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44 results for “uncertainty analysis”
Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset
<p><strong>Dataset of <a href="https://doi.org/10.1109/jstars.2022.3188922">Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain</a>.</strong></p> <p>The data is composed of:</p> <ul> <li><strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization);</li> <li><strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac.</li> </ul> <p>The filenames correspond to those used in the <strong>associated GitHub repository</strong>: <a href="https://github.com/rhugonnet/dem_error_study">https://github.com/rhugonnet/dem_error_study</a>. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at <a href="https://www.glims.org/RGI/">https://www.glims.org/RGI/</a>.</p> <p>The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of <a href="https://lpdaac.usgs.gov/products/ast_l1av003/">AST L1A products</a>. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.</p>
Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis
<p>This data repository is associated with the paper:</p> <p>Morris,J., A. Sokolov, J. Reilly, A. Libardoni, C. Forest, S. Paltsev, A Schlosser, R. Prinn and H. Jacoby (2025). Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis. <em>Nature Communications </em><strong>16</strong>, 2703. https://doi.org/10.1038/s41467-025-57897-1</p> <p>This paper quantifies key socio-economic and climate uncertainties using the MIT Integrated Global System Model. </p>
Dataset for "Regional Uncertainty Analysis in the Air-Sea CO2 Flux"
<p>This repository contains processed and output data used in the "Regional Uncertainty Analysis in the Air-Sea CO2 Flux" project. </p> <ul> <li><strong>fractional-uncertanties-1x1-1993-2022.nc </strong>: fractional uncertanies calculated with FluxError</li> </ul> <p>The following is the processed data used to calculate fractional uncertanties.</p> <p><strong>Individual Datasets</strong></p> <p>Sea Surface Temperature (SST)</p> <ul> <li><strong>oisst-1x1-1993-2022.nc : </strong>NOAA SST</li> <li><strong>cobe2-1x1-1993-2022.nc :</strong> COBE2 SST </li> <li><strong>esa-1x1-1993-2022.nc : </strong>ESA SST</li> <li><strong>ostia-1x1-1993-2022.nc : </strong>OSTIA SST</li> </ul> <p>10m Wind Speed</p> <ul> <li><strong>ccmp-1x1-1993-2022.nc : </strong>CCMP 10m wind speed</li> <li><strong>jra3q-wind-1x1-1993-2022.nc : </strong>JRA wind speed</li> <li><strong>era5-wind-1x1-1993-2022.nc : </strong>ERA5 wind speed</li> </ul> <p>Sea Surface Salinity (SSS)</p> <ul> <li><strong>en4-1x1-1993-2022.nc : </strong>EN4 salinity </li> <li><strong> glorys-1x1-1993-2022.nc :</strong> GLORYS salinity <strong> </strong></li> <li><strong>oras5-1x1-1993-2022.nc :</strong> ORAS5 salinity </li> </ul> <p>Atmospheric xCO2</p> <ul> <li><strong>noaa-mbl_197901-202301_1x1.nc : </strong>atmospheric xCO2</li> </ul> <p>Ocean pCO2</p> <ul> <li><strong>pco2-1x1-1993-2022.nc : </strong>Global Carbon Budget ocean model and data product output, converted to pCO2</li> </ul> <p>Sea Level Pressure </p> <ul> <li><strong>era5-slp-1x1-1993-2022.nc : </strong>ERA5 sea level pressure</li> </ul> <p>1 Degree Ocean Mask</p> <ul> <li><strong>ocean-mask_invariant_1x1.nc : </strong>Ocean mask </li> </ul> <p><strong>Merged datasets: </strong>these datasets are larger and contain the ensemble of datasets above merged into single files</p> <ul> <li><strong>salinity-1x1-1993-2022.nc : </strong>merged salinity datasets</li> <li><strong>sst-1x1-1993-2022.nc : </strong>merged SST datasets</li> <li><strong>wind-1x1-1993-2022.nc : </strong>merged wind speed datasets</li> </ul>
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </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 supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"
<p>Changelog</p> <p>v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged. </p> <p>v1: Initial dataset released along with the supporting manuscript</p> <p> </p> <p>Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO<sub>2</sub> flux uncertainties constructed from surface ocean data products".</p> <p>Two files are present:</p> <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> <p>Within the ZIP file, multiple folders are present:</p> <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO<sub>2</sub> and fCO<sub>2</sub> network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO<sub>2 (sw), </sub>xCO<sub>2 (atm)</sub> and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> <p>In the main folder, three files are present:</p> <ol> <li>Annual_flux.csv contains the annual air-sea CO<sub>2</sub> flux (or ocean sink estimate) estimated from the fCO<sub>2 (sw)</sub> fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO<sub>2 (sw)</sub>, the air-sea CO<sub>2</sub> flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol> <p> </p> <p>Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions.</p> <p><strong>Acknowledgements</strong></p> <p>This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p> </p> <p><strong>References</strong></p> <p>Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803</p>
New calibrated models for the TRGB luminosity and a thorough analysis of theoretical uncertainties
<p>The files contain the grid models for TRGB stars based on <strong>Saltas & Tognelli, 2022, MNRAS, in press (arXiv: 2203.02499)</strong>. See the paper for details on the physics, and the attached readme file for a description of the structure of the attached files. If you use these data in your research please cite the above article. <br> </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. For more information please visit https://abunai.dev</p>
Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
<div> <div> <div> <div> <div>This dataset includes experimental measurements taken on a laboratory testbed at Colorado State University that was used for model validation of a software toolkit, the Building Electrical Efficiency Analysis Model (BEEAM). This toolkit was developed for comparing electrical efficiency of AC versus DC distribution systems in buildings. The testbed emulated loads found in a small office building and included laptop computer chargers, LED lighting systems, and miscellaneous DC and AC loads. Measurements were taken under AC and DC configurations in electrically balanced and unbalanced loading conditions. Also included in the dataset is an uncertainty analysis. A complete description of the testbed, hardware, measurements and uncertainty analysis is contained in the paper cited below.</div> </div> </div> </div> </div> <div> </div> <div>Avpreet Othee, James Cale, Arthur Santos, Stephen Frank, Daniel Zimmerle, Omkar Ghatpande, Gerald Duggan and Daniel Gerber, <em>"A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings," Energies, 2023 (accepted, publication in progress).</em> </div>
Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
Open the record for dataset details and reuse information.
Uncertainty Analysis of MSL's CCT-K7.2021 Key Comparison Measurements Using Uncertain Numbers
<p>This dataset is associated with a publication of the same name (currently submitted to Metrologia). It contains Python modules and text files and can be used to repeat the analysis described in the article. The GUM Tree Calculator (GTC) Python software package is required (version 1.4.0, or above: https://github.com/MSLNZ/GTC).</p>
A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment - supporting material
<p>Publication dataset - A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment (<em>accepted for publication in the Journal of Industrial Ecology</em>).</p>
Architecture-based Uncertainty Impact Analysis for Confidentiality (Reproduction Set)
<p>Reproduction set for master thesis of Niko Benkler.</p> <p>Zip file contains code, data set and installation guides. </p> <p>Please open README.md to get information about content structure.</p>
Supporting data for article comparison and Uncertainty Analysis of Species Distribution Models
<p>Downloaded from Web of Science for the supporting data of article comparison and Uncertainty Analysis of Species Distribution Models.</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>
Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
<p>This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.</p>
Moment tensor inversion and uncertainty analysis for 40 Uttarakhand Earthquakes (2010-2022)
<p>This repository provides detailed descriptions of the files that were used for the Moment tensor and uncertainty analysis study of earthquakes in the Uttarakhand Himalayas. These files contain the Moment Tensor (MT) estimation results and uncertainty quantification of 40 earthquakes using different networks.</p> <p> </p> <p><strong>Contents:</strong></p> <p>1. waveform_fits.docx - Waveform fits for MT estimation</p> <p>2. confidence_plots.docx - The confidence parameters associated with each MT</p> <p>3. depth_vs_misfit_plot.docx - The confidence in the MT solution for each depth against the misfit values</p> <p>4. weight_files.zip - Weight files for 40 events read by the MTUQ package</p> <p>5. CMT_solution_files.zip - Centroid Moment Tensor (CMT) solutions for 40 events</p>
A Deep Learning Approach for TEM Data Denoising, Inversion and Uncertainty Analysis with Monte Carlo Dropout
<p>This dataset includes the code and data for training the inversion network used in the study. The provided files cover data loading, preprocessing, and network training for transient electromagnetic (TEM) data inversion. For details on the included files and instructions on usage, please refer to the README.txt file.</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.