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50 results for “Uncertainty Estimation”
Data from: Joint estimation of survival and breeding probability in female dolphins and calves with uncertainty in state assignment
While the population growth rate in long-lived species is highly sensitive to adult survival, reproduction can also significantly drive population dynamics. Reproductive parameters can be challenging to estimate as breeders and non-breeders may vary in resighting probability and reproductive status may be difficult to assess. We extended capture–recapture (CR) models previously fitted for data on other long-lived marine mammals to estimate demographic parameters while accounting for detection heterogeneity between individuals and state uncertainty regarding reproductive status. We applied this model to data on 106 adult female bottlenose dolphins observed over 13 years. The detection probability differed depending on breeding status. Concerning state uncertainty, offspring were not always sighted with their mother, and older calves were easier to detect than young-of-the-year (YOY), respectively 0.79 (95% CI 0.59–0.90) and 0.58 (95% CI 0.46–0.68). This possibly led to inaccurate reproductive status assignment of females. Adult female survival probability was high (0.97 CI 95% 0.96–0.98) and did not differ according to breeding status. Young-of-the-year and 1-year-old calves had a significantly higher survival rate than 2-year-old (respectively 0.66 CI 95% 0.50-0.78 and 0.45 CI 95% 0.29–0.61). This reduced survival is probably related to weaning, a period during which young are exposed to more risks since they lose protection and feeding from the mother. The probability of having a new YOY was high for breeding females that had raised a calf to the age of 3 or lost a 2-year-old calf (0.71, CI 95% 0.45– 0.88). Yet this probability was much lower for non-breeding females and breeding females that had lost a YOY or a 1-year-old calf (0.33, 95% CI 0.26–0.42). The multievent CR framework we used is highly flexible and could be easily modified for other study questions or taxa (marine or terrestrial) aimed at modelling reproductive parameters.
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>
The Data for 'Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation'
<p>Produced synthetic kilonova spectra for 'Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation' using the Sedona radiative transfer code. Each model is an h5 file with the following groups:</p> <ul> <li>Lnu - Array of length # of timesteps by # of frequency bins that contains the spectral sequence of each kilonova model in cgs units (erg/s/Hz)</li> <li>click - Array of length # of timesteps by # of frequency bins for the number of Monte Carlo particles that make up Lnu</li> <li>mu - Center of polar angular bins (not useful since simulations are 1D)</li> <li>mu_edges - Edges of polar angular bins (not useful since simualtions are 1D)</li> <li>nu - Array of length # of frequency bins that are the central frequencies of a bin (Hz)</li> <li>nu_edges - Array of length # of frequency bins +1 that are the edges of each frequency bin (Hz)</li> <li>phi - Center of azimuthal angular bins (not useful since simulations are 1D)</li> <li>phi_edges - Edges of azimuthal angular bins (not useful since simulations are 1D)</li> <li>time - Times at which each spectrum was generated (Days)</li> <li>time_edges - Edges of time bins (Days)</li> </ul> <p>Each kilonova spectra file is named according to <atomic dataset>_<thermalization prescription>_KN_<lanthanide fraction>X_lan_<characteristic velocity>v_<mass>M_1D_spec.h5 where:</p> <ul> <li>atomic dataset is one of: HULLAC, ATOMIC, or Autostructure</li> <li>thermalization prescription is one of: local or global</li> <li>lanthanide fraction is the fraction of lanthanides in the ejecta by mass</li> <li>characteristic velocity is the kinetic velocity of the ejecta in units of the speed of light</li> <li>mass is the mass of the ejecta in units of solar masses</li> </ul>
Trained Surface Layer Models and Metrics for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
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Data from: Uncertainty in geographic estimates of performance and fitness
1. Thermal performance curves (TPCs) have become key tools for predicting geographic distributions of performance by ectotherms. Such TPC-based predictions, however, may be sensitive to errors arising from diverse sources. 2. We analyzed potential errors that arise from common choices faced by biologists integrating TPCs with climate data by constructing case studies focusing on experimental sets of TPCs and simulating geographic patterns of mean performance. We first analyzed differences in geographic patterns of performance derived from two pairs of commonly used TPCs. Mean performance differed most (up to 30%) in regions with relatively constant mean temperatures similar to those at which the TPCs diverged the most. 3. We also analyzed the effects of thermal history by comparing geographic estimates derived from (1) a broad TPC based on short-term measurements of insect larvae (Manduca sexta) with a history of exposure to thermal variation versus (2) a narrow TPC based on long-term measurements of larvae held at constant temperatures. Estimated mean performance diverged by up to 40%, and differences were magnified in simulated future climates. 4. Finally, to quantify geographic error arising from statistical error in fitted TPCs, we propose and illustrate a bootstrapping technique for establishing 95% prediction intervals on mean performance at each location (pixel). 5. Collectively, our analyses indicate that error arising from several underappreciated sources can significantly affect the mean performance values derived from TPCs, and we suggest that the magnitudes of these errors should be estimated routinely in future studies.
