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648 results for “uncertainties”
Supplementary material 1 from: Yemshanov D, Koch F, Ducey M, Haack R, Siltanen M, Wilson K (2013) Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker's perspective. NeoBiota 18: 193-218. https://doi.org/10.3897/neobiota.18.4002
Risk of out-of-state (out-of-province) locations to be the source of forest pests transported in firewood carried by campers. The risk rank values are based on the delineation of nested non-dominant sets via the first-degree stochastic dominance rule (FSD). The ranks close to 1.0 denote the highest risk of pest arrival and the ranks close to 0 denote the lowest risk. (doi: 10.3897/neobiota.18.4002.app1) File format: Adobe PDF File (pdf).:
Supplementary material 4 from: Yemshanov D, Koch F, Ducey M, Haack R, Siltanen M, Wilson K (2013) Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker's perspective. NeoBiota 18: 193-218. https://doi.org/10.3897/neobiota.18.4002
Summary of differences between risk rank classes, 0–0.05, 0.05–0.25, 0.25–0.5, 0.5–0.75, 0.75–0.95 and 0.95–1 in the delineations based on the FSD and SSD rules. (doi: 10.3897/neobiota.18.4002.app4) File format: Adobe PDF File (pdf).:
Data and code for "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making" (CHI 2017)
<p>This repository contains data and analysis code for the following paper:</p> <p>Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, and Matthew Kay. "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making", Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI 2018. DOI: 10.1145/3173574.3173718<br> </p>
Robust quantum dots charge autotuning using neural network uncertainty - Output data
<p>Outputs of the model training and the offline autotuning experiments presented in the paper: "<em>Robust quantum dots charge autotuning using neural network uncertainty</em>".</p> <p>For convenience, the results are splitted in several zipped files:</p> <ul> <li><strong>run_outputs_light.zip</strong>: contains only settings and results text files (sufficient for compiling result tables).</li> <li><strong>run_outputs_full_scan.zip</strong>: contains complete scan of the diagrams (for qualitative analyse)</li> <li><strong>run_outputs_part<N>.zip</strong>: contains all autotuning simulation output, grouped by seed (images and video output types might vary between seeds)</li> </ul> <p>Each folder in the zipped files represent a run that includes:</p> <ul> <li>log file</li> <li>plots / images</li> <li>run settings</li> <li>performance results</li> <li>pytorch model parameters</li> </ul> <p>See README.txt for more information about the file strucutre.</p>
Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers
<p>Hydraulic and tracer data used in the Chillan case study presented in "Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers"</p>
Spatial patterns of uncertainty in climate exposure metrics for North America at 1km resolution
<p>The data provided below represents the degree of uncertainty or variation between 8 individual general circulation models (GCM) for three metrics commonly used to assess the intensity of exposure to climate change. The three exposure metrics (forward and backward <a href="https://adaptwest.databasin.org/pages/adaptwest-velocitywna">climatic velocity</a> and <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">local climatic dissimilarity</a>) were calculated based on the first two principal components (PC) scores derived from <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">11 different climate variables</a>. Frameworks and heuristics supporting climate adaptation for conservation often rely on projections of climate change or climate exposure. However, projections of climate change vary among alternative GCM outputs, different emissions scenarios, and different future time periods. The potential for these model predictions to vary geographically presents a source of uncertainty in assigning climate-informed conservation strategies to landscapes. Regions with high agreement among predictions could be more confidently assigned a climate-informed strategy, whereas regions with less agreement among predictions may require a more cautious approach. More information on the data can be found at https://adaptwest.databasin.org/pages/uncertainty-climate-metrics.</p>
Advice Taking under Uncertainty: The Impact of Genuine Advice versus Arbitrary Anchors on Judgment
