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648 results for “uncertainties”
Data in "The uncertainties in the laboratory-measured short-wave refractive indices of mineral dust aerosols and the derived optical properties: A theoretical assessment"
<p>This is the data for publication "The uncertainties in the laboratory-measured short-wave refractive indices of mineral dust aerosols and the derived optical properties: A theoretical assessment"</p> <p>Version 1: data</p> <p>Version 2: rename the data files and add a readme file</p>
MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network
<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>
Snakemake report for manuscript "Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing"
<p>For viewing the report, unzip the file and open index.html in your browser.</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>
Artifact supplement for 'An Oracle-Guided Approach to Constrained Controller Synthesis Under Uncertainty'
<p>Artifact supplement for submission "An Oracle-Guided Approach to Constrained Controller Synthesis Under Uncertainty".</p> <p>The attached file includes:</p> <ul> <li>docker image containing our tool and the models considered in our article</li> <li>log files from the conducted experiments</li> </ul>
Bayesian Uncertainty Quantification and Optimization of Jet Grout Column Diameter Prediction
<p><span>This dataset includes the jet grout data compiled from published case histories for Bayesian Uncertainty Quantification and Optimization of Jet Grout Column Diameter Prediction.</span></p>
MATLAB codes for paper: UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings
<p>These codes belong to the following paper. Please cite our work.</p> <p>Berghout T, Benbouzid M. UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings. <em>Electronics</em>. 2024; 13(12):2419. https://doi.org/10.3390/electronics13122419</p>
Supporting GIS file for: Tectonic landform and lithologic age impact uncertainties in fault displacement hazard models
<p>This project aims to understand how the error in mapped fault location and the residual between the modeled and observed coseismic displacements vary with tectonic landform and the surficial lithologic age. We focus on four historical earthquakes: the M6.9 Borah Peak, 2014 M6.0 Napa, 2016 M7.0 Kumamoto, and 2016 M7.8 Kaikoura earthquakes.</p> <p>The GIS shape file contains information about the tectonic landform, the surficial landscape age, the observed and modelled coseismic displacement, fault location error, and the confidence ranking of the mapped fault trace. Each entry corresponds to a location where a displacement measurement was made following the earthquake of focus. Additional detail is given in the readme.</p> <p>The entries in the GIS file are collected from the following references:</p> <p>Chiou, B., Chen, R., Thomas, K., Milliner, C. W. D., Dawson, T., & Petersen, M. D. (2022). Surface Fault Displacement Models for Strike-Slip Faults. <em>Natural Hazards Risk and Resiliency Research Center B. John Garrick Institute for the Risk Sciences University of California, Los Angeles</em>, <em>Report GIRS‐2022‐07</em>, 186. https://doi.org/10.34948/N3RG6X</p> <p>Crone, A. J., Machette, M. N., Bonilla, M., Lienkaemper, J. J., Pierce, K., Scott, W., & Bucknam, R. (1987). Surface faulting accompanying the Borah Peak earthquake and segmentation of the lost river fault, central Idaho. <em>Bulletin of the Seismological Society of America</em>, <em>77</em>.</p> <p>Graymer, R. W., Brabb, E., Jones, D. L., Barnes, J., Nicholson, R. S., & Stamski, R. E. (2007). <em>Geologic Map and Map Database of Eastern Sonoma and Western Napa Counties, California</em> (No. U.S. Geological Survey Scientific Investigations Map 2956). Retrieved from https://doi.org/10.3133/sim2956</p> <p>Heron, D. W. (2018). Geological Map of New Zealand 1:250 000. GNS Science Geological Map 1 (2nd ed.) Lower Hutt, New Zealand. GNS New Zealand. Retrieved from https://www.gns.cri.nz/data-and-resources/geological-map-of-new-zealand/</p> <p>Hoshizumi, H., Ozaki, M., Miyazaki, K., Matsuura, H., Toshimitsu, S., Uto, K., et al. (2004). Geological Map of Japan 1:200,000: Kumamoto. Geological Survey of Japan. Retrieved from https://www.gsj.jp/Map/EN/geology2-6.html#Kumamoto</p> <p>Janecke, S. U., & Wilson, E. (1992). Geologic map of the Borah Peak, Burnt Creek, Elkhorn Creek, and Leatherman Peak 7.5’ quadrangles, Custer County, Idaho, Scale 1:24,000. Idaho Geological Survey Technical Report 92-5. Retrieved from https://www.idahogeology.org/product/T-92-5</p> <p>Kuehn, Nicolas, Kottke, A., Madugo, C., Sarmiento, A., & Bozorgnia, Y. (2022). Report GIRS 2022-06: UCLA–PG&E Fault Displacement Model. https://doi.org/10.34948/N3X59H</p> <p>Lewis, R. S., Link, P., Stanford, L. R., & Long, S. P. (2012). <em>Geologic Map of Idaho</em>. Moscow, Boise, Pocatello: Idaho Geologic Survey. Retrieved from https://www.idahogeology.org/maps-pubs-data/state-geologic-map</p> <p>Ponti, D. J., Blair, J. L., & Rosa, C. M. (2019). Digital Datasets Documenting Fault Rupture and Ground Deformation Features Produced by the Mw 6.0 South Napa Earthquake of August 24, 2014 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7P26W84</p> <p>Sarmiento, A., Madugo, D., Bozorgnia, Y., Shen, A., Mazzoni, S., Lavrentiadis, G., et al. (2021). Fault Displacement Hazard Initiative Database. <em>Report No. GIRS-2021-08, Revision 3.3 Dated 29 May 2024. Los Angeles, CA: The B. John Garrick Institute for the Risk Sciences at UCLA Engineering</em>. https://doi.org/10.34948/N36P48</p> <p>Scott, C., Adam, R., Arrowsmith, R., Madugo, C., Powell, J., Ford, J., et al. (2023). Evaluating how well active fault mapping predicts earthquake surface-rupture locations. <em>Geosphere</em>, <em>19</em>(4), 1128–1156. https://doi.org/10.1130/GES02611.1</p> <p>Scott, C. P., Arrowsmith, J. R., Nissen, E., Lajoie, L., Maruyama, T., & Chiba, T. (2018). The <em>M</em> 7 2016 Kumamoto, Japan, Earthquake: 3-D Deformation Along the Fault and Within the Damage Zone Constrained From Differential Lidar Topography. <em>Journal of Geophysical Research: Solid Earth</em>, <em>123</em>, 6138–6155. https://doi.org/10.1029/2018JB015581</p> <p>Vincent, K. R. (1995). Implications for models of fault behavior from earthquake surface displacement along adjacent segments of the Lost River fault, Idaho<em>:</em> University of Arizona.</p> <p>Wagner, D., & Gutierrez, C. (2017). <em>Preliminary Geologic Map of the Napa and Bodega Bay 30’ x 60’ Quadrangles, California</em>. California Department of Conservation. Retrieved from https://ngmdb.usgs.gov/Prodesc/proddesc_105819.htm</p> <p>Zinke, R., Hollingsworth, J., Dolan, J. F., & Van Dissen, R. (2019). Three‐Dimensional Surface Deformation in the 2016 M <sub>W</sub> 7.8 Kaikōura, New Zealand, Earthquake From Optical Image Correlation: Implications for Strain Localization and Long‐Term Evolution of the Pacific‐Australian Plate Boundary. <em>Geochemistry, Geophysics, Geosystems</em>, <em>20</em>(3), 1609–1628. https://doi.org/10.1029/2018GC007951</p>
The superconducting clock-circuit: Improving the coherence of Josephson radiation beyond the thermodynamic uncertainty relation
Open the record for dataset details and reuse information.
Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks
<p>This dataset contains the behavioral choice data obtained from N = 230 participants that played a two-armed bandit task (139 females, age: 34 +/- 10 years) in partial and complete feedback conditions, as described in (Findling, Skvortsova et al., 2019, Nature Neuroscience, https://doi.org/10.1038/s41593-019-0518-9).</p> <div> <div> <div> <p>The experiment was performed on the Prolific platform (prolific.co) and the research was carried out following the principles and guidelines for experiments including human participants provided in the declaration of Helsinki and approved by the relevant authorities (Inserm Ethical Review Committee, IRB #00003888). All participants provided written informed consent prior to their inclusion.</p> </div> </div> </div>
Map of Aboveground Biomass and Uncertainty of Haloxylon in the Ancient Manas Lake Basin Area, Western Junggar Basin, Xinjiang, China
<p>This dataset is the research outcome of the National Natural Science Foundation of China project "Spatiotemporal Evolution and Attribution of Haloxylon Aboveground Biomass in the Junggar Basin under the Background of Climate Change" (Grant No.42261062) and the Natural Science Foundation of Xinjiang Uygur<br> Autonomous Region, China project "Remote Sensing Technology for Acquiring Aboveground Biomass of Haloxylon Forests in the Ancient Manas Lake Basin Sedimentary Area" (Grant No.2022D01A97). It reflects the map of aboveground biomass and uncertainty of Haloxylon in the ancient Manas Lake basin area, western Junggar Basin. The data is in tif format, with a coordinate system of UTM 45N and a resolution of 30 meters. It contains three bands, namely "AGB", "Uncertainty", and "AOA", representing "Haloxylon Aboveground Biomass", "Uncertainty of Haloxylon Aboveground Biomass", and "Areas of Applicability and Non-applicability".</p> <p>please cite "Yang XF. 2025. Mapping desert shrub aboveground biomass in the Junggar Basin, Xinjiang, China using Quantile Regression Forest (QRF). PeerJ 13:e19099 http://doi.org/10.7717/peerj.19099".</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>
UV scans measured by Brewer #150 during the XVI intercomparison campaign of the RBCC-E and their associated uncertainties
<p>All files uploaded are needed to evaluate the uncertainty of the UV measurements recorded by the Brewer MKIII double monochromator (No. 150). The 'data.rar' contains the UV scans measured by Brewer #150 during the XVI intercomparison campaign of the RBCC-E and their associated uncertainties and all the necessary information to characterize Brewer #150 (cosine correction, raw counts, lamp irradiance, responsivity instabilities, noise and wavelength shifts). The uncertainty analysis was carried out using two methodologies: the GUM uncertainty framework (based on the law of propagation of uncertainties) and the Monte Carlo technique. Both approaches are implemented in a separate R file (doi: https://doi.org/10.5281/zenodo.10973560).</p> <p>The previous version used the standard deviation instead of the standard deviation (as recommended by the GUM) for the analysis of the uncertainty of dark counts. This second version corrects this.</p>
Data for the manuscript "Spatially resolved uncertainties for machine learning potentials"
<p>This repository accompanies the manuscript "Spatially resolved uncertainties for machine learning potentials" by E. Heid, J. Schörghuber, R. Wanzenböck, and G. K. H. Madsen. The following files are available:</p> <ul> <li> <p><code>mc_experiment.ipynb</code> is a Jupyter notebook for the Monte Carlo experiment described in the study (artificial model with only variance as error source).</p> </li> </ul> <ul> <li> <p><code>aggregate_cut_relax.py</code> contains code to cut and relax boxes for the water active learning cycle.</p> </li> <li> <p><code>data_t1x.tar.gz</code> contains reaction pathways for 10,073 reactions from a subset of the Transition1x dataset, split into training, validation and test sets. The training and validation sets contain the indices 1, 2, 9, and 10 from a 10-image nudged-elastic band search (40k datapoints), while the test set contains indices 3-8 (60k datapoints). The test set is ordered according to the reaction and index, i.e. rxn1_index3, rxn1_index4, [...] rxn1_index8, rxn2_index3, [...].</p> </li> <li> <p><code>data_sto.tar.gz</code> contains surface reconstructions of SrTiO3, randomly split into a training and validation set, as well as a test set.</p> </li> <li> <p><code>data_h2o.tar.gz</code> contains:</p> <ul> <li> <p><code>full_db.extxyz</code>: The full dataset of 1.5k structures.</p> </li> <li> <p><code>iter00_train.extxyz</code> and <code>iter00_validation.extxyz</code>: The initial training and validation set for the active learning cycle.</p> </li> <li> <p>the subfolders in the folders <code>random</code>, and <code>uncertain</code>, and <code>atomic</code> contain the training and validation sets for the random and uncertainty-based (local or atomic) active learning loops.</p> </li> </ul> </li> </ul>
Importance of Parametric Uncertainty in Predicting Probability Distributions for Burst Wait-Times in Fissile Systems
<p>In accordance with EPSRC funding requirements this folder contains all raw data relevant to the named paper: Importance of Parametric Uncertainty in Predicting Probability Distributions for Burst Wait-Times in Fissile Systems.</p>
Fig. S5. Uncertainty from k in Supplementary Material for The global tree restoration potential
Fig. S5. Uncertainty from k-fold cross validation. The uncertainty is expressed as the standard deviation of the tree cover predicted from the k potential tree cover layers computed during the k-fold crossvalidation. (A) Summary of the procedure. (B) Uncertainty (standard deviation) vs. mean predicted tree cover at the pixel level. The relationship shows that the level of uncertainty is greater at intermediate tree cover classes, reaching 15% of tree cover variation at 50% of the predicted potential tree cover.
parallelpro/numax: Source code for "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity and Numax" by Li, Yaguang et al. (2024)
<p>This repository contains the datasets and Python scripts used in the paper "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity, and Numax" by Yaguang Li et al. (2024). The stellar models used in this project are available on <a href="https://doi.org/10.5281/zenodo.12815718">Zenodo</a>.</p>
Restart uncertainty relation for monitored quantum dynamics. DataSet
<p>Source Data Set for the publication entitled: Restart uncertainty relation for monitored quantum dynamics.</p>
Datasets for "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the datasets for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations".</p>
Intermediate Data Products for "A NASA GISTEMPv4 Observational Uncertainty Ensemble"
<p>Contains the Intermediate data for "A NASA GISTEMPv4 Observational Uncertainty Ensemble" as accepted at JGR:Atmospheres (August 2024).</p> <p>Raw and Results Data can be found here: <a href="https://doi.org/10.5281/zenodo.13343335">https://doi.org/10.5281/zenodo.13343335</a></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.