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648 results for “uncertainties”
Dataset for publication "Uncertainty Evaluation on the Absolute Phase Error of Digitizers"
<p>This is the dataset related to the paper</p> <p>Delle Femine, Antonio, Gallo, Daniele, Landi, Carmine, & Luiso, Mario. (2019). Uncertainty Evaluation on the Absolute Phase Error of Digitizers. Transactions of the Institute of Measurement and Control. http://doi.org/10.5281/zenodo.3600435</p> <p>that is identical to</p> <p>A. Delle Femine, D. Gallo, C. Landi, M. Luiso, “Uncertainty evaluation on the absolute phase error of digitizers”, Transactions of the Institute of Measurement and Control, Volume: 42 issue: 4, page(s): 749-758, 01 February 2020, doi: 10.1177/0142331219879836</p> <p> </p> <p> </p> <p>Excel file contains the data for Figures 6, 7, 8, 9.</p>
Data from: Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors
Calibration is the rate-determining step in every molecular clock analysis and, hence, considerable effort has been expended in the development of approaches to distinguish good from bad calibrations. These can be categorized into a priori evaluation of the intrinsic fossil evidence, and a posteriori evaluation of congruence through cross-validation. We contrasted these competing approaches and explored the impact of different interpretations of the fossil evidence upon Bayesian divergence time estimation. The results demonstrate that a posteriori approaches can lead to the selection of erroneous calibrations. Bayesian posterior estimates are also shown to be extremely sensitive to the probabilistic interpretation of temporal constraints. Furthermore, the effective time priors implemented within an analysis differ for individual calibrations when employed alone and in differing combination with others. This compromises the implicit assumption of all calibration consistency methods, that the impact of an individual calibration is the same when used alone or in unison with others. Thus, the most effective means of establishing the quality of fossil-based calibrations is through a priori evaluation of the intrinsic palaeontological, stratigraphic, geochronological and phylogenetic data. However, effort expended in establishing calibrations will not be rewarded unless they are implemented faithfully in divergence time analyses.
Data from: Not Normal: the uncertainties of scientific measurements
Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student's t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply.
Data from: Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media
In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen–Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.
Data from: Accounting for uncertainty in the evolutionary timescale of green plants through clock-partitioning and fossil calibration strategies
Establishing an accurate evolutionary timescale for green plants (Viridiplantae) is essential to understanding their interaction and coevolution with the Earth's climate and the many organisms that rely on green plants. Despite being the focus of numerous studies, the timing of the origin of green plants and the divergence of major clades within this group remain highly controversial. Here, we infer the evolutionary timescale of green plants by analysing 81 protein-coding genes from 99 chloroplast genomes, using a core set of 21 fossil calibrations. We test the sensitivity of our divergence-time estimates to various components of Bayesian molecular dating, including the tree topology, clock models, clock-partitioning schemes, rate priors, and fossil calibrations. We find that the choice of clock model affects date estimation and that the independent-rates model provides a better fit to the data than the autocorrelated-rates model. Varying the rate prior and tree topology had little impact on age estimates, with far greater differences observed among calibration choices and clock-partitioning schemes. Our analyses yield date estimates ranging from the Paleoproterozoic to Mesoproterozoic for crown-group green plants, and from the Ediacaran to Middle Ordovician for crown-group land plants. We present divergence-time estimates of the major groups of green plants that take into account various sources of uncertainty. Our proposed timeline lays the foundation for further investigations into how green plants shaped the global climate and ecosystems, and how embryophytes became dominant in terrestrial environments.
Data from: Recommendations for using msBayes to incorporate uncertainty in selecting an ABC model prior: a response to Oaks et al.