<p>A major module of rational advice taking consists in the metacognitive ability to distinguish between credible advice and arbitrary anchors. Accordingly, we investigated the extent to which framing the very same information as either advice or anchor exerts a differential influence on quantitative judgments. Four experiments showed that although arbitrary anchors were given lower weight than advice, they nevertheless exerted a systematic impact on final judgments. Degree of integration was related to subjective confidence only in the advice condition, but not in the anchoring condition, suggesting that arbitrary anchors were not considered informative. Framing the source of advice as a human being versus as a computer did not affect our results. Only the aboutness of advice, that is, whether it targeted the focal judgment item, determined its influence on final judgments and on confidence. On the one hand, these findings speak to the (partial) sensitivity of human judges to the source and validity of advice under uncertainty. On the other hand, the persevering effect of arbitrary anchors demonstrates the dependence of judgments on unauthorized influences. Both findings together highlight the need to study advice taking from a metacognitive perspective.</p>
Supplemental material to the paper: Incorporating epistemic uncertainty in the assessment of an existing masonry building through a Point Estimate Method
<p>This repository contains the meterials used to study numerically the effect of model uncertainties in the seismic assessment of a case-study stone masonry building. The simulations were run in the research version of the software <a href="http://www.tremuri.com/">Tremuri</a>. The repository contains input files, model results and the matlab code to reproduce the figures presented in the paper:</p> <blockquote> <p>Vanin F., Beyer K., "Incorporating epistemic uncertainty in the assessment of an existing masonry building through a Point Estimate Method", submitted for publication (2019)</p> </blockquote>
The Effect of QPF on Real-time Deterministic Hydrologic Forecast Uncertainty
<p>The use of Quantitative Precipitation Forecast (QPF) in hydrologic forecasting is commonplace, but QPF is subject to considerable error. When QPF is included as a model forcing in the hydrological forecast process, significant error is passed to subsequent hydrologic predictions. Two questions arise: (1) are the resulting observed hydrologic forecast errors sufficiently large to suggest the use of zero QPF in the forecast process; if the use of QPF is indicated, (2) how many periods (hours) of QPF (1-, 6-, 12-,..., 72-h...) should be used? Also, do forecast conditions exist under which the use of QPF should be different? This study presents results from two real-time hydrologic forecast experiments, focused on the NOAA/NWS Ohio River Forecast Center (OHRFC). The experiments rely on forecasts from subbasins at 38 forecast point locations, ranging in drainage area, geographic location within the Ohio River Valley, and watershed response time. Results from an experiment, spanning all flow ranges, for the August 10, 2007 - August 31, 2009 period, show that non-zero QPF produces smaller hydrologic forecast error than zero QPF. A second experiment, January 23, 2009 through September 15, 2010, suggests that QPF should be limited to 6- to 12-h duration for flood forecasts. Beyond 12-h, hydrologic forecast error increases substantially across all forecast ranges, but errors are much larger for flood forecasts. Increased durations of QPF produce smaller forecast error than shorter QPF durations only for non-flood forecasts. Experimental results are shown to be consistent with NWS, April 2001 to October 2016, forecast verification statistics for the OHRFC.</p>
Model output used in the manuscript "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; <a href="https://doi.org/10.3402/tellusa.v53i5.12229" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the manuscript "<em>Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective</em>", published by the Bulletin of the American Meteorological Society (<a href="https://doi.org/10.1175/BAMS-D-24-0064.1" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2025</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a> and <a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al. (2024)</a>.</p> <p>All files uploaded were generated from simulations run by the lead author.</p> <p>For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the manuscript. If you have any questions, please feel free to contact me.</p>
Raw data for "A Composite Bayesian Optimisation Framework for Material and Structural Design under Uncertainty"
<p>This dataset contains the raw data for the paper "A Composite Bayesian Optimisation Framework for Material and Structural Design under Uncertainty" (submitted) by R. P. Cardoso Coelho, A. F. Carvalho Alves, T. M. Nogueira Pires and F. M. Andrade Pires (INEGI and Faculty of Engineering of the University of Porto, Portugal).</p> <p> </p> <p>The data has been generated with the development branch of piglot - an open-source optimisation toolbox (https://github.com/CM2S/piglot). The numerical simulations have been conducted with both an in-house finite element solver (Links) and with the open-source SCA implementation CRATE (https://github.com/bessagroup/CRATE).</p>
MANET: uncertainty in demographics – data on population projections
