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648
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Dataset results
648 results for “uncertainties”
Data from: Sensitive response of atmospheric oxidative capacity to the uncertainty in the emissions of nitric oxide (NO) from soils in Amazonia
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Data from: Cooperative breeding in birds increases the within-year fecundity mean without increasing the variance: A potential mechanism to buffer environmental uncertainty
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Navigating uncertainty in environmental DNA detection of a nuisance marine macroalga
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Data from: Quantification and mitigation of uncertainties in thermal conductivity measurements using a modified ASTM D5470 thermal resistance tester
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Data for: Uncertainty about old information results in differential predator memory in tadpoles
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Data from: navigating uncertainty: managing herbivore communities enhances savanna ecosystem resilience under climate change
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The dilemma of objective function selection for sensitivity and uncertainty analyses of semi-distributed hydrologic models across spatial and temporal scales
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Data from: Retracing the Hawaiian silversword radiation despite phylogenetic, biogeographic, and paleogeographic uncertainty
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Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
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Data from: A multilocus phylogeny of the fish genus Poeciliopsis: solving taxonomic uncertainties and preliminary evidence of reticulation
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Understanding the Influence of Parameter Value Uncertainty on Climate Model Output: Developing an Interactive Web Dashboard
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Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation
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Ancient introgression in mouse lemurs (Microcebus:Cheirogaleidae) explains 20 years of phylogenetic uncertainty
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Data for: Weighting by gene tree uncertainty improves accuracy of quartet-based species trees
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Data from: Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation
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Set of European CO2 and CO emission grids representing emission uncertainties
<p>This dataset was prepared by TNO as a contribution to the H2020 project CHE and the H2020 project VERIFY. The basis is a high-resolution (~1x1 km) emission inventory providing CO<sub>2</sub> and CO (from fossil fuels and biofuels separately) over western Europe (2ºW - 19ºE, 47ºN - 56ºN). The reported emissions by European countries to UNFCCC (CO<sub>2</sub>) and to EMEP/CEIP (CO) have been used and where needed gap-filled or replaced with emission data from the GAINS model. These country-level emissions are disaggregated in space using a consistent spatial distribution methodology, whereas large point sources are listed with their exact locations. This approach is similar to the one described by Kuenen et al., (ACP, 2014). Emissions are reported per GNFR sector, with an extra split for road transport.</p> <p>The emission grids that are part of this dataset are a variation on the base grid, representing the uncertainty in the emission data. Each grid is equally plausible. The grids have been created using a Monte Carlo approach. The uncertainties in the underlying data used to create the base grid (emissions: activity data and emission factors, spatial proxies) have been collected (either from country reports or based on expert judgement). Through the Monte Carlo simulation these uncertainties, taking into account error correlations between some sub-sectors, are combined to create ten new emission grids. The spread in emissions between these emission maps gives an indication of the uncertainty in the emissions.</p> <p>The grid files (in .csv and .nc format) contain annual total emissions per grid cell for the year 2015. A separate file has been prepared for each ensemble member in the Monte Carlo simulation (indicated with M). The unit in the files is kg/yr.</p> <p>A detailed description of the Monte Carlo simulation is presented in:</p> <p>Super, I., Dellaert, S. N. C., Visschedijk, A. J. H., and Denier van der Gon, H. A. C.: Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-696, in review, 2019.</p> <p><strong>N.B. It is important to note that 10 maps are not sufficient to describe the sometimes complex uncertainty structures, for example in the case of lognormal uncertainty distributions. The interpretation of the uncertainty based on these 10 maps should therefore be done with care.</strong></p> <p><strong>NB. Despite efforts to prevent negative emissions to occur in the grid maps, some negative values are still present. In local studies this might cause some issues, and we recommend to set negative emissions to zero in those cases.</strong></p>
Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake
<p>Simulation output as described in Meyerholt, J., Sickel K., and Zaehle, S., (2020), Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake, Global Change Biology, doi:10.1111/gcb.15114</p> <p>Data in the file <a href="https://zenodo.org/api/files/62e7c4a2-96e9-46b8-810f-c4c3c24b06c0/ocn4magicc_carbon_model.nc?versionId=cb25734e-52ce-42fa-b238-9b7192baf4dd">ocn4magicc_carbon_model.nc</a> describe the carbon-only version of the model, <a href="https://zenodo.org/api/files/62e7c4a2-96e9-46b8-810f-c4c3c24b06c0/ocn4magicc_carbon_model.nc?versionId=cb25734e-52ce-42fa-b238-9b7192baf4dd">ocn4magicc_nitrogen_models.nc </a>describe the carbon-nitrogen model outputs.</p>
Companion for "Measuring Phenology Uncertainty with Large Scale Image Processing"
<p>This is the software and dataset companion for the paper entitled "Measuring Phenology Uncertainty with Large Scale Image Processing". Further instructions can be found in the README.org file.</p>
Using the history of the Antarctic Ice Sheet to reduce uncertainties in projections of global sea level rise
<p>Ice sheet models are the most descriptive tools available to simulate the future evolution of the Antarctic Ice Sheet (AIS), including its contribution towards changes in global sea level. However, our knowledge of the dynamics of the coupled ice-ocean-lithosphere system is inevitably limited, in part due to a lack of observations. Furthermore, to build computationally efficient models that can be run for multiple millennia, it is necessary to use simplified descriptions of ice dynamics. Ice sheet modelling is therefore a poorly constrained exercise. The past evolution of the AIS provides an opportunity to improve the description of physical processes within ice sheet models and, therefore, to constrain our understanding of the role of the AIS in driving changes in global sea level.</p> <p>We use the Parallel Ice Sheet Model (PISM) to demonstrate how past changes can be used to improve our ability to predict the future evolution of the AIS. A large perturbed-physics ensemble is generated, spanning uncertainty in the parameterisations of key physical processes within the model. A Latin hypercube approach is used to optimally sample the range of uncertainty in parameter values. This perturbed-physics ensemble is used to simulate the evolution of the AIS from the Last Glacial Maximum (21,000 years ago) until 5,000 years into the future. Records of past ice sheet thickness and extent are then used to determine which ensemble members are the most realistic. This allows us to use the known history of the AIS to constrain our understanding of its past contribution towards changes in global sea level. Critically, it also allows us to determine which ensemble members are most likely to generate realistic projections of the future evolution of the AIS. This enables us to use past changes in the AIS to reduce uncertainty in projections of future sea level rise.</p>
Reducing uncertainties in projections of global sea level rise
<p>Ice sheet models are the most descriptive tools available to simulate the future evolution of the Antarctic Ice Sheet (AIS), including its contribution towards changes in global sea level. However, our knowledge of the dynamics of the coupled ice-ocean- lithosphere system is inevitably limited, in part due to a lack of observations. Furthermore, to build computationally efficient models that can be run for multiple millennia, it is necessary to use simplified descriptions of ice dynamics. Ice sheet modelling is therefore a poorly constrained exercise. The past evolution of the AIS provides an opportunity to improve the description of physical processes within ice sheet models and, therefore, to constrain our understanding of the role of the AIS in driving changes in global sea level.<br> <br> We use the Parallel Ice Sheet Model (PISM) to demonstrate how past changes can be used to improve our ability to predict the future evolution of the AIS. A large perturbed-physics ensemble is generated, spanning uncertainty in the parameterisations of key physical processes within the model. A Latin hypercube approach is used to optimally sample the range of uncertainty in parameter values. This perturbed-physics ensemble is used to simulate the evolution of the AIS from the Last Glacial Maximum (21,000 years ago) until 5,000 years into the future. Records of past ice sheet thickness and extent are then used to determine which ensemble members are the most realistic. This allows us to use the known history of the AIS to constrain our understanding of its past contribution towards changes in global sea level. Critically, it also allows us to determine which ensemble members are most likely to generate realistic projections of the future evolution of the AIS. This enables us to use past changes in the AIS to reduce uncertainty in projections of future sea level rise.</p>
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.