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50 results for “Uncertainty Estimation”
Data from: The effect of bed roughness uncertainty on tidal stream power estimates for the Pentland Firth
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Estimating uncertainty in divergence times among three-spined stickleback clades using the multispecies coalescent
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Data from: Accounting for genotype uncertainty in the estimation of allele frequencies in autopolyploids
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Soil Organic Carbon Stock Estimates with Uncertainty across Latin America
This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America. SOC stocks (kg/m2) were estimated for the SOC and bulk density point measurements and a spatially explicit measure of the SOC estimation error was also calculated. A modeling ensemble, using a linear combination of five statistical methods (regression Kriging, random forest, kernel weighted nearest neighbors, partial least squared regression and support vector machines) was applied to the SOC stock data at (1) country-specific and (2) regional scales to develop gridded SOC estimates (kg/m2) for all of Latin America. Uncertainty estimates are provided for the two model predictions based on independent model residuals and their full conditional response to the SOC prediction factors.
Soil Organic Carbon Estimates and Uncertainty at 1-m Depth across Mexico, 1999-2009
This dataset provides an estimate of soil organic carbon (SOC) in the top one meter of soil across Mexico at a 90-m resolution for the period 1999-2009. Carbon estimates (kg/m2) are based on a field data collection of 2852 soil profiles by the National Institute for Statistics and Geography (INEGI). The profile data were used for the development of a predictive model along with a set of environmental covariates that were harmonized in a regular grid of 90x90 m2 across all Mexican states. The base of reference was the digital elevation model (DEM) of the INEGI at 90-m spatial resolution. A model ensemble of regression trees with a recursive elimination of variables explained 54% of the total variability using a cross-validation technique of independent samples. The error associated with the predictive model estimates of SOC is provided. A summary of the total estimated SOC per state, statistical description of the modeled SOC data, and the number of pixels modeled for each state are also provided.
EMIT L2B Estimated Mineral Identification and Band Depth and Uncertainty 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 the 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 2B Estimated Mineral Identification and Band Depth and Uncertainty (EMITL2BMIN) Version 1 data product provides estimated mineral identification and band depths in a spatially raw, non-orthocorrected format. Each EMITL2BMIN granule contains two Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Mineral Identification (EMIT_L2B_MIN) and Mineral Uncertainty (EMIT_L2B_MINUNCERT). The EMIT_L2B_MIN file contains the band depth (the depth of the identified spectral feature) and the identified mineral for each pixel. Two spectral groups, which correspond to different regions of the spectra, are identified independently and often co-occur. These estimates are generated using the [Tetracorder system](https://www.usgs.gov/publications/tetracorder-user-guide-version-44) ([code](https://github.com/PSI-edu/spectroscopy-tetracorder)) and are based on [EMITL2ARFL](https://doi.org/10.5067/EMIT/EMITL2ARFL.001) reflectance values. The EMIT_L2B_MINUNCERT file provides band depth uncertainty estimates calculated using surface Reflectance Uncertainty values from the EMITL2ARFL data product. The band depth uncertainties are presented as standard deviations. The fit score for each mineral identification is also provided as the coefficient of determination (r<sup>2</sup>) of the match between the continuum normalized library reference and the continuum normalized observed spectrum. Associated metadata indicates the name and reference information for each identified mineral, and additional information about aggregating minerals into different categories is available in the [emit-sds-l2b repository](https://github.com/emit-sds/emit-sds-l2b) and will be available as subsequent data products.The EMITL2BMIN data product includes a total of 19 Science Dataset (SDS) layers. There are four layers for each of the Spectral Groups (Group 1 and Group 2): Mineral Identification, Band Depth, Band Depth Uncertainties, and Fit Score. Additional layers consist of geometric lookup table (GLT) x values, GLT y values, latitude, longitude, elevation, associated spectral library record, mineral name, URL for the spectral library description, spectral group, spectral library, and spectral group index. A browse image with Group 1 Band Depth, Group 2 Band Depth, Group 1 Band Depth Uncertainty, and Group 2 Band Depth Uncertainty is also included.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.DisclaimerThis product is generated to support the EMIT mission objectives of constraining the sign of dust related radiative forcing. Ten mineral types are the core focus of this work: calcite, chlorite, dolomite, goethite, gypsum, hematite, illite+muscovite, kaolinite, montmorillonite, and vermiculite. A future product will aggregate these results for use in Earth System Models. Additional minerals are included in this product for transparency but were not the focus of this product. Further validation is required to use these additional mineral maps, particularly in the case of resource exploration. Similarly, the separation of minerals with similar spectral features, such as a fine-grained goethite and hematite, is an area of active research. The results presented here are an initial offering, but the precise categorization is likely to evolve over time, and the limits of what can and cannot be separated on the global scale is still being explored. The user is encouraged to read the Algorithm Theoretical Basis Document (ATBD) for more details.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.
NESOSIM-MCMC Multi-Reanalysis-Average Product With Uncertainty Estimates
<p><strong>Updates:</strong></p> <p>12 Feb 2025: extending output up to April 2023, adding ERA5 snowfall as ERA5.zip</p> <p><strong>Overview:</strong></p> <p>This repository contains output from the NASA Eulerian Snow On Sea Ice Model (NESOSIM; Petty et al., 2018; available at <a href="../doi/10.5281/zenodo.4448355">https://zenodo.org/doi/10.5281/zenodo.4448355</a> ) calibrated to snow depth and density observations using a Markov chain Monte Carlo (MCMC) approach (Cabaj et al. 2023; available at <a href="../doi/10.5281/zenodo.7644947">https://zenodo.org/doi/10.5281/zenodo.7644947</a> ) with ECMWF ERA5 (Hersbach et al., 2020), NASA GMAO MERRA-2 (Gelaro et al., 2017), and JMA JRA-55 (Kobayashi et al., 2015) reanalysis snowfall inputs, for the 1980-2020 time period. Reanalysis snowfall input used with NESOSIM is scaled to scaling factors calculated from CloudSat-derived monthly snowfall climatologies, interpolated over the NESOSIM model domain (Cabaj et al. 2020, 2023). The ERA5, MERRA-2 and JRA-55 reanalysis inputs and CloudSat scaling factors are included in this repository. Other forcing data used as inputs to NESOSIM to generate this output are provided in <a href="../records/7051062">https://zenodo.org/records/7051062</a> .</p> <p>The NESOSIM-MCMC-Average product is a snow-on-sea-ice product constructed from the average of MCMC-calibrated NESOSIM outputs when the model is run with CloudSat-scaled snowfall from ERA5, MERRA-2, and JRA55. This product also includes an estimate of uncertainty due to model parameter uncertainties (derived from the MCMC calibration process run for each reanalysis product) and due to differences between reanalysis snowfall products providing snowfall input to NESOSIM. Snow depth and bulk snow density, with associated uncertainty estimates, are provided.</p> <p><strong>Repository Structure</strong></p> <p>ERA5.zip, MERRA2.zip and JRA55.zip: Contain ERA5, MERRA-2, and JRA-55 (respectively) reanalysis snowfall data regridded for use as forcing input to NESOSIM, from 1980-2023. This data is stored as daily binary NumPy files (the default NESOSIM input format) and does not have CloudSat scaling applied. Corresponding ERA5 snowfall for NESOSIM input is available at <a href="../records/7051062">https://zenodo.org/records/7051062</a>.</p> <p>CloudSat_Scaling_Factors.zip: Contains netCDF files with monthly scaling factors generated from the monthly climatology of the CloudSat 2C-SNOW-PROFILE product, version P1_R05 (Wood et al., 2013, 2014; scaling method cf. Cabaj et al., 2020) to be applied to ERA5, MERRA-2, and JRA-55 snowfall in NESOSIM. To be placed in the anc_data folder when running the model.</p> <p>NESOSIM_MCMC-*.zip: NESOSIM output data from 1980-2020 for the model when MCMC-calibrated (following the approach in Cabaj et al., 2023) with snowfall input from ERA5, MERRA-2, and JRA-55, respectively. The NESOSIM-MCMC-Average product (calculated as the average of the outputs) is also included. Within each zip file, model output is included in the 'Output' subdirectory, and snow depth and density uncertainties estimated from the ensemble-propagated spread of the posterior MCMC distributions (cf. Cabaj et al., 2023) are included in the 'Uncertainty' subdirectory. For the multi-product average, uncertainties are calculated as the combined standard deviation of the three separate output ensembles, and are provided in separate files for snow depth and snow density. All output is stored in netCDF format.</p> <p><strong>References:</strong></p> <p>Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426.</p> <p>Cabaj, A., P. J. Kushner, A. A. Petty (2023), Automated calibration of a snow-on-sea-ice model. Earth and Space Science, 10, doi:10.1029/2022EA002655.</p> <p>Gelaro, R. et al. (2017), The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Journal of Climate, 30, 5419–5454, doi:10.1175/JCLI-D-16-0758.1.</p> <p>Hersbach, H. et al. (2020), The ERA5 Global Reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, doi:10.1002/qj.3803.</p> <p>Kobayashi, S. et al. (2015), The JRA-55 Reanalysis: General Specifications and Basic Characteristics, Journal of the Meteorological Society of Japan, 93, 5–48, doi:10.2151/jmsj.2015-001.</p> <p>Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018.</p> <p>Wood, N. B., T. S. L'Ecuyer, A. J. Heymsfield, G. L. Stephens, D. R. Hudak, P. Rodrigues (2014), Estimating snow microphysical properties using collocated multisensor observations. J. Geophys. Res. Atmos., 119, 8941-8961, doi:10.1002/2013JD021303.</p> <p>Wood, N. B., T. S. L'Ecuyer, F. L. Bliven, and G. L. Stephens (2013), Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity, Atmos. Meas. Tech., 6, 3635-3648, doi:10.5194/amt-6-3635-2013.</p>
Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation
This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In par- ticular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it re- lates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probabil- ity density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.
Evaluating Prognostics Performance for Algorithms Incorporating Uncertainty Estimates
Uncertainty Representation and Management (URM) are an integral part of the prognostic system development.1As capabilities of prediction algorithms evolve, research in developing newer and more competent methods for URM is gaining momentum.2Beyond initial concepts, more sophisticated prediction distributions are obtained that are not limited to assumptions of Normality and unimodal characteristics. Most prediction algorithms yield non-parametric distributions that are then approximated as known ones for analytical simplicity, especially for performance assessment methods. Although applying the prognostic metrics introduced earlier with their simple definitions has proven useful, a lot of information about the distributions gets thrown away. In this paper, several techniques have been suggested for incorporating information available from Remaining Useful Life (RUL) distributions, while applying the prognostic performance metrics. These approaches offer a convenient and intuitive visualization of algorithm performance with respect to metrics like prediction horizon and α-λ performance, and also quantify the corresponding performance while incorporating the uncertainty information. A variety of options have been shortlisted that could be employed depending on whether the distributions can be approximated to some known form or cannot be parameterized. This paper presents a qualitative analysis on how and when these techniques should be used along with a quantitative comparison on a real application scenario. A particle filter based prognostic framework has been chosen as the candidate algorithm on which to evaluate the performance metrics due to its unique advantages in uncertainty management and flexibility in accommodating non-linear models and non-Gaussian noise. We investigate how performance estimates get affected by choosing different options of integrating the uncertainty estimates. This allows us to identify the advantages and limitations of these techniques and their applicability towards a standardized performance evaluation method.
A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components
This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework and specifically for a Kalman filter appli- cation to electronics components. In particular, it explores the implication of modeling remaining useful life prediction as a stochastic process and how it relates to remaining useful life computation by statistical models, to uncertainty representation and management, and to the role of prognostics in decision-making. A discussion on how uncertainty propagates from the health state estimation process through the health state forecasting process is provided. Remaining useful life computation steps under uncertainty are pre- sented and analytical results on uncertainty quantification are provided under a simplified scenario. A proper propagation of uncertainty through the RUL prediction step as well as its correct interpretation are key to developing decision-making methodologies that make use of the remaining useful life prediction estimates and their corresponding uncertainties in order to make actionable choices that will optimize reliability, operations or safety in view of the prognostics information.
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.