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102 results for “Data Model comparison”
Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution models
1.Community-level models (CLMs) consider multiple, co-occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analyzing and predicting biodiversity patterns. CLMs simultaneously model multiple species, including rare species, while reducing overfitting and implicitly considering drivers of co-occurrence. Many CLMs are direct extensions of well-known SDMs and therefore should be familiar to ecologists. However, CLMs remain underutilized, and there have been few tests of their potential benefits and no systematic reviews of their assumptions and implementations. Here we review this emerging field and provide examples in R to fit common CLMs. Our goal is to introduce CLMs to a broader audience, and discuss their attributes, benefits, and limitations relative to SDMs. 2.We review i) statistical implementations and applications of CLMs, ii) their advantages and limitations, and iii) comparative analyses of CLMs and SDMs. We also suggest directions for future research. 3.We identify seven CLM algorithms with similar data structures and predictive outputs as SDMs that should be most accessible to ecologists familiar with species-level modeling, including five methods that predict assemblage composition and individual species distributions and two methods that model compositional turnover along environmental gradients. CLMs have been applied to numerous taxa, regions, and spatial scales, and a variety of topics (e.g., studying drivers of community structure or assessing relationships between community composition and functional traits). Studies suggest that the relative benefits of CLMs and SDMs may be case specific, especially in terms of predicting species distributions and community composition. However, CLMs may offer advantages in terms of computational efficiency, modeling rare species, and projecting to no-analog climates. A major shortcoming of CLMs is their reliance on presence-absence community composition data. 4.Studies are needed to assess the relative merits of SDMs and CLMs, and different CLM algorithms, with a focus on three key areas: i) under which circumstances CLMs improve predictions for rare species, ii) how CLMs perform under different community compositions (e.g. relative abundance of rare vs. common species), including the extent to which co-occurrence patterns are structured by biotic interactions, and iii) ability to project across time/space.
Model Data for Doubly Periodic SCREAM Comparison with ARM Observations
<p>Includes the SAM LES, E3SM SCM, and DP-SCREAM simulations used in the study.</p>
raw data - Comparison of High-Flow Nasal Cannula Oxygen and Conventional Oxygen Therapy in a Rat Model of Severe Carbon Monoxide Poisoning
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Models, scripts, simulated data, and results from the article "Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework."
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Data from: Comparison of non-Gaussian quantitative genetic models for migration and stabilizing selection
The balance between stabilizing selection and migration of maladapted individuals has formerly been modeled using a variety of quantitative genetic models of increasing complexity, including models based on a constant expressed genetic variance and models based on normality. The infinitesimal model can accommodate non-normality and a non-constant genetic variance as a result of linkage disequilibrium. It can be seen as a parsimonious one-parameter model which approximates the underlying genetic details well when a large number of loci are involved. Here, the performance of this model is compared to several more realistic explicit multilocus models, with either two, several or a large number of alleles per locus with unequal effect sizes. Predictions for the deviation of the population mean from the optimum are highly similar across the different models, so that the non-Gaussian infinitesimal model forms a good approximation. It does however generally estimate a higher genetic variance than the multilocus models, with the difference decreasing with an increasing number of loci. The difference between multilocus models depends more strongly on the effective number of loci, accounting for relative contributions of loci to the variance, than on the number of alleles per locus.
Data from: Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees
Stochastic simulation studies of animal breeding have mostly relied on either the infinitesimal genetic model or finite polygenic models. In this study, we investigated the long-term effects of the chosen model on honeybee breeding schemes. We implemented the infinitesimal model, as well as finite locus models, with 200 and 400 gene loci and simulated populations of 300 and 1000 colonies per year over the course of 100 years. The selection was of a directly and maternally influenced trait with maternal heritability of h²_m = 0.42, direct heritability of h² d = 0.27, and a negative correlation between the effects of r_md = −0.18. Another set of simulations was run with parameters h²_m = 0.53, h²_d = 0.34, and r_md = −0.53. All models showed similar behavior for the first 20 years. Throughout the study, we observed a higher genetic gain in the direct than in the maternal effects and a smaller gain with a stronger negative covariance. In thelong-term, however, only the infinitesimal model predicted sustainable linear genetic progress, while the finite locus models showed sublinear behavior and, after 100 years, only reached between 58% and 62% of the mean breeding values in the infinitesimal model. While the infinitesimal model suggested a reduction of genetic variance by 33% to 49% after 100 years, the finite locus models saw a more drastic loss of 76% to 92%. When designing sustainable breeding strategies, one should, therefore, not blindly trust the infinitesimal model as the predictions may be overly optimistic. Instead, the more conservative choice of the finite locus model should be favored.
Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Data for thesis: Online Discovery and Model-to-Model Comparison of DCR Models from Event Streams
<p>This is the collection of data referenced in the Thesis</p>
Data from: Comparison of seven simple loss models for runoff prediction at the plot, hillslope and catchment scale in the semiarid southwestern U.S.
<p>Infiltration excess overland flow is the dominant mechanism for runoff generation in many dryland watersheds. Event-based rainfall-runoff models therefore partition precipitation into two components: loss and excess precipitation. The latter is then transformed into a runoff hydrograph. Numerous loss models have been developed over the past century ranging from simple empirical to sophisticated physically based methods. Complex models can lead to equifinality and associated uncertainty at larger spatial scales with varying soil and cover conditions. Simple models are therefore widely used in hydrologic practice. In the absence of measured data in many arid and semiarid regions, model parameters are often estimated based on laboratory or field infiltrometer tests. Given the documented importance of spatial scale on the runoff response in dryland catchments, it is not clear how models parameterized at the point or soil column scale will perform at the hillslope or catchment scale under real-world conditions. In this study, we compared the performance of seven simple loss models with three or less parameters: the Philip, Smith-Parlange, Horton, Kostiakov, curve number (CN), initial and constant (IC) and the linear and constant (LC) models. The latter is a modification of the IC model introduced in this study. We estimated parameters at the plot scale (2.8 m<sup>2</sup><span><span><span><span><span><span><span><span><span>) using rainfall simulation and then tested model performance at the hillslope (1.5–3.7 ha) and catchment scale (2.4–2.8 km</span></span></span></span></span></span></span></span></span><sup>2</sup><span><span><span><span><span><span><span><span><span>) based on measured rainfall-runoff data at two sites in New Mexico and Arizona, U.S. Results show that rainfall simulation can be used successfully to parameterize loss models at the hillslope scale. At the catchment scale, most models showed positive bias, suggesting that other losses (such as channel or transmission losses) play an important role in determining the catchment runoff response. Rainfall intensity and temporal distribution were found to be crucial for accurate runoff prediction. Models that are sensitive to rainfall intensity during the entire simulation (Philip, Smith-Parlange, Horton, Kostiakov, LC) therefore performed better than those with an initial abstraction term (CN, IC). During intermittent rain, the best results were achieved by methods expressing infiltration capacity as a function of cumulative infiltration (LC, Smith-Parlange). </span></span></span></span></span></span></span></span></span></p>
Data from: Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees
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Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution models
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Data from: Quantification and comparison of anti-fibrotic therapies by polarized SRM and SHG-based morphometry in rat UUO model
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Data from: ModelOMatic: fast and automated comparison between RY, nucleotide, amino acid, and codon substitution models
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Data from: Comparison of non-Gaussian quantitative genetic models for migration and stabilizing selection
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Data from: Comparison of seven simple loss models for runoff prediction at the plot, hillslope and catchment scale in the semiarid southwestern U.S.
