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93 results for “joint model”
Joint Modelling of Astrophysical Systematics (JMAS) data
<p>This dataset is part of the Joint Modelling of Astrophysical Systematics (JMAS) project and includes redshift distributions (NZ), intrinsic alignment amplitudes (IA), parametric sweeps for NZ and IA, data vectors and Fisher matrices.</p> <p>The data is intended to support the findings published in [add]. The code to recreate the plots can be found on <a href="https://github.com/nikosarcevic/JMAS" target="_blank" rel="noopener">JMAS GH Repository</a>.</p>
Code and data for Bayesian joint species distribution model selection for community-level prediction
<p>Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.</p>
Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling
<p>A necessary component of understanding vector-borne disease risk is the accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data-rich species but may produce inaccurate results for sparsely-documented vectors. In light of global change, vectors that are currently not well-documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a<strong> </strong>sparsely-documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely-documented species. To demonstrate this approach, we evaluated the ability of twelve models—which differed in their pooling of data from multiple vector species and inclusion of disease data—to improve distribution estimates of sparsely-documented vectors. We assessed our models on two simulated data sets, which allowed us to generalize our results and examine their mechanisms. We found that when the focal species is sparsely documented, incorporating data on related vector species reduces uncertainty and improves accuracy by reducing overfitting. When data on vector species are already incorporated, disease data only marginally improve model performance. However, when data on other vectors are not available, disease data can improve model accuracy and reduce overfitting and uncertainty. We then assessed the approach on empirical data on ticks and tick-borne diseases in Florida and found that incorporating data on other vector species improved model performance. This study illustrates the value of exploiting correlated data via joint modeling to improve distribution models of data-limited species.</p>
Data from: A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
<p>1. The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts.</p> <p>2. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference.</p> <p>3. In this study, we used Bayesian hierarchical models and a 43-year capture-mark-recovery dataset from 404,241 female mallards (Anas platyrhynchos) released in the North American midcontinent to estimate mallard demographic parameters. Further, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters.</p> <p>4. We demonstrate that density-dependence, habitat conditions, and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models.</p> <p>5. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.</p>
Fig. 2 in Parasite species co-occurrence patterns on Peromyscus: Joint species distribution modelling
Fig. 2. Results of variance partitioning for variation in ectoparasite prevalence explained by fixed and random effects for each ectoparasite species (columns). Explained variance presented for the constrained model for deer mice (n = 229 individuals). DM, deer mice; RBV, southern red-backed vole; WJM, woodland jumping mouse; PA, population abundance. Population abundance of small mammal species measured as captures per 100 trap nights. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Martian crustal models at the InSight landing site from joint inversion of ellipticity, P-to-s RFs and autocorrelations times
<p>- Preferred models resulting from the joint inversion of three local-scale measurements: Rayleigh-wave ellipticity, P-to-s Receiver functions (Joshi et al., 2023) and P-wave lag times from autocorrelation (Schimmel et al., 2021).</p> <p>- Scripts and observables to perform joint inversion.</p>
Data from: Frugivore traits predict plant-frugivore interactions using generalized joint attribute modeling
Open the record for dataset details and reuse information.
Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling
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Data from: A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
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Code and data for Bayesian joint species distribution model selection for community-level prediction
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Data Tables from contribution titled "Joint geophysical-petrological modeling on the Ivrea geophysical body beneath Valsesia, Italy: Constraints on the continental lower crust" published in Geochemistry, Geophysics, Geosystems
<p><strong>TABLE CAPTIONS</strong></p> <p><strong>Table 1: </strong>Average and specific bulk compositions of the representative exposed primitive rocks of the deep lower crust and upper mantle of the IVZ (Balmuccia and Lower Mafic Complex in Valsesia, Finero in Val Cannobina, and Sessera river in Val Sessera) used for the Perple_X calculations using the two thermodynamic models STX11 and HP02. Mn, Ni, P, and Cr were not used for the Perple_X calculations. Mg# is calculated as Mg / (Mg + Fe) (mole fractions). Symbols in the last row indicate the rock compositions that are reported (✓) and not reported (✗) in Figure 5. Average compositions are normalized to 100 wt.% on a volatile-free basis; specific compositions report original totals and H<sub>2</sub>O contents estimated based on the volumetric proportions hydrous minerals (amphibole and phlogopite) at the scale of the bulk rock. References: 1) Voshage et al. (1990); 2) Voshage et al. (1990), Shervais and Mukasa (1991), Hartmann and Wedepohl (1993), Rivalenti et al. (1995), and Mukasa and Shervais (1999); 3) Pin and Sills (1986); 4) Pin and Sills (1986), Shervais and Mukasa (1991), Hartmann and Wedepohl (1993), Rivalenti et al. (1995), and Mukasa and Shervais (1999); 5) Shervais and Mukasa (1991) and Mazzucchelli et al. (2009); 6) Sinigoi et al. (1991; 2011); 7) Hartmann and Wedepohl (1993); 8) Coltorti and Siena (1984); 9) Siena and Coltorti (1989), Lu et al. (1997a; b), and Stähle et al. (2001).</p> <p> </p> <p><strong>Table 2: </strong>Summary of <em>V<sub>P</sub></em> and <em>r</em> from the thermodynamic calculations based on representative rock compositions listed in Table 1. Mineral abbreviations: Pl = plagioclase, Spl = spinel, Grt = garnet, Ky = kyanite, Px = pyroxene, Cr-Di = Cr-diopisde, Am = amphibole, Phl = phlogopite.</p>
Data from: Effectiveness of joint species distribution models in the presence of imperfect detection
<p>Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise.</p> <p>A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of "collapsed data". A case study of owls and gliders in Victoria Australia is also illustrated.</p> <p>Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to disentangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications.</p> <p>To avoid biased estimates of inter-species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter-dependencies and occupancy.</p>
Data and scripts for "Joint species-trait distribution modelling: The role of intraspecific trait variation in community assembly"
<p>The README explains how to reproduce the analyses presented in the paper <strong>"Joint species-trait distribution modelling: The role of intraspecific trait variation in community assembly"</strong> by Abrego et al.</p> <p>The input data for the script pipeline is the file “Kilpisjarvi_plant_data.csv”. This file includes the data on the plants and their traits in the long format. Hence, each row of the data matrix corresponds to measurements on one plant species in one study plot. The joint species-trait distribution modelling (JSTDM) pipeline that analyses these data consists of the following R-scripts.</p> <p>· <strong>S1_define_JSTDM_models.R</strong>. This script defines the JSDTM models (null model and environmental model) that include five response types for each species: the presence-absence, abundance conditional on presence, and the plot-level trait values of specific leaf area (SLA), leaf area (LA) and mean height (MH). The model is defined in the Hierarchical Modelling of Species Communities (HMSC) framework utilizing the R-package Hmsc. The models are saved in the file “unfitted_models.RData”.</p> <p>· <strong>S2_fit_models.R. </strong>This script loads the unfitted models and fits them using the posterior sampling methods implemented in the R-package Hