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Dataset results
124 results for “quantitative modeling”
Quantitative Assessment of the Impact of Future Land Use Changes on Flood Risk Using Remote Sensing, Machine Learning, and a Hydraulic Model
<p> </p> <p>The RF Machine learning code </p> <p>Topological, geomorphology, geology, metrological information of the Tajan watershed.</p> <p>Land use land cover images of the Tajan watershed</p> <p>River, transportation roads, villages map </p> <p>Global damage function datasets.</p>
Quantitative modeling of streamer discharge branching in air
<p>Streamer discharges are the primary mode of electric breakdown of air in lightning and high voltage technology. Streamer channels branch many times, which determines the developing tree-like discharge structure. We simulate branching of positive streamers in air using a 3D fluid model with stochastic photoionization. The distributions of branching angles and branching locations agree quantitatively with dedicated experiments. The simulated branching is remarkably sensitive to the photoionization coefficients, which confirms the validity of the classical photoionization model.</p>
Study on a quantitative method for determining mixing proportion of transparent cemented soil for visual geotechnical model tests
<p>An effective mixing of transparent cemented soil is necessary for visual geotechnical model tests, so a quantitative method for determining mixing proportion of transparent cemented soil was generated in this paper. Firstly, quartz sand, Nanoscale silica powder and N-dodecane mixed 15# white oil were selected as the raw materials, and a series of orthogonal experiments were designed. Concurrently, the main physical and mechanical parameters (volumetric weight γ, internal friction angle φ, cohesion c) of transparent cemented soil were measured, caused by the change of "particle size of quartz sand" and " mass ratios between fumed silica and fused quartz". Subsequently, multiple linear regression equations of various physical and mechanical parameters (γ, φ, c) were obtained by fitting the original test data. Finally, the rationality of multiple linear regression equations was proved. The research results indicated: (1) the volumetric weight changes from 16.13kN/m<sup>3</sup> to 12.53kN/m<sup>3</sup>, the Internal friction angle is between 27.07° and 14.82°, and the cohesion varies from 31kPa to 2.3kPa, the parameters meet the similar requirements of the surrounding rock (grade ⅳ and ⅴ) and clay; (2) The values of Multiple R values (all greater than 0.88) and the Significance F value (all close to 0) proves the three regression equations were valid; (3) Combining the three regression equations and particle size of quartz sand, the mass ratio between fumed silica and fused quartz and geometry similarity constant were solved. All the conclusions mentioned could provide theoretical support and data reference for transparent soil model test implementation.</p>
Quantitative modeling of streamer discharge branching in air
<p>Streamer discharges are the primary mode of electric breakdown of air in lightning and high voltage technology. Streamer channels branch many times, which determines the developing tree-like discharge structure. We simulate branching of positive streamers in air using a 3D fluid model with stochastic photoionization. The distributions of branching angles and branching locations agree quantitatively with dedicated experiments. The simulated branching is remarkably sensitive to the photoionization coefficients, which confirms the validity of the classical photoionization model.</p>
Data from: Quantitative genetic analysis of brain size variation in sticklebacks: support for the mosaic model of brain evolution
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Quantitative and qualitative methods complementing: Bridging modelling and policy-making efforts to realise the European bioeconomy
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Study on a quantitative method for determining mixing proportion of transparent cemented soil for visual geotechnical model tests
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Data from: Human judgment vs. quantitative models for the management of ecological resources
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Supplementary material 2 from: Desvignes V, Buschhardt T, Guillier L, Sanaa M (2019) Quantitative microbial risk assessment for Salmonella in eggs. Food Modelling Journal 1: e39643. https://doi.org/10.3897/fmj.1.39643
Parameter settings
Supplementary material 1 from: Desvignes V, Buschhardt T, Guillier L, Sanaa M (2019) Quantitative microbial risk assessment for Salmonella in eggs. Food Modelling Journal 1: e39643. https://doi.org/10.3897/fmj.1.39643
QMRA_Salmonella_egg_Virginie.fskx
Figure 2 from: Desvignes V, Buschhardt T, Guillier L, Sanaa M (2019) Quantitative microbial risk assessment for Salmonella in eggs. Food Modelling Journal 1: e39643. https://doi.org/10.3897/fmj.1.39643
Figure 2 Predicted number of salmonellosis per million servings of eggs, according to the cooking method.
Data from: Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models
Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. 'Animal models', which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations. However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non-randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology. We first illustrate how the occurrence of non-random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce 'genetic group' methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents. We summarize the conceptual foundations of genetic group animal models and provide extensive, step-by-step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods. Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow. We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets.
Data from: Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model
Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.
Data from: Evolution of female multiple mating: a quantitative model of the "sexually-selected sperm" hypothesis
Explaining the evolution and maintenance of polyandry remains a key challenge in evolutionary ecology. One appealing explanation is the sexually-selected sperm (SSS) hypothesis, which proposes that polyandry evolves due to indirect selection stemming from positive genetic covariance with male fertilization efficiency, and hence with a male's success in post-copulatory competition for paternity. However, the SSS hypothesis relies on verbal analogy with 'sexy-son' models explaining co-evolution of female preferences for male displays, and explicit models that validate the basic SSS principle are surprisingly lacking. We developed analogous genetically-explicit individual-based models describing the SSS and 'sexy-son' processes. We show that the analogy between the two is only partly valid, such that the genetic correlation arising between polyandry and fertilization efficiency is generally smaller than that arising between preference and display, resulting in less reliable co-evolution. Importantly, indirect selection was too weak to cause polyandry to evolve in the presence of negative direct selection. Negatively-biased mutations on fertilization efficiency did not generally rescue runaway evolution of polyandry unless realized fertilization was highly skewed towards a single male, and co-evolution was even weaker given random mating-order effects on fertilization. Our models suggest that the SSS process is, on its own, unlikely to generally explain the evolution of polyandry.
Data from: Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits
The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F2 (IF2) to the two parents, from random-cross of IF2 or from selfing of IF2 population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF2 population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.
Supplementary material 1 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
QRA simulator
Figure 6 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 6 The relative batch risk (with respect to a baseline risk value) is plotted as a function of the initial STEC (main pathogenic serotypes MPS-STEC) concentration (CFU/ml).
Figure 5 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 5 Batch rejection probability as a function of the initial STEC (main pathogenic serotypes MPS-STEC) concentration (CFU/ml).
Figure 4 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 4 Evolution of STEC colony size during draining, salting and ripening of cheese fabrication. The decline rate for the MPS O157:H7 strain and non-MPS strains are equal (orange line) and significantly higher than the decline rate of MPS non-O157:H7 strain (red line). The three phases, namely, draining, salting and ripening are separated by vertical blue dotted lines.
Figure 3 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 3 Evolution of STEC (main pathogenic serotypes MPS-STEC) in log10 CFU/ml during the storage and moulding step. The blue vertical line shows the end of the storage phase.
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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.