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44 results for “uncertainty analysis”
Topographic data to support the analysis of error and uncertainty that degrade topographic corrections of remotely sensed data
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Numerical code and data for the stellar structure and dynamical instability analysis of generalised uncertainty white dwarfs
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Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
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Data of paper "Grid ,Hydrodynamic boundary and Uncertainty analysis of 2D-SWEs in the context of digital twins: Taking numerical simulation of river networksas an example"
<p>论文数据 “数字孪生背景下2D-SWEs的网格、水动力边界和不确定性分析:以河流网络数值模拟为例”</p>
Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China
<p>The attached is the dataset assoicated with the paper titled "Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China" which was submitted to Journal of Earth and Space Science.</p><p>The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth System Models (ESMs). In this paper, historical LS3MIP hydrological variables including precipitation (PR), evapotranspiration (ET), soil moisture (SM), total runoff (Ro), and snow cover fraction (SCF) were extensively evaluated with various high-quality reference datasets over Chinese mainland. The six ESMs in LS3MIP were driven by four meteorological forcing datasets. The results indicated that the LS3MIP multi-model means (MMEs) of most variables are underestimated overall, while they show high spatial consistency in term of linear trends, with the percentage area ranging 56% ~ 85% between simulations and reference datasets. After computing and ranking multi statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root-mean-square biases), it is found that the CESM2 model produces the best performance of land surface hydrological variables, while as the meteorological forcing dataset GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) was then used to trace sources of the uncertainty of the LS3MIP hydrological variables for 1900–2012 (1948–2012 for Ro). In ANOVA, the simulation uncertainties may be decomposed into three sources: model, atmospheric forcing datasets and their interactions. In LS3MIP historical hydrological variables over China, model uncertainty is the dominant factor overall although it shows regional differences, and the dependence of uncertainty on the model differs among hydrological regimes. This highlights the urgent requirements to improve the land surface model representation in future research.</p>
Data Set and Replication Package of Paper on Handling Environmental Uncertainty in Design Time Access Control Analysis
<p>Data set and replication package for Paper "Handling Environmental Uncertainty in Design Time Access Control Analysis".</p> <p>The data set contains an overview of used case studies, with illustrations and descriptions.</p> <p>The replication package contains the implemented application as well as model instances of every case study used for the evaluation.</p>
Prototype Implementation: Uncertainty-aware Confidentiality Analysis Using Architectural Variations
<p>Dataset for the bachelor thesis "Uncertainty-aware Confidentiality Analysis Using Architectural Variations".</p> <p>The ZIP file contains the Eclipse project of the prototype with installation instructions and the models used, modeled in the Palladio Component Model.</p>
Dataset for publication 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'
<p>This dataset includes all data and information on how to reproduce the results and the figures of the paper 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'.</p>
Modelling input data for the case study of the paper "Uncertainty-Based Market-Clearing Models: A Comparative Analysis of the Dutch, French, and German Markets".
<p>This data package includes the modelling input data to replicate the results of the case study included in the paper "Uncertainty-Based Market-Clearing Models: A Comparative<br>Analysis of the Dutch, French, and German Markets". </p> <p>The case study models the Dutch, French and German day-ahead electricity markets, in which the existing capacities of electricity generation and upward- and downward reserve capacities are considered, in addition to 105 wind output realization scenarios for each simulation day. A detailed description of the case study is provided in the readme file.</p> <p>This supplementary data package includes the following files:</p> <p>- Meta Data – Netherlands.xlsx: Dataset containing the meta data for the Dutch case study</p> <p>- Meta Data – France.xlsx: Dataset containing the meta data for the French case study</p> <p>- Meta Data – Germany.xlsx: Dataset containing the meta data for the German case study</p> <p>- Readme.txt: Includes a detailed description of the data packages</p>
ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis - Data Set
<p>Data set of the Paper "ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://abunai.dev</p>
Code and data for Global sensitivity analysis can unveil the hidden universe of uncertainty in multiverse studies
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Architectural Uncertainty Analysis for Access Control Scenarios in Industry 4.0 - Data Set
