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648 results for “uncertainties”
Figures 8, 9 in A new species of Solaropsisfrom Amapá, Brazil (gastropoda: Solaropsidae) triggering uncertainty about the genus and redefinition of some species
Figures 8, 9. Holotype, details of punctuation in the sculpture, scales = 1 mm.
Residual Life and Reliability Assessment of Underground RC sanitary Sewer Pipelines Under Uncertainty
<p>Prioritization of limited funding for pipelines maintenance is a major issue that concerns municipalities nationwide. Conducting a probabilistic assessment can provide a complete characterization of the performance of structural elements and systems along with optimizing the limited resources. The most widely used probabilistic performance indicator is reliability, a measure of the probability of failure corresponding to a particular limit state (e.g., ultimate strength or serviceability). Reliability methods can be used to identify which pipeline sections within a particular system require the most urgent inspection or repair. To this end, an automated data-driven framework for large diameter reinforced concrete pipes (RCPs) is developed that converts the raw unfiltered inspection readings to data that is used for estimating residual life and further reliability assessment purposes. In the current work, initially, the wall thickness erosion is determined based on the inspection data collected using Light Detection and Ranging (LiDAR). Furthermore, the best fit among several probability distribution functions for the wall thickness loss is obtained, which is integrated with a serviceability limit state that defines failure as the complete loss of 1-in concrete cover caused by environmental conditions such as sulfide-induced erosion. Considering this limit state and a prescribed probability of exceedance threshold, a reliability-based prediction of the remaining service life is proposed. The developed framework requires minimal user interference and is, therefore, less time consuming and more consistent compared to previous research. From an asset management point of view, the most vulnerable pipeline sections are identified that will require further inspection and attention. This will provide decision makers crucial information regarding the current state of the pipeline network, to better allocate the already scarce maintenance funding of these pipelines.</p>
Dynamic Uncertainty Lot Sizing Model: Formulation, Solution Methods, and Potential over the Static-Dynamic Uncertainty Model
<p>Test data used in Section 6</p>
Preconditioners for robust optimal control problems under uncertainty - numerical tests
<p>Codes and data of the numerical experiments described in the manuscript "Preconditioners for optimal control problems under uncertainty".</p>
Datasets for "Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement"
<p>Official datasets for the manuscript "Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement".</p> <p>SciSp-C and SciSp-H are presented in the dataset files.</p> <p>Due to our inability to get copyright permissions from various publishers for each image in the SciSp-C dataset, we provide the detailed identifiers for each image to prevent potential copyright disputes. Please kindly track the identifiers to get the original images. If you have any questions about this dataset, please contact linxun@buaa.edu.cn.</p>
Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity
Open the record for dataset details and reuse information.
Replication package for "Testing Models of Strategic Uncertainty: Equilibrium Selection in Repeated Games"
<p>This package contains the data, analysis code, and experimental software to replicate manuscript "<em>Testing Models of Strategic Uncertainty: Equilibrium Selection in Repeated Games</em>" by the Boczon, Vespa, Weidman and Wilson forthcoming at JEEA.</p>
List of articles that comprise the final sample. (Not only Opportunity, but also Uncertainty: A systematic review of how entrepreneurship literature appropriates both constructs.)
Open the record for dataset details and reuse information.
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.)
Open the record for dataset details and reuse information.