Prior specification is an essential component of parameter estimation and model comparison in Approximate Bayesian computation (ABC). Oaks et al. present a simulation-based power analysis of msBayes and conclude that msBayes has low power to detect genuinely random divergence times across taxa, and suggest the cause is Lindley's paradox. Although the predictions are similar, we show that their findings are more fundamentally explained by insufficient prior sampling that arises with poorly chosen wide priors that critically undersample nonsimultaneous divergence histories of high likelihood. In a reanalysis of their data on Philippine Island vertebrates, we show how this problem can be circumvented by expanding upon a previously developed procedure that accommodates uncertainty in prior selection using Bayesian model averaging. When these procedures are used, msBayes supports recent divergences without support for synchronous divergence in the Oaks et al. data and we further present a simulation analysis that demonstrates that msBayes can have high power to detect asynchronous divergence under narrower priors for divergence time. Our findings highlight the need for exploration of plausible parameter space and prior sampling efficiency for ABC samplers in high dimensions. We discus potential improvements to msBayes and conclude that when used appropriately with model averaging, msBayes remains an effective and powerful tool.
Data from: A revised design for microarray experiments to account for experimental noise and uncertainty of probe response
Background: Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Results: Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. Conclusion: The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.
Estimating uncertainty in divergence times among three-spined stickleback clades using the multispecies coalescent
<p>Incomplete lineage sorting (ILS) can lead to biased divergence time estimates. To explore if and how ILS has influenced the results of a recent study of worldwide phylogeny of three-spined sticklebacks (Gasterosteus aculeatus), we estimated divergence times among major clades by applying both a concatenation approach and the multispecies coalescent (MSC) model to single-nucleotide polymorphisms. To further test the influence of different calibration strategies, we applied different calibrations to the root and to younger nodes in addition to the ones used in the original study. Both the updated calibrations and the application of the MSC model influenced divergence time estimates, sometimes significantly. The new divergence time estimates were more ancient than in the previous study for older nodes, whereas the estimates of younger nodes were not strongly affected by the re-analyses. However, given the applied MSC method employs a simple substitution model and cannot account for changes in population size, we suggest that different analytical approaches and calibration strategies should be used in order to explore uncertainty in divergence time estimates. This study provides a valuable reference timeline for the ages of worldwide three-spined stickleback populations and emphasizes the need to embrace, rather than obscure, uncertainties around divergence time estimates.</p>
Data from: A Darwinian uncertainty principle
Reconstructing ancestral characters and traits along a phylogenetic tree is central to evolutionary biology. It is the key to understanding morphology changes among species, inferring ancestral biochemical properties of life, or recovering migration routes in phylogeography. The goal is twofold: to reconstruct the character state at the tree root (e.g. the region of origin of some species), and to understand the process of state changes along the tree (e.g. species flow between countries). We deal here with discrete characters, which are `unique', as opposed to sequence characters (nucleotides or amino-acids), where we assume the same model for all the characters (or for large classes of characters with site-dependent models) and thus benefit from multiple information sources. In this framework, we use mathematics and simulations to demonstrate that although each goal can be achieved with high accuracy individually, it is generally impossible to accurately estimate both the root state and the rates of state changes along the tree branches, from the observed data at the tips of the tree. This is because the global rates of state changes along the branches that are optimal for the two estimation tasks have opposite trends, leading to a fundamental trade-off in accuracy. This inherent `Darwinian uncertainty principle' concerning the simultaneous estimation of `patterns' and `processes' governs ancestral reconstructions in biology. For certain tree shapes (typically speciation trees) the uncertainty of simultaneous estimation is reduced when more tips are present, however, for other tree shapes it does not (e.g. coalescent trees used in population genetics).