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Data and R-Code from: How to account for behavioral states in step-selection analysis: a model comparison
<p>This repository provides the R-code and data used for the simulation and case study of the research paper: "How to account for behavioral states in step-selection analysis: a model comparison".</p><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_Data</strong>" contains the landscape rasters used for data generation in the simulation study, and the bank vole (<i>Myodes glareolus</i>) movement data used in the case study on bank vole interactions:</p><ul><li>landscape10.RData and landscape50.RData: Landscape rasters for the simulation study.</li><li>Vole_case_control.rds: Case-control bank vole data for the case study.</li><li>Info_replicates.rds: Information about bank vole indiviuals and corresponding replicates for the case study.</li><li>Codebook_case_study.xlsx: Codebook for the case study data sets.</li><li>Read_me.txt</li></ul><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_RCode</strong>" contains the R-scripts for the simulation and case study:</p><ul><li>Functions.R: Functions to apply HMMs, TS-iSSAs, and HMM-iSSAs to movement data; used for the simulation and case study.</li><li>Simulation_study.R: R-Code to run the simulation study. Parallel computation is used.</li><li>Results_simulation_study.R: R-Code to create the result figures and tables for the simulation study.</li><li>Case_study.R: R-Code to run the bank vole interaction case study. Parallel computation is used.</li><li>Results_case_study.R: R-Code to create the result figures and tables for the case study.</li><li>Read_me.txt</li></ul><p>Besides the simulation and case study from the paper, the included functions (<i>Functions.R</i>) can generally be used to perform an HMM-iSSA analysis.</p><p>For the bank vole movement data without control locations, see: Schlägel, U.E. et al. (2019). Data from: Estimating interactions between individuals from concurrent animal movements [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rt535m8">https://doi.org/10.5061/dryad.rt535m8</a>.</p><p><strong>Acknowledgements</strong></p><p>We thank Sophie Eden, Angela Puschmann and Pauline Lange for help with the bank vole data collection and maintenance of the outdoor enclosures.</p><p> </p><p> </p>
JAMES technical report, scripts and data for "'Comparison of C3 photosynthetic responses to light and CO2 predicted by the leaf photosynthesis models of Farquhar et al. (1980) and Goudriaan et al. (1985)"
<p>Dear reader,</p> <p>In this repository you will find 7 MATLAB scripts and 3 Excel datasets. The script "A_curves_Figure2.m" calls the function scripts "FvCB_model_Figure2.m", "FvCB_model_Figure2_noTPU" and "G85_model_Figure2" to create Figure 2 of the JAMES publication. The script "G85_Rd_FigureS1" calls the function script "G85_model_FigureS1" to create Figure S1 of the Supporting Information. The script "r2_RMSE_Table2" calculates statistics displayed in Table 2 of the JAMES publication. The Excel worksheet "Table2.xlsx" contains the values in Table 2 of the JAMES publication for quick data copying. The Excel worksheets "FvCBparameters_fitted_withTPU.xlsx" and "FvCBparameters_fitted_noTPU.xlsx" contains fitted FvCB model parameter values that were obtained using the "fitaci" function from the plantecophys R package (Duursma, 2015). <br> <br> Kind regards,</p> <p>Kevin van Diepen </p>
Data from: Comparison of biometrical models for joint linkage association mapping
Joint linkage association mapping (JLAM) combines the advantages of linkage mapping and association mapping, and is a powerful tool to dissect the genetic architecture of complex traits. The main goal of this study was to use a cross-validation strategy, resample model averaging and empirical data analyses to compare seven different biometrical models for JLAM with regard to the correction for population structure and the quantitative trait loci (QTL) detection power. Three linear models and four linear mixed models with different approaches to control for population stratification were evaluated. Models A, B and C were linear models with either cofactors (Model-A), or cofactors and a population effect (Model-B), or a model in which the cofactors and the single-nucleotide polymorphism effect were modeled as nested within population (Model-C). The mixed models, D, E, F and G, included a random population effect (Model-D), or a random population effect with defined variance structure (Model-E), a kinship matrix defining the degree of relatedness among the genotypes (Model-F), or a kinship matrix and principal coordinates (Model-G). The tested models were conceptually different and were also found to differ in terms of power to detect QTL. Model-B with the cofactors and a population effect, effectively controlled population structure and possessed a high predictive power. The varying allele substitution effects in different populations suggest as a promising strategy for JLAM to use Model-B for the detection of QTL and then to estimate their effects by applying Model-C.
Data from: Comparison of biometrical models for joint linkage association mapping
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Exp 3 Model Data Comparison
<p>Model results for the base case of comparing with the MODEX flume data</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.