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648 results for “uncertainties”
Fig. 2 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 2 Phulocenetic tree of sicmodontine rodents based on Cytb (a) and Rbp3 (b) DNA sequences. Bauesian posterior probabilitu values creater than 0.95 are represented bu asterisks placed above the branch
Fig. 1 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 1 Graphical representation of the PCA analuses (a, b) and LDA analusis (c, d). Vectors labeled as in Table 1. a, c Samples identified bu localitu and ace. Eastern samples (N. vossi sp. nov.: V) are light gray, western samples (N. monticolus: M) are dark gray, specimens from Antioquia (N. monticolus: M1) are mid-gray. Adults (fused craniosutures) are circles, and subadults (closed craniosutures) are triangles. b, d Samples identified bu localitu and sex. Location sumbols same as above, females—circles, males—triangles
Fig. 3 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 3 Map of recordinc localities of specimens of Neusticomys analuzed in the present studu. Eastern samples (N. vossi sp. nov.: V) are circles, western samples (N. monticolus: M) are squares, and specimens from Antioquia (N. monticolus: M1) are triangles
Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements (Replication Package)
<p>Replication package for Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements</p>
Monash X band radar case for bushfire detection and a precipitation case used to examine uncertainty in ZDR
<p>Data sets from a mobile X-band radar used to examine uncertainty in ZDR and roHV . Data includes preciptation and wildfire ash clouds. </p> <p>The data format is <span>unprocessed, ungridded (spherical coordinates) and provided using the Eumetnet </span><a href="https://www.eumetnet.eu/wp-content/uploads/2019/01/ODIM_H5_v23.pdf" target="_blank" rel="noopener"><span>ODIMH5 model</span></a><span> of the HDF5 format</span> . </p> <p>The bublication will be May et al, J. Atmos. Oceanic. Tech titled: </p> <p><span>Accuracy of polarimetric radar Z<sub>DR</sub> estimates: Implications for the quantitative observation of meteorological and non-meteorological echoes</span></p>
Experiment results for the paper "Uncertainty-Aware Ship Location Estimation using Multiple Cameras in Coastal Areas" to appear in MDM'2024
<p>After decompression, there are 16 folders which corresponding to the 16 multi-camera settings in the paper.</p> <p> </p> <p>Under each folder, there are two files: trajs.csv and trajsGuess.csv.</p> <p> </p> <p>1. trajs.csv contains the trajectories of ships that are located inside the monitored area of the mult-camera setting.</p> <p> The first four columns are MMSI (ship identity), timestamp, lon, and lat.</p> <p> The following columns are the corresponding pixel of the coordinate (lon, lat) in each camera, where (-1,-1) means (lon, lat) is outside the monitored area by a camera.</p> <p> A pixel is a pair of integers. </p> <p> xPos1 and yPos1 are for the 1st camera, and xPos2 and yPos2 are for the 2nd camera, and so on so forth.</p> <p> </p> <p>2. trajsGuess.csv contains the estimated ship locations by using the proposed approach in the paper.</p> <p> There are 6 columns.</p> <p> The 1st column is timestamp.</p> <p> The 2nd column is used to distinguish between the different pixel polygon intersections.</p> <p> The 3rd column and the 4th column can be either a pixel coordinate or a spatial point coordinate in lon/lat.</p> <p> The 5th column is either the cameraID of a pixel, or the order of a boundary point for a spatial polygon. The cameraID starts from 1.</p> <p> The 6th column is the type of the record, which can be</p> <p> pixel,</p> <p> or intersection1 (a polygon),</p> <p> or center1 (center of intersection1),</p> <p> or intersection2 (a polygon),</p> <p> or center2 (center of intersection2).</p> <p> Note that intersection2 and center2 appear rarely in the 6th column.</p>
Dataset for: Online Virtual Machine Provisioning under Uncertainty: A Robust Approach to Ultimate Resource Utilization
<div> <div>In cloud resource scheduling and management, efficiency and quality are two vital yet conflicting objectives. Cloud providers, such as Huawei, prioritize quality by avoiding hotspots and aim to optimize and increase the utilization efficiency of PM resources without compromising quality. The typical online VM scheduling problem in cloud practice can be stated as follows: given a fixed number of PMs and a queue of arriving VMs, the goal is to place as many VMs as possible onto the PMs while ensuring that no hotspots occur.</div> <br> <div>The trace consists of a total of 297 instances, where each instance is represented by a JSON file named in the format X-1.json, X-2.json, and X-3.json. Here, X represents the number of PMs capable of hosting the arriving VMs, ranging from 2 to 100. For example, 50-1.json indicates that there are 50 available PMs to host the arriving VMs. For each value of X, there are three replicas denoted by the suffixes 1, 2, and 3.