WRF model output used in an ideal model experiment demonstrating the applicability of a channel-synthesizing method for reducing uncertainties in satellite radiance transfer modeling
<p>This repository stores the WRF model output used in the paper "A novel channel-synthesizing method for reducing uncertainties in satellite radiance transfer modeling" submitted to Geophysical Research Letters.</p>
Uncertainty Quantification in Multivariate Mixed Models for Mass Cytometry Data (Processed Data)
<p>Processed data computed using R packages <a href="https://christofseiler.github.io/CytoGLMM">CytoGLMM</a> and <a href="https://christofseiler.github.io/cytoeffect">cytoeffect</a>. Raw data available <a href="http://flowrepository.org/id/FR-FCM-ZY3Q">here</a>.</p>
UNCERTAINTY IN THE HEISENBERG UNCERTAINTY PRINCIPLE
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Data from: Accounting for uncertainty in dormant life stages in stochastic demographic models
Dormant life stages are often critical for population viability in stochastic environments, but accurate field data characterizing them are difficult to collect. Such limitations may translate into uncertainties in demographic parameters describing these stages, which then may propagate errors in the examination of population-level responses to environmental variation. Expanding on current methods, we 1) apply data-driven approaches to estimate parameter uncertainty in vital rates of dormant life stages and 2) test whether such estimates provide more robust inferences about population dynamics. We built integral projection models (IPMs) for a fire-adapted, carnivorous plant species using a Bayesian framework to estimate uncertainty in parameters of three vital rates of dormant seeds – seed-bank ingression, stasis and egression. We used stochastic population projections and elasticity analyses to quantify the relative sensitivity of the stochastic population growth rate (log λs) to changes in these vital rates at different fire return intervals. We then ran stochastic projections of log λs for 1000 posterior samples of the three seed-bank vital rates and assessed how strongly their parameter uncertainty propagated into uncertainty in estimates of log λs and the probability of quasi-extinction, Pq(t). Elasticity analyses indicated that changes in seed-bank stasis and egression had large effects on log λs across fire return intervals. In turn, uncertainty in the estimates of these two vital rates explained > 50% of the variation in log λs estimates at several fire-return intervals. Inferences about population viability became less certain as the time between fires widened, with estimates of Pq(t) potentially > 20% higher when considering parameter uncertainty. Our results suggest that, for species with dormant stages, where data is often limited, failing to account for parameter uncertainty in population models may result in incorrect interpretations of population viability.
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling
<p><strong>Abstract:</strong><br> To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effectiveness of our approach in ensuring better generalizability as well as physical consistency in MC estimates over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. We further show that our MC estimates correctly match the distribution of ground-truth observations, thus making the PGA paradigm amenable to physically grounded uncertainty quantification.</p>
Digital representation of measurement uncertainty: a case study linking an RMO key comparison with a CIPM Key Comparison
<pre>This dataset is associated with a publication of the same name (currently submitted to the MDPI journal Metrology). It contains digital records (JSON files) of participant results as well as DoE results obtained from data processing. Python modules to display the contents of these files are provided as are modules for the comparison analyses. The GUM Tree Calculator (GTC) Python software package is required (version~1.3.6, or above: https://github.com/MSLNZ/GTC).</pre>
Rotating shallow water flow under location uncertainty with a structure-preserving discretization -- Data set
<p>With this data set one can reproduce the figures in the manuscript "Rotating shallow water flow under location uncertainty with a structure-preserving discretization".</p> <p>The figures can be created with the following MATLAB scripts:</p> <p>Figure 3 - plot_contour_plane.m</p> <p>Figure 4 and 5 - plot_Energy_convergence.m</p> <p>Figure 6 - plot_sphere_snapshot.m</p> <p>Figure 7 - plot_contour_sphere.m</p> <p>Figure 8 - plot_specs.m</p> <p>Figure 9 - plot_spread.m</p> <p>Figure 10 - createhist.m</p> <p>Figure 11 - plot_MSB_MEV.m</p> <p> </p> <p> </p>
Figure 1 from: Cepeda GD, Sabatini ME, Scioscia CL, Ramírez FC, Viñas MD (2016) On the uncertainty beneath the name Oithona similis Claus, 1866 (Copepoda, Cyclopoida). ZooKeys 552: 1-15. https://doi.org/10.3897/zookeys.552.6083
Figure 1 - Former selected drawings of Oithona similis / helgolandica. A, B Oithona spinifrons Boeck, 1864 (=? Oithona helgolandica Claus), female body and "one of swimming feet" (= leg 4?) (after Brady1878, Plates XIV and XXIV A) C–F Oithona similis exopod of legs 1 to 4 (after Gisbrecht 1893, Plate 34) G–J Oithona helgolandica, female body, legs 1-2 and leg 4 (after Sars 1913, Plate III) K–O Oithona similis, female body and legs 1 to 4 (after Nishida 1985, fig. 50 and 51). Original illustrations were faithfully copied in all details and rearranged to facilitate comparisons. Scale bars only provided in Nishida (1985).
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>
Supplementary material 3 from: Auliya M, Altherr S, Nithart C, Hughes A, Bickford D (2023) Numerous uncertainties in the multifaceted global trade in frogs' legs with the EU as the major consumer. Nature Conservation 51: 71-135. https://doi.org/10.3897/natureconservation.51.93868
Online sources and those useful with explanatory information
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.