Supplemental data for "Uncertainty in Land Use Obscures Global Soil Organic Carbon Stock Estimates"
<p>These are supporting data for the manuscript: "Uncertainty in Land Use Obscures Global Soil Organic Carbon Stock Estimates." They include data for the figures showing the spatial dynamics of cropland and LULCC-induced SOC loss etc, including Fig.3, Fig.6, Fig.9, and Fig.10.</p>
Fig. 3 in Validation and uncertainty estimation of analytical method for quantification of phytochelatins in aquatic plants by UPLC-MS
Fig. 3. Contribution of the sources (%) to the total uncertainty for the quantification of GSH and PCs in the L. gibba.
Fig. 1. a in Validation and uncertainty estimation of analytical method for quantification of phytochelatins in aquatic plants by UPLC-MS
Fig. 1. a) Total ion chromatogram (TIC) for the L. gibba sample, b) Extracted ion chromatogram of GSH and PCs from the TIC of L. gibba, c) Extracted ion chromatogram of GSH and PCs from the standard solution at 10 μg mL 1.
Fig. 2 in Validation and uncertainty estimation of analytical method for quantification of phytochelatins in aquatic plants by UPLC-MS
Fig. 2. Ishikawa diagram representing the main sources of uncertainties for measuring GSH and PC concentration in aquatic plants.
Estimation of the Uncertainties Introduced in Thermal Map Mosaic: A Case of Study with PIX4D Mapper Software
<p>Available data sets, used to analyse problems related to thermal mapping obtained from thermal data acquired from unmanned aerial systems (UAS) equipped with thermal cameras. We focused on an accurate analysis of uncertainties introduced by the PIX4D Mapper software.</p>
Estimating Setup Uncertainty in Pediatric Proton Therapy Using Volumetric Images
ClinicalTrials.gov study NCT04125095. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Estimating uncertainty in multivariate responses to selection
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Data from: Joint estimation of survival and breeding probability in female dolphins and calves with uncertainty in state assignment
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Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
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Data from: Accounting for uncertainty in gene tree estimation: summary-coalescent species tree inference in a challenging radiation of Australian lizards
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Data from: Estimating correlated rates of trait evolution with uncertainty
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Data from: Quantifying and reducing uncertainties in estimated soil CO2 fluxes with hierarchical data-model integration
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Data from: Uncertainty in geographic estimates of performance and fitness
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EMIT L2A Estimated Surface Reflectance and Uncertainty and Masks 60 m V001
The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/).The EMIT Level 2A Estimated Surface Reflectance and Uncertainty and Masks (EMITL2ARFL) Version 1 data product provides surface reflectance data in a spatially raw, non-orthocorrected format. Each EMITL2ARFL granule consists of three Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Reflectance (EMIT_L2A_RFL), Reflectance Uncertainty (EMIT_L2A_RFLUNCERT), and Reflectance Mask (EMIT_L2A_MASK). The Reflectance file contains surface reflectance maps of 285 bands with a spectral range of 381-2493 nanometers (nm) at a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Reflectance Uncertainty file contains uncertainty estimates about the reflectance captured as per-pixel, per-band, posterior standard deviations. The Reflectance Mask file contains six binary flag bands and two data bands. The binary flag bands identify the presence of features including clouds, water, and spacecraft which indicate if a pixel should be excluded from analysis. The data bands contain estimates of aerosol optical depth (AOD) and water vapor.Each NetCDF4 file holds a location group containing a geometric lookup table (GLT) which is an orthorectified image that provides relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files will also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT’s spatial resolution.Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules at the end of an orbit segment reaching 150 km in length.Known Issues:* Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.* Possible Reflectance Discrepancies: Due to changes in computational architecture, EMITL2ARFL reflectance data produced after December 4, 2024, with Software Build 010621 and onward may show discrepancies in reflectance of up to 0.8% in extreme cases in some wavelengths as compared to values in previously processed data. These discrepancies are generally lower than 0.8% and well within estimated uncertainties. Between earlier builds and Build 010621, neither resulting output should be interpreted as more ‘correct’ than the other, as their results are simply convergence differences from an optimization search. Most users are unlikely to observe the impact.
Estimating uncertainty in divergence times among three-spined stickleback clades using the multispecies coalescent
<p>Incomplete lineage sorting (ILS) can lead to biased divergence time estimates. To explore if and how ILS has influenced the results of a recent study of worldwide phylogeny of three-spined sticklebacks (Gasterosteus aculeatus), we estimated divergence times among major clades by applying both a concatenation approach and the multispecies coalescent (MSC) model to single-nucleotide polymorphisms. To further test the influence of different calibration strategies, we applied different calibrations to the root and to younger nodes in addition to the ones used in the original study. Both the updated calibrations and the application of the MSC model influenced divergence time estimates, sometimes significantly. The new divergence time estimates were more ancient than in the previous study for older nodes, whereas the estimates of younger nodes were not strongly affected by the re-analyses. However, given the applied MSC method employs a simple substitution model and cannot account for changes in population size, we suggest that different analytical approaches and calibration strategies should be used in order to explore uncertainty in divergence time estimates. This study provides a valuable reference timeline for the ages of worldwide three-spined stickleback populations and emphasizes the need to embrace, rather than obscure, uncertainties around divergence time estimates.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.