</div> <br> <div>There are multiple flavors of PM available in our cloud service provider. Readers can refer to our online paper, which we will provide the link for below, to learn about the specific flavor we used for academic and testing purposes. However, they are also free to use other typical flavors as per their requirements.</div> <br> <div>Each JSON file contains information about the VMs waiting to be assigned to PMs, and the fields for these VMs are as follows (each line represents a VM):</div> <br> <div> <ul> <li><strong>Created_at_point</strong>: the timestamp when the VM arrives. The list is already sorted in ascending order based on the arrival times.</li> </ul> </div> <ul> <li><strong>memory</strong>: the memory capacity of the VM defined by its flavor, measured in gigabytes (GB).</li> <li><strong>duration_point</strong>: the timestamps indicating the duration of the VM's usage. Each timestamp represents a 5-minute interval in practice.</li> <li><strong>vm_util</strong>: the real utilized capacity of the VM at each timestamp, with a similar meaning as the Hotspot Resolution trace. The length of the list is equal to the value of "duration_point".</li> </ul> </div> <div> </div>
Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS) - code and data sets
<p>This repository contains the scripts for the paper in revision to the AAPS J: Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS) </p> <p>Authors: Niels Hendrickx, MSc, France Mentré, MD, PhD, Andreas Traschütz, MD, PhD, Cynthia Gagnon, PhD, Rebecca Schüle, MD, ARCA Study Group, EVIDENCE-RND consortium, Matthis Synofzik, MD, Emmanuelle Comets, PhD</p> <p>A simulated dataset (<strong>simulated_arsacs.csv</strong>) has been included in the repository to make the code executable as a standalone. Four main scripts have been provided in addition with the present Readme describing the files. The repository also includes 3 R objects and 2 folders which will be overwritten when the scripts are run, and are included as examples of the expected outputs. The main scripts are:</p> <p>- <strong>Script_imputation_selection.R</strong>: runs the covariate selection method. It uses a simulated dataset provided in the depot. The multiple imputation model is hardcoded as an input to the mice package to generate 10 imputed datasets, saved in current_directory/imputed_data_sets/df_arsacs_mi_i.csv. The script then runs the covariate selection method. The script prints out the list of selected covariates and returns a saemixObject containing the fit of the selected covariate model.<br> After the script executes, a list will be saved with the name of the selected covariates in the current directory (an example is included under the name "cov_matrix_model.RData" in the repository), the output of the selection, containing the whole history of runs will be saved under "final_covariate_model.RData", the list of selected covariate names will be saved under "list_covariates.RData".</p> <p>- <strong>source_mi.R</strong>: contains the functions used by Script_imputation_selection.R</p> <p>- <strong>script_bootstrap_indfit.R</strong>: This script loads "cov_matrix_model.RData" containing the matrix of covariate effects (used by saemix) and "list_covariates.RData", the list of covariates included, fits the model on the imputed data sets and computes its bootstrap distribution for each imputed data set (in the script, using only 20 samples for computation time, saved in current_directory/bootstrap/boot.arsacs.case.mi.i). It then computes the mean parameter and relative standard error of each parameter. It then computes the conditional distribution of each patient in each bootstrap samples and returns a data frame of individual predictions. The script will then plot 4 indivudal predictions. </p> <p>-<strong> source_bootstrap.R</strong>: contains the functions used by script_bootstrap_indfit.R</p> <p>Both scripts need the saemix package to run, which we haven’t included in the repository as it is freely available on the CRAN (https://cran.r-project.org/web/packages/saemix/index.html). Additional libraries we make use of in the code (MICE, tidyverse, ggplot2) also need to be installed prior to execution. <br>The R code provided can be further customised to be adapted to different scenarios.</p> <p>For the code to run, it is preferable to unzip the whole folder and set the working directory to the source file location as the script uses the "bootstrap" and "imputed_data_sets" sub-folders</p> <p>To execute this code, assuming the required libraries are available in the local R installation, please open an R session and run:<br>source("Script_imputation_selection.R") # for the covariate selection method (runtime: 3h on a i7-8565U laptop)<br>source("script_bootstrap_indfit.R") # to obtain individual trajectories (runtime: 1h on a i7-8565U laptop)</p>
