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648 results for “uncertainties”
Reconstructing Ecological Niche Evolution via Ancestral State Reconstruction with Uncertainty Incorporated
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Data from: Quantifying demographic uncertainty: Bayesian methods for integral projection models (IPMs)
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Data from: Bayesian and likelihood phylogenetic reconstructions of morphological traits are not discordant when taking uncertainty into consideration: a comment on Puttick et al
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Data from: Estimating correlated rates of trait evolution with uncertainty
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Data from: Identifying acne treatment uncertainties via a James Lind Alliance Priority Setting Partnership
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Data from: The impact of uncertainty on cooperation intent in a conservation conflict
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Data from: Phylogenetic uncertainty revisited: implications for ecological analyses
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Data from: How much is new information worth? Evaluating the financial benefit of resolving management 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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Data from: How long do anti-predator interventions remain effective? Patterns, thresholds and uncertainty
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Planning for Resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia
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Data from: Quantifying uncertainty of taxonomic placement in DNA barcoding and metabarcoding
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Implications of life history uncertainty when evaluating status in the Northwest Atlantic population of white shark (Carcharodon carcharias)
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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.
Data for T-MTT X-parameter Measurement Uncertainty Paper
<p>Data in support of T-MTT paper.</p>
Stochastic Modeling of Subglacial Topography Exposes Uncertainty in Water Routing at Jakobshavn Glacier
<p>Abstract:</p> <p>These data products accompany the paper "Stochastic Modeling of Subglacial Topography Exposes Uncertainty in Water Routing at Jakobshavn Glacier" (MacKie et al., in review). In this study, geostatistical techniques were used to generate an ensemble of topographic realizations that retain the spatial statistics of radar bed elevation measurements. The simulation was conditioned to local radar data and mass conservation bed estimates. This repository contains the radar and mass conservation conditioning data, the ensemble of topographic realizations, and coordinate data.</p> <p> </p> <p>Content and processing steps:</p> <p>The study area is 75.15 x 48.90 km^2. The grid cell resolution is 150 meters. Each digital elevation model (DEM) has 501 x 326 grid cells. The mass conservation DEM was obtained from BedMachine Greenland (Morlighem and others, 2017). The radar data were acquired from the Center for Remote Sensing of Ice Sheets (CReSIS) 2009 flights (Gogineni, 2012; Gogineni and others, 2014). A probabilistic modeling technique called sequential Gaussian co-simulation (Verly, 1993; Almeida and Journel, 1994; Journel, 1999; Remy, 2005) was used to generate the topographic realizations. The datasets are as follows:</p> <p> </p> <p>1) Jakobshavn_mass_conservation.txt - Mass conservation conditioning data</p> <p>2) Jakobshavn_radar_data.txt - Radar conditioning data</p> <p>3) Jakobshavn_simulation.txt - 250 topographic realizations. The shape of this file is 250 x 163326, where each column corresponds to one topographic realization. Each column should be reshaped to 501 x 326 to view the DEM.</p> <p>4) Jakobshavn_x_data.txt - Polar stereographic X coordinates in meters</p> <p>5) Jakobshavn_y_data.txt - Polar stereographic Y coordinates in meters</p> <p> </p> <p>References:</p> <p>Almeida, A. S., & Journel, A. G. (1994). Joint simulation of multiple variables with a Markov-type coregionalization model. <em>Mathematical Geology</em>, <em>26</em>(5), 565-588.</p> <p>Gogineni, P. (2012). CReSIS radar depth sounder data. <em>Center for Remote Sensing of Ice Sheets, Lawrence, KS https://data. cresis.-ku. edu</em>.</p> <p>Gogineni, S., Yan, J. B., Paden, J., Leuschen, C., Li, J., Rodriguez-Morales, F., ... & Gauch, J. (2014). Bed topography of Jakobshavn Isbræ, Greenland, and Byrd Glacier, Antarctica. <em>Journal of Glaciology</em>, <em>60</em>(223), 813-833.</p> <p>Journel, A. G. (1999). Markov models for cross-covariances. <em>Mathematical Geology</em>, <em>31</em>(8), 955-964.</p> <p>Morlighem, M., Williams, C. N., Rignot, E., An, L., Arndt, J. E., Bamber, J. L., ... & Fenty, I. (2017). BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo sounding combined with mass conservation. <em>Geophysical research letters</em>, <em>44</em>(21), 11-051.</p> <p>Remy, N. (2005). S-GeMS: the stanford geostatistical modeling software: a tool for new algorithms development. In <em>Geostatistics banff 2004</em> (pp. 865-871). Springer, Dordrecht.</p> <p>Verly, G. W. (1993). Sequential Gaussian cosimulation: a simulation method integrating several types of information. In <em>Geostatistics Troia’92</em> (pp. 543-554). Springer, Dordrecht.</p>
How to account for the uncertainty from standard toxicity tests in species sensitivity distributions: an example in non-target plants
<p>Raw data sets (.txt format) for the seven case studies in the paper entitled "How to account for the uncertainty from standard toxicity tests in species sensitivity distributions: an example in non-target plants" submitted to PLOS One in July 2020.</p>
Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions"
<p>Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions" submitted to Water Resources Research.</p> <p>Contains:</p> <ul> <li>SWMM template files.</li> <li>Objective function values for all model runs.</li> <li>Jupyter Notebooks used to create figures and tables for the article.</li> <li>Copy of rainfall runoff data available from <a href="https://doi.org/10.5281/zenodo.3931582">https://doi.org/10.5281/zenodo.3931582</a></li> </ul> <p>For python implementations of the Hydrograph Matching Algorithm (Ewen 2011) see <a href="https://doi.org/10.5281/zenodo.3923792">https://doi.org/10.5281/zenodo.3923792</a></p> <p>Ewen, John. “Hydrograph Matching Method for Measuring Model Performance.” <em>Journal of Hydrology</em> 408, no. 1–2 (September 2011): 178–87. <a href="https://doi.org/10.1016/j.jhydrol.2011.07.038">https://doi.org/10.1016/j.jhydrol.2011.07.038</a>.</p>
Development and uncertainty assessment of pedotransfer functions for predicting water contents at specific pressure heads
<p>There has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e. different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset in training and testing subsets, and outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov-Smirnov test for prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian regression process and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared to machine learning methods that estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling generating covariate shift. From these, a hundred sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncertainty. For correlated data, simple regression models performed as good as sophisticated machine learning methods. Using PTF-predicted water contents for van Genuchten retention parameter fitting may lead to a high uncertainty.</p>
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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.