<p>This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future).</p> <p>More specifically, it contains:</p> <p>- in `other_pop_data` folder files from <a href="https://databank.worldbank.org/source/population-estimates-and-projections">World Bank,</a> the <a href="https://www.census.gov/data-tools/demo/idb/#/dashboard?COUNTRY_YEAR=2023&COUNTRY_YR_ANIM=2023">International Database</a> from the US Census, and from <a href="https://ghdx.healthdata.org/record/ihme-data/global-population-forecasts-2017-2100">IHME</a></p> <p>- in the `SSP` folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from <a href="https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10">IIASA</a> and as in the version 3.0 downloaded from <a href="https://data.ece.iiasa.ac.at/ssp/#/workspaces">IIASA workspace</a></p> <p>- in the `UN` folder, the demographic projections from <a href="https://population.un.org/wpp/Download/Standard/Population/">UN</a></p> <p>- `IAMstat.xlsx`, an overview file of the metadata accompanying the scenarios present in the IPCC databases</p> <p>- `RFF.csv`, an overview file containing the population projections obtained by <a href="../record/6016583#.Y42iFuzP2rP">Resources For the Future</a> </p> <p>'- the remaining `.csv` files with names `AR6#`, `AR5#`, `IAMC15#` contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from <a href="https://tntcat.iiasa.ac.at/AR5DB">AR5</a>, <a href="https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/downloads">SR 1.5</a>, and <a href="https://data.ene.iiasa.ac.at/ar6/#/workspaces">AR6</a></p> <p>This data in intended to be downloaded for use together with the package downloadable <a href="https://github.com/sgiarols/Climate_Scenario_Data_Science">here</a>.</p> <p>The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).</p>
FIGURE 11 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 11 | Bayesian phylogenetic tree of Cambeva species obtained from COI data. The vertical red line shows the coalescent branching process estimated by using the single-threshold model in the GMYC combination 4 (strict clock model vs. a coalescent constant population
FIGURE 7 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 7 | Coloration pattern of Cambeva species. (A, B). Cambeva guaraquessaba, UFRGS 24554, 37.0 and 36.9 mm SL, respectively, tributary of Guaraqueçaba River basin, Paranaguá Bay. C–D. Cambeva tupinamba, UFRGS 24550, 63.0 mm SL and 39.4 mm SL, Betari River, Ribeira de Iguape River basin.
FIGURE 10 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 10 | Variation on the coloration of Cambeva sp. 2 from (A–D) Tubarão River, UFRGS 24553, 66.1, 45.4, and 56.04, 52.1 mm SL, and (E, F) Araranguá River, UFRGS 22964, 61.1 and 60.7 mm SL, respectively, showing a unique color pattern that differs from other congeners in the study area: one layer of coloration composed of not coalescent round small blotches.
FIGURE 8 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 8 | Coloration pattern of Cambeva zonata, (A–C) UFRGS 24538, (A) 51.1 mm SL, (B) 48.4 mm SL, Betari River, Ribeira de Iguape River basin, and (C) 41.4 mm SL.
FIGURE 9 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 9 | ndividuals representing populations of Cambeva sp. 1 from distinct river drainages. (A) Maquiné River basin, UFRGS 22211, 58.6 mm SL, (B) Mampituba River basin, MCP 23623, 55.7 mm SL; and (C, D) Araranguá River basin, UFRGS 22962, 45.2 and 59.8 mm SL, respectively.
FIGURE 6 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 6 | Variation on the coloration in Cambeva cubataonis, showing a mottled color pattern composed by blotches variable in shape and forming marked saddles dorsally, (C–E) specimens with large dark areas merged giving an overall dark color pattern. Specimens from tributaries of Guaratuba Bay (A) UFRGS 24549, 53.1 mm SL; (B) UFRGS 24556, 60.3 mm SL; and from tributaries to Babitonga Bay (C–E) UFRGS 24552, 77.2 mm SL, 85.7 mm SL, and 61.6 mm SL, respectively.
FIGURE 5 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 5 | Variation on the coloration in vivo and ontogenetic series of Cambeva botuvera from (A, C–F) Itajaí-Açu River, UFRGS 23182, 47.3 mm SL, 64.8 mm SL, 68.1 mm SL, 81.1 mm SL, 95.6 mm SL, respectively; and (B) from Itajaí-Mirim River, UFRGS 24558, 67.1 mm SL, showing the color pattern with two layers in the skin composed of large and small round black blotches in the inner and outer layer, respectively.
FIGURE 4 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 4 | Polymorphic color pattern within of Cambeva barbosae ranging from a mottled color pattern in individuals from (A) Santa Catarina Island, UFRGS 23183, 80.1 mm SL, and (B) Biguaçu River basin, UFRGS 22907, 61.3 mm SL; to a blotched color pattern with dark marks varying in shape and size from Biguaçu River basin, (C, D) UFRGS 22932, 58.7mm SL and 51.0 mm SL, respectively, and (E) UFRGS 20937, 30.0 mm SL.
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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.