HRS Hydrogen uncertainty budget
<p>Spreadsheet to determine the HRS total mass uncertainty</p>
Future wave climate in the Mediterranean Sea and associated uncertainty from an ensemble of 31 GCM-RCM wave simulations
<p>The data provided is used in a study aimed at assessing future changes in the Mediterranean wave climate. A total of 31 GCM-RCM simulations were used to characterize the wave climate during the historical (1979-2005), mid-century (2034-2060), and end-century (2074-2100) periods. Changes in seasonal significant wave height (Hs) and peak period (Tp) wave parameters are evaluated for both the mean and intense (quantile 0.95) wave climate, along with the shift in wave direction (wave peak dominant direction, θp) for sea states characterized as intense. The robustness of the climate change signal is evaluated following the guidelines outlined in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change. Additionally, using a wave hindcast as a reference, the changes in future extreme events are assessed by fitting a GEV to a unique and coherent set of bias-corrected annual maxima from each model.</p> <p>We provide data used to obtain the results of our study, comprising: 1) wave climate statistics for Hs, Tp, and θp for each model and each period studied; 2) two sets of annual maxima distribution for each model, which were bias-corrected assuming that the set of extreme events follows either a Gumbel distribution or a GEV distribution. For more details on the methods to obtain these files describing wave climate statistical as used in the study, please refer to Toomey et al., 2024: "Future wave climate in the Mediterranean Sea and associated uncertainty from an ensemble of GCM-RCM wave simulations."</p>
Disentangling Sources of Uncertainty in CLM5 Model Predictions: Water, Energy, and Carbon Fluxes at European Observation Sites
<p>The datasets include:</p> <ul> <li>EC data from Europement measurement sites in <a href="https://www.icos-cp.eu/data-products/2G60-ZHAK">ICOS</a>, <a href="https://fluxnet.org/login/?redirect_to=/data/download-data/">FLUXNETS</a>, and <a href="https://doi.org/10.34731/x9s3-Kr48">COSMOS-Europe</a>.</li> <li>Ensemble simulation data used for analysis</li> </ul> <p>The atmospheric forcings used in driving the model were all local measurements pre-processed using the script in GitHub repository <a href="https://github.com/FedoAIworld/CLM5-Disentangling-Uncertainty/tree/main/00_create_forcing_ds">CLM5-Disentangling-Uncertainty</a>.</p>
Uncertainty in Pedestrian Decision-Making in Urgent Scenarios Modulates Multi-Level Neural Hierarchies from Perception to Execution
<p><span>In urgent traffic scenarios, pedestrians exhibit decision-making uncertainty, significantly influencing safe interaction dynamics with automated vehicles. However, the inherent mechanisms of such decision behavior remain inadequately understood. To address this gap, we designed dynamic interactive stimulus experiments to replicate pedestrian-vehicle interactions in urgent scenarios, incorporating spatiotemporal pressure and introducing substantial penalties for decision failures. We employed multimodal data analysis, including behavioral data, electroencephalography (EEG) and eye-tracking data, to investigate the influence of urgency on uncertainty in decision-making and the underlying multi-level neural processes. Our findings demonstrate that as the urgency of the stimulus increases, humans adjust their decision objectives, resulting in an initial decrease followed by an increase in decision uncertainty when dealing with more urgent stimuli. Specifically, urgency augments top-down perceptual processes during the early perception stage. <span>Such a mechanism implies an enhanced dependence on prior experiences for perceptual </span></span><span><span><span>decision<span>-making in high-urgency situations. </span></span></span></span><span>While urgency accelerated motion preparation time during the decision-execution stage, it is noteworthy that the culmination of evidence accumulation (represented by the CPP peak) manifested later than the actual response. These results suggest that insufficient perceptual information and evidence accumulation may increase decision-making uncertainty. Our experimental study unveils a correlation between human decision-making uncertainty and scenario urgency, particularly within a defined urgency range. </span></p>
Optimisation of size and operation of stand-alone renewable-based systems with diesel and hybrid PHS-battery storage considering uncertainties
<p>Results of the optimal system found by the stochastic optimisation</p>
Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100Ⓡ
<p>Scripts and datasets that were used to obtain figures 4B, 6, 7, 8, 9, 10, and 11.</p>
Non-intrusive semi-analytical uncertainty quantification using Bayesian quadrature with application to CFD simulations
<p>The data contained in the uploaded '.zip' file is for some of the plots in the paper ‘Duan Y*, Eaton MD, Bluck MJ, 2021, Non-intrusive semi-analytical uncertainty quantification using Bayesian quadrature with application to CFD simulations, International Journal of Heat and Fluid Flow.’ (accepted)</p>
Uncertainties of monthly discharge data and parameters for the ungauged catchments of the Ethiopian Rift Valley Lake Basin (RVLB)
<p>In this study to quantify the uncertainty of the regionalization procedure, we apply all 14 regionalized models that were created for the leave-one-out evaluation to the ungauged catchments. With this regard, an ensemble of 14 predicted streamflow time series is produced for each ungauged catchment to reflect the regionalization uncertainty. The entire procedures used to create the dataset are provided within the manuscript.</p> <p>We provide the summary of the data below:</p> <p>1) We provided the Uncertainties of Monthly Discharge simulation for the ungauged catchments obtained from the regionalization. The data is provided by excel file as Uncertainties_of_Monthly_Discharge_Ungauged.xlsx. This file contains 35 sheets for the 35 ungauged catchment, and 14 prediction intervals (ensembles) on each sheet. The data ranges from 1995-2007 on a monthly scale and contains 156-row values for the 13-year simulation periods.</p> <p>2) We also provided the Uncertainties of parameters for the ungauged catchments. The data is prepared in the file (Uncertainties_of_parameters_ungauged_catchments.xlsx), which contains 9 parameters for the 14-prediction interval. These parameter values are saved in 35 sheets representing the 35 ungauged catchments in the order 1 to 35. The order of parameters is shown in Table 3 of the manuscript.</p> <p> </p>
Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC
<p>Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow is readily generalized to a wide range of systems, accelerates computational understanding and design.</p>
Planning for Resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia
<p>Climate change has become the greatest threat to the world's ecosystems. Locating and managing areas that contribute to the survival of key species under climate change is critical for the persistence of ecosystems in the future. Here we identify "Climate Priority" sites as coral reefs exposed to relatively low levels of climate stress that will be more likely to persist in the future. We present the first analysis of uncertainty in climate change scenarios and models, along with multiple objectives, in a marine spatial planning exercise and offer a comprehensive approach to incorporating uncertainty and trade-offs in any ecosystem. We first described each site using environmental characteristics that are associated with a higher chance of persistence (larval connectivity, hurricane influence and acute and chronic temperature conditions in the past and the future). Future temperature increases were assessed using downscaled data under four different climate scenarios (SSP1 2.6, SSP2 4.5, SSP3 7.0 and SSP5 8.5) and 57 model runs. We then prioritized sites for intervention (conservation, improved management or restoration) using robust decision-making approaches that select sites that will have a benign climate under most climate scenarios and models. The modeling work is novel because it solves two important issues. 1) It considers trade-offs between multiple planning objectives explicitly through Pareto analyses; and 2) It makes use of all the uncertainty around future climate change. Priority intervention sites identified by the model were verified and refined through local stakeholder engagement including assessments of local threats, ecological condition and government priorities. The workflow is presented for the Insular Caribbean and Florida, and at the national level for Cuba, Jamaica, Dominican Republic and Haiti. Our approach allows managers to consider uncertainty and multiple objectives for climate smart spatial management in coral reefs or any ecosystem across the globe.</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>
Figures 17–19 in A new species of Solaropsisfrom Amapá, Brazil (gastropoda: Solaropsidae) triggering uncertainty about the genus and redefinition of some species
Figures 17–19. Reproduction of relevant figures of solaropsids from the literature: 17, fig. 76 of Lister (1770); 18, pl. 63, fig. G3 of Favanne (1780); 19, fig. 3 of Hupé (1857) of Helix pellisboae.
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.