microgravity at global gene expression level. C57BL/N6 male mice were flown aboard the BION-M1 biosatellite for 30 days (BF) or housed in a replicate flight habitat on ground (BG). Global gene expression analysis identified 89 transcripts differentially regulated in longissimus dorsi of BF vs. BG mice (False Discovery Rrate < 0,05 and fold change < -2 and > +2) while only a small number of genes were found differentially regulated in tongue muscle ( BF vs. BG = 27 genes). Overall Design: C57BL/N6 mice were randomly divided in 3 groups: Bion Flown (BF) mice flown aboard the Bion M1 biosatellite in microgravity environment for 30 days; Bion Ground (BG) mice housed in the same habitat of flown animals but exposed to earth gravity; and Flight Control (FC) mice housed in a standard animal facility.
Global gene expression analysis in TAE sensitive and resistant Kelly cells.
GEO Series GSE103083. Homo sapiens. 6 samples. Type: Expression profiling by array.
Global gene expression analysis highlights microgravity sensitive key genes in longissimus dorsi and tongue of 30 days space-flown mice
GEO Series GSE94381. Mus musculus. 23 samples. Type: Expression profiling by array.
Global gene expression analysis highlights microgravity sensitive key genes in soleus and EDL of 30 days space flown mice
GEO Series GSE80223. Mus musculus. 18 samples. Type: Expression profiling by array.
Global gene expression analysis highlights microgravity sensitive key genes in soleus and EDL of 30 days space flown mice
Microgravity exposure as well as chronic muscle disuse are two of the main causes of physiological adaptive skeletal muscle atrophy in humans and murine animals in physiological condition. The aim of this study was to investigate at both morphological and global gene expression level skeletal muscle adaptation to microgravity in mouse soleus and extensor digitorum longus (EDL). Adult male mice C57BL/N6 were flown aboard the BION-M1 biosatellite for 30 days on orbit (BF) or housed in a replicate flight habitat on Earth (BG) as reference flight control. In this study we investigated for the first time gene expression adaptation to 30 days of microgravity exposure in mouse soleus and EDL highlighting potential new targets for improvement of countermeasures able to ameliorate or even prevent microgravity-induced atrophy in future spaceflights. Overall Design: C57BL/N6 mice were randomly divided in 3 groups: Bion Flown (BF) mice flown aboard the Bion M1 biosatellite in microgravity environment for 30 days; Bion Ground (BG) mice housed in the same habitat of flown animals but exposed to earth gravity; and Flight Control (FC) mice housed in a standard animal facility.
Global gene expression analysis highlights microgravity sensitive key genes in longissimus dorsi and tongue of 30 days space-flown mice
Microgravity as well as chronic muscle disuse are two causes of low back pain originated at least in part from paraspinal muscle deconditioning. At present no study investigated the complexity of the molecular changes in human or mouse paraspinal muscles exposed to microgravity. The aim of this study was to evaluate longissimus dorsi and tongue (as a new potential in-flight negative control) adaptation to microgravity at global gene expression level. C57BL/N6 male mice were flown aboard the BION-M1 biosatellite for 30 days (BF) or housed in a replicate flight habitat on ground (BG). Global gene expression analysis identified 89 transcripts differentially regulated in longissimus dorsi of BF vs. BG mice (False Discovery Rrate < 0,05 and fold change < -2 and > +2) while only a small number of genes were found differentially regulated in tongue muscle ( BF vs. BG = 27 genes). Overall Design: C57BL/N6 mice were randomly divided in 3 groups: Bion Flown (BF) mice flown aboard the Bion M1 biosatellite in microgravity environment for 30 days; Bion Ground (BG) mice housed in the same habitat of flown animals but exposed to earth gravity; and Flight Control (FC) mice housed in a standard animal facility.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.