Distributions of Uncertainty by Climate Impact
<p>These summary tables describe the distributions of results produced by allowing only one of statistical, model, and weather uncertainty to vary, to determine the contributions of each.</p> <p>These files are extracted from the general ACP Monte Carlo results using scripts from https://github.com/ClimateImpactLab/acp-impacts/tree/master/extract as defined below:</p> <p>The contents of variation-split are produced by uncertain.py. These results are organized into files with the filename template <VARIATION>-<IMPACT>--<RCP>-<YEAR>.csv. VARIATION may be "impact", for the variation driven by statistical uncertainty; "model", for the uncertainty driven by GCM choice; "weather", for the variation driven by weather realization; or "total", allowing all of these to vary simultaneously. IMPACT may be any of the estimated impacts, RCP can be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5, and YEAR is 2020, for an average from 2020 - 2039; 2040 for an average of 2040 - 2059; or 2080 for an average of 2080 - 2099.</p> <p>The contents of baseline-testing are produced by uncertain-test.py. These results are organized into files with the filename template <IMPACT>-<VARIATION>-<GCM>-<RCP>-<YEAR>.csv. The filename parts are as described above, with the addition of the name of the GCM that is held fixed when the GCM is not allowed to change.</p> <p>variation-split.tsv is produced by calcvar.py and is a summary of the results in variation-split/.</p> <p>baseline-testing.tsv is produced by calcvar-test.py and is a summary of the results in baseline-testing/.</p> <p><em>This data is provided for non-commercial research and educational purposes.</em></p>
Effects of Parametric Uncertainty on ITCZ Precipitation: Understanding the Role of Interactions between Parameters in Reducing the Double ITCZ Bias
<p>Data used to draw figures in the manuscript.</p>
Fig. 4 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 4 View of the tupe localitu of Neusticomys vossi sp. nov
Robust Active Measuring under Model Uncertainty - Code
<p>Repository containing code, as well as gathered data, as used for the paper</p> <blockquote> <p>Merlijn Krale, Thiago D. Simao, Jana Tumova, Nils Jansen<br>Robust Active Measuring under Model Uncertainty<br>In AAAI, 2024.</p> </blockquote> <p>For instructions, see the readme.md file in the repository.</p> <p>All code can also be found on GitHub, at <a href="https://github.com/LAVA-LAB/RATM">https://github.com/LAVA-LAB/RATM</a>.</p>
Figure 3 in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 3. The truncated tail tip of death adder specimen D4349 (left) compared with the full tail tip of specimen D3579 (right).
Diagnosing uncertainties in global biomass burning emission inventories and their impact on modeled air pollutants
<p>the data and codes I used to plot figures </p>
Uncertainty-aware genomic deep learning with knowledge distillation
Open the record for dataset details and reuse information.
Uncertainty in health impact assessments of smoke from a wildfire event
<p>Manuscript abstract: Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM<sub>2.5</sub>) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire-related smoke PM<sub>2.5 </sub>fields and variability in concentration-response parameters from epidemiologic studies of ambient and smoke PM<sub>2.5</sub>. This analysis focused on the 2016 Southeastern United States wildfires suggests that emissions from these wildfires had public health consequences in North Carolina. Using multiple methods based on publicly available monitor data and atmospheric models to represent wildfire-attributable PM<sub>2.5</sub>, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration-response parameters derived from studies of ambient and wildfire-specific PM<sub>2.5</sub> are used to assess health-related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health-related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire-related decision-making.</p>
Dataset for publication "Uncertainty assessment for very high temperature thermal diffusivity measurements on molybdenum, tungsten and isotropic graphite"
<p>Experimental data presented in the paper:</p> <p>Hay B., Beaumont O., Failleau G., Fleurence N., Grelard M., Razouk R., Davée G., Hameury J., Uncertainty assessment for very high temperature thermal diffusivity measurements on molybdenum, tungsten and isotropic graphite, <em>International Journal of Thermophysics</em> 43:2 (2022). https://doi.org/10.1007/s10765-021-02926-6.</p> <p>Excel file contains the data for Figures 3 to 5.</p>
Serotonin neurons modulate learning rate through uncertainty
<p>Regulating how fast to learn is critical for flexible behavior. Learning about the consequences of actions should be slow in stable environments, but accelerate when that environment changes. Recognizing stability and detecting change is difficult in environments with noisy relationships between actions and outcomes. Under these conditions, theories propose that uncertainty can be used to modulate learning rates ("meta-learning"). We show that mice behaving in a dynamic foraging task exhibit choice behavior that varied as a function of two forms of uncertainty estimated from a meta-learning model. The activity of dorsal raphe serotonin neurons tracked both types of uncertainty in the foraging task, as well as in a dynamic Pavlovian task. Reversible inhibition of serotonin neurons in the foraging task reproduced changes in learning predicted by a simulated lesion of meta-learning in the model. We thus provide a quantitative link between serotonin neuron activity, learning, and decision making.</p>
Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes
<p><strong>Data Availability Statement:</strong> The meteorological data ((S1 Data File) and hydrological data (S2 Data File) in the manuscript can be downloaded and publicly available to anyone.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.