<p>This data set contains additional information to the master's thesis of Nicolas Boltz. Included are the implementation, tests, and model instances of sample scenarios.</p>
Architecture-based Uncertainty Impact Analysis to ensure Confidentiality - Data Set
<p>Data set of the Paper "Architecture-based Uncertainty Impact Analysis to ensure Confidentiality". For more information, please see the README.md.</p>
TRENDYv10 DGVM output for: Process-oriented analysis of dominant sources of uncertainty in the land carbon sink
<p><strong>New datasets saved as NCDF files.</strong></p> <p><strong>Data</strong></p> <p>Post-processed TRENDYv10 DGVM data covering the period 1901-2020. Each file contains monthly/annual mean values for 19 models: CABLE-POP, CLASSIC, CLASSIC-N, CLM5.0, DLEM, IBIS, ISAM, ISBA-CTRIP, JSBACH, JULES-ES-1.1, LPJ-GUESS, LPJ, LPX-Bern, OCN, ORCHIDEE, ORCHIDEEv3, SDGVM, VISIT, YIBs. See https://blogs.exeter.ac.uk/trendy/</p> <p>List of variables can be found at https://blogs.exeter.ac.uk/trendy/documents/ . File name: 'trendy_listofvariables_GCP2021'.</p> <p>Data for three experiments (S1 - S3) is provided. For experiment details, see 'GlobalCarbonBudget-Protocol-2021-web' at https://blogs.exeter.ac.uk/trendy/documents/</p> <p><strong>Code</strong></p> <p>All data processing code is also provided. Files starting "raw#..." are run in order 1-4. Then the figure/manuscript files are executed, starting with "proc1.." - "proc3..". The remaining Figure scripts (Figures 1-5 and S1-S8) and "temp_changes.R" can be run in any order.</p> <p>Any enquiries, contact Mike O'Sullivan at m.osullivan@exeter.ac.uk</p>
Analysis and Data of "Adaptive tuning of human learning and choice variability to unexpected uncertainty"
<p>Data and analysis scripts in "Adaptive tuning of human learning and choice variability to unexpected uncertainty". See https://github.com/jlexternal/RLVOLUNP_ana for directory structure. </p>
Table-Matrix, containing all the codes generated from the inductive analysis process of the 78 articles that made up the final sample. (Not only Opportunity, but also Uncertainty: A systematic review of how entrepreneurship literature appropriates both constructs.)
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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>
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation: Model Data and Analysis Code
<p>This repository contains model data and analysis codes related to the manuscript entitled "Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation", as follows:</p> <ol> <li>Model data are maximum water surface elevations of ensemble 48-hr forecast ADCIRC model simulations for three historical US landfalling hurricanes: 2017 Irma, 2018 Florence, and 2020 Laura. These are located in the "NameYYYY_Results.tar" archive files as "maxele.63.nc" files. Also included in the tar files are the hurricane forecast track files in Automated Tropical Cyclone Forecasting (ATCF) system format (*.22) and the error variable parameters (*.json) for each forecast. </li> <li>Model data of best-track runs for the 2017 Irma, 2018 Florence, and 2020 Laura hurricanes, and astronomical tide-only runs for the corresponding time periods are located in the "NameYYYY_besttrack+tides.tar" archive files. Both the maximum water surface elevations "maxele.63.nc" and the time series of water surface elevations "fort.63.nc" are included. </li> <li>ADCIRC input mesh (*.14) and mesh property files (*.13) are included in "ADCIRC_mesh_files.zip".</li> <li>Joint Karhunen-Loeve Polynomial Chaos (KL-PC) analysis python scripts with and without considering inundation are located in "klpc_analysis_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> <li>Python scripts for analyzing and plotting the KL-PC results (Figures 6-14 and Table 1 in the manuscript) are located in "results_plotting_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> </ol>
The dataset of article "Analysis of Uncertainties and Associated Convective Processes in Simulations of Extreme Precipitation over Cities with a Regional Earth System Model: A Case Study"
<p>The "out<i>EXP02.nc" and "out</i>EXP11.rar" are two examples of 11 simulations' output file in the article, in each of the file, 9 variables (horizontal wind u, horizontal wind v, vertical speed, height, temperature, pressure, precipitation, longitude and latitude) from outputs of simulation are included, the time interval is 2 hours.</p><p>The MERRA-2 dataset provides the initial and boundary conditions of chemical fields in simulations.</p><p>The era5 dataset provides the meteorological initial and boundary conditions in simulations.</p><p> </p>
Data and Codes for: Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis
<p>Phase segmentation is a crucial step in X-ray computed tomography (CT) for image-based analysis (CT-IBA) to derive soil and root information. How segmentation uncertainty (SU) affects CT-IBA of vegetated soil has never been explored. The enclosed data and codes are used to assist the analysis of SU quantification and propagation in the journal paper published in Plant & Soil. The title of the paper is Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis. </p>
ScienceDex guides
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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.