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648 results for “uncertainties”
Dataset for "Regional Uncertainty Analysis in the Air-Sea CO2 Flux"
<p>This repository contains processed and output data used in the "Regional Uncertainty Analysis in the Air-Sea CO2 Flux" project. </p> <ul> <li><strong>fractional-uncertanties-1x1-1993-2022.nc </strong>: fractional uncertanies calculated with FluxError</li> </ul> <p>The following is the processed data used to calculate fractional uncertanties.</p> <p><strong>Individual Datasets</strong></p> <p>Sea Surface Temperature (SST)</p> <ul> <li><strong>oisst-1x1-1993-2022.nc : </strong>NOAA SST</li> <li><strong>cobe2-1x1-1993-2022.nc :</strong> COBE2 SST </li> <li><strong>esa-1x1-1993-2022.nc : </strong>ESA SST</li> <li><strong>ostia-1x1-1993-2022.nc : </strong>OSTIA SST</li> </ul> <p>10m Wind Speed</p> <ul> <li><strong>ccmp-1x1-1993-2022.nc : </strong>CCMP 10m wind speed</li> <li><strong>jra3q-wind-1x1-1993-2022.nc : </strong>JRA wind speed</li> <li><strong>era5-wind-1x1-1993-2022.nc : </strong>ERA5 wind speed</li> </ul> <p>Sea Surface Salinity (SSS)</p> <ul> <li><strong>en4-1x1-1993-2022.nc : </strong>EN4 salinity </li> <li><strong> glorys-1x1-1993-2022.nc :</strong> GLORYS salinity <strong> </strong></li> <li><strong>oras5-1x1-1993-2022.nc :</strong> ORAS5 salinity </li> </ul> <p>Atmospheric xCO2</p> <ul> <li><strong>noaa-mbl_197901-202301_1x1.nc : </strong>atmospheric xCO2</li> </ul> <p>Ocean pCO2</p> <ul> <li><strong>pco2-1x1-1993-2022.nc : </strong>Global Carbon Budget ocean model and data product output, converted to pCO2</li> </ul> <p>Sea Level Pressure </p> <ul> <li><strong>era5-slp-1x1-1993-2022.nc : </strong>ERA5 sea level pressure</li> </ul> <p>1 Degree Ocean Mask</p> <ul> <li><strong>ocean-mask_invariant_1x1.nc : </strong>Ocean mask </li> </ul> <p><strong>Merged datasets: </strong>these datasets are larger and contain the ensemble of datasets above merged into single files</p> <ul> <li><strong>salinity-1x1-1993-2022.nc : </strong>merged salinity datasets</li> <li><strong>sst-1x1-1993-2022.nc : </strong>merged SST datasets</li> <li><strong>wind-1x1-1993-2022.nc : </strong>merged wind speed datasets</li> </ul>
Effects of intolerance of uncertainty on subjective and psychophysiological measures during threat acquisition and delayed threat extinction
<p>This dataset includes measurements of intolerance of uncertainty (Intolerance of Uncertainty Scale [Freeston et al., 1994]), trait anxiety (State-Trait Anxiety Inventory [Spielberger et al., 1983]), skin conductance response (SCR), fear potentiated startle (FPS) and fear ratings (RAT) acquired in a differential fear conditioning paradigm with habituation and threat acquisition training on one day and extinction training, mood induction (by presenting negative vs. neutral slides), re-extinction training, reinstatement and reinstatement-test 24h later. Overall, 66 participants (female = 44, aged between 18 and 40 years, M = 25.76, SD = 5.82) took part in the study. Several participants had to be excluded due to technical issues (n = 3), non-responding (SCR: n = 2; auditory startle blink: n = 1) and no SCRs to the CSs (n = 1). Visual CSs were two shapes resembling snowflakes. The US consisted of a train of three 2 ms electrotactile square-waves (inter stimulus interval, ISI: 50 ms) and was delivered 7.9 s after each CS+ onset (100% reinforcement rate) during threat acquisition training and three times during reinstatement. The duration of the ITIs ranged from 10 to 13 s (M = 11.5). For SCR measurements, a 1 Hz lowpass filter and a gain of 5 or 10 μΩ were applied. SCR data were scored by using the semi-automatic scoring system Autonomate (Green et al., 2014), down sampled to 10 Hz and scored as the first response within 0.9 to 4 s after CS onset as SCR from trough to peak with a maximum rise time of 5 s. SCRs were square root transformed to reduce skew and z-scored within individuals across trials for day 1 and day 2 separately. To elicit the auditory startle blink, a 95 dB white noise burst was presented simultaneously on both ears. Startle probes were administered 6 or 8 s after the ITI-onset and 6 or 7 s after CS-onset. A gain of 5000 at 1000 Hz and a band-pass filter (28–500 Hz) were applied. Data were rectified and integrated online (averaged over 20 samples) and scored semi-automatically by using a custom-made computer program (EDA View, developed by Prof. Dr. Matthias Gamer, University of Würzburg) as trough to peak 20–120 ms after startle probe onset. For analyses, FPS data was z-scored within individuals across trials for day 1 and day 2 separately. To acquire fear ratings, participants rated throughout the experiment, how much stress, fear, and tension they experienced, when they last saw the CSs. Answers had to be logged in via button press within 7 s on a visual analog scale (VAS) ranging from zero (answer = none) to 100 (answer = maximum). Unlogged ratings were considered as missing values.</p>
The CloudSat-CALIPSO Cloud Amount Uncertainty product
<p>Version 1.0 of the dataset. The peer-reviewed publication for this dataset has been published in Remote Sensing, 2021, 13(4), 807, and can be accessed here <a href="https://doi.org/10.3390/rs13040807">https://doi.org/10.3390/rs13040807</a>. Please cite this when using the dataset.</p> <p>The<strong> </strong>‘CloudSat-CALIPSO Cloud Amount Uncertainty’ product provides information about mean annual and mean monthly cloud amount, at 40 vertical levels (480 m), and four spatial resolutions (1°, 2.5°, 5°, and 10°), as derived from the joint CloudSat-CALIPSO lidar-radar observations (2006–2011). For the very first time, the CloudSat-CALIPSO climatology comes with a quantitative uncertainty assessment - bootstrapped confidence intervals for mean values. The width of confidence intervals is an essential element in studying spatial and/or temporal variation in cloud amount with satellite profiling instruments. Uncertainty data are provided at four confidence levels (85%, 90%, 95%, 99%). Data products are distributed as HDF4 files, and can be accessed under the CC BY 4.0 license at doi:10.5281/zenodo.6113205. See the Product documentation file ( LIRAC.conf.v01_doc01.pdf ) for details.</p>
Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
<p>This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.</p>
Dataset of "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification"
<p>Dataset of the article "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification" (https://doi.org/10.1016/j.expthermflusci.2022.110756). Local similarity between non-time-resolved snapshots is enforced by KNN to extract high-resolution velocity fields and estimate the uncertainty of the measurements.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/KNN-PTV.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Random and systematic uncertainties for OMPS-LP ozone profiles
<p>This data set contains random and systematic uncertainties for ozone profiles retrieved at the University of Bremen from OMPS-LP observations. The uncertainties are expressed in relative values and are reported as vertical profiles (every 5 km) and as a function of latitude (5 bands, i.e., SH polar, SH mid-latitude, tropics, NH mid-latitudes, NH polar) and season. The user can associate to any OMPS-LP ozone profile the uncertainty corresponding to the appropriate latitude-season bin. The description of the uncertainties is provided in the AMT paper "Assessment of the error budget for stratospheric ozone profiles retrieved from OMPS limb scatter measurements", Arosio et al. 2022.</p>
Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent
<p>Code and data to reproduce figures in manuscript entitled "Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent" published in Hydrology and Earth System Sciences (https://hess.copernicus.org/preprints/hess-2022-136/).</p> <p>The contents include three folders, "Codes", "Data", and "Figures". In "Codes" folder, R scripts are listed in the order needed to reproduce the figures. All code is written in R version 4.2.0. Data sets needed to reproduce figures are provided in "Data" folder (Rdata format). The pdf files in "Figures" folder are outputs generated from the corresponding R scripts. Note that final figures in the article were produced by combining multiple figures using a vector graphics software (Inkscape) or PowerPoint. Please contact Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>) with any questions. </p> <p>Preferred citation: Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M. (2022). Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2022-136.</p> <p>Corresponding author: Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>; <a href="mailto:escho@umd.edu">escho@umd.edu</a>)</p>
Model projections of North Sea cod under deep uncertainty
<p>This is model output generated with code publicly available on Github (https://github.com/imf-uham/DMDU_North_Sea).</p>
Glacier runoff projections and their multiple sources of uncertainty in the Patagonian Andes (40-56°S)
<p>This dataset contains the catchment scale results of the study: "<strong>Unravelling the sources of uncertainty in glacier runoff projections in the Patagonian Andes (40–56° S)</strong>". The results are disaggregated in the following files (for more details, please read the README file):</p> <p><em>- basins_boundaries.zip:</em> Contains the polygons (in .shp format) of the studied catchments. Each catchment is identified by its "basin_id".</p> <p><em>- dataset_historical.csv: </em>Summarises the historical conditions of each glacier at the catchment scale (area, volume and reference climate).</p> <p><em>- dataset_future.csv: </em>Summarises the future glacier climate drivers and their impacts at the catchment scale. </p> <p><em>- dataset_signatures.csv: </em>Summarises the glacio-hydrological signatures of each glacier at the catchment scale.</p> <p><strong>Citation (preprint under review): </strong></p> <p>- Aguayo, R., Maussion, F., Schuster, L., Schaefer, M., Caro, A., Schmitt, P., Mackay, J., Ultee, L., Leon-Muñoz, J., and Aguayo, M.: Assessing the glacier projection uncertainties in the Patagonian Andes (40–56° S) from a catchment perspective, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2325, 2023.</p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty
<p>Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"</p>
Supporting data: "Uncertainty in sea level rise projections due to the dependence between contributors"
<p>These files contain the data analyzed in Le Bars 2018. The paper is available on EarthArXiv (https://eartharxiv.org/uvw3s/) and was submitted to Earth's Future.</p> <p>The NetCDF files contain the Probability Density Functions output from the Probabilistic Sea Level Projection (PSLP) model version 1.</p> <p>Simulations are:<br> IPCC1: The control IPCC AR5 simulation<br> IPCC2: The same but assuming independence between sea level contributors<br> IPCC3: The same but assuming correlation of 1 between sea level contributors<br> Prob1: The control simulation from the probabilistic model<br> Prob2: Assuming independence<br> Prob3: Assuming correlation of 1 between sea level contributors<br> Prob4: Low dependence case<br> Prob5: High dependence case<br> Prob6 to Prob9: Sensitivity experiments replacing each contributor by its expected value.</p> <p>The matrices of Spearman correlation for year 2100 for all experiments are called: <br> SpearmanCorr_namelist*_*.txt</p> <p>The Table*.txt files contain the data used to make tables of sea level percentiles in the paper.</p> <p>The pdf files contain the figures used in the paper and additional pannels not included in the paper.</p> <p>Reference:<br> Le Bars, D. (2018, March 8). Uncertainty in sea level rise projections due to the dependence between contributors. http://doi.org/10.17605/OSF.IO/UVW3S</p>
Examples dataset in conformity assessment with measurement uncertainty
<p>Thie dataset summarizes examples that can be used to validate the software developed as part of 17SIP05 CASoft project, which aims at popularizing the use of the methodology described by the reference document JCGM106:2012 for decision-making in conformity assessment problems. </p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
Regional scale shear wave velocity profiles for ground response analyses and uncertainties evaluations – the Piedmont Region (NW Italy) Database
<p>The databases provide detailed information for the Piedmont region in Northwest Italy, offering a view of its geological and geophysical characteristics:</p> <ul> <li><strong>Geological-Geomorphological Database</strong>: Includes 13 distinct Geological-Geomorphological Domains (GGD) in shapefile format. It supports spatial analysis and visualization, based on data from the Geological Map of the Piedmont Region at a 1:250,000 scale.</li> <li><strong>Geotechnical Database</strong>: Contains geotechnical data on bedrock depth and texture attributes derived from available logs in CSV format. Georeferenced using UTM coordinates (WGS84 UTM32N), it includes depth values and texture codes (C for clay, G for gravel, S for sand, R for rock, X for not available).</li> <li><strong>Geophysical Database</strong>: Provides data on shear wave velocity (Vs) profiles in CSV format. Georeferenced with UTM coordinates (WGS84 UTM32N), it includes layer interface depth and shear wave velocity above each layer.</li> </ul>
Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification
<p>This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. For an analysis see the article below.</p>
Ensemble projections (+ uncertainties) of contemporary (2012-2031) and future (2081-2100) mean annual plankton/phytoplankton/zooplankton species diversity (and species turn-over in time) for the global surface open ocean.
<p><em><strong>Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). </strong></em></p> <p>The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/).</p> <p>In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors.</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Data and codes for 'A Bayesian Approach to Blood Rheological Uncertainties in Aortic Hemodynamics'
<p>This submission is supplementary material in the form of data and codes used in and for the manuscript 'A Bayesian Approach to Blood Rheological Uncertainties in Aortic Hemodynamics' submitted to the International Journal of Numerical Methods in Biomedical Engineering (currently under review).</p>
Photometric Redshifts for Cosmology: Improving accuracy and uncertainty estimates using Bayesian Neural Networks
<p><strong>This data consists of 286,401 with broad-band g,r,i,z,y photometry from the HSC DR2 survey and spectroscopic redshifts. The majority of galaxies in our sample lies between redshift of 0.01 and 2.5</strong></p>
Model Output and Figure Scripts for: "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records"
<p>New climate model output and figure scripts for the paper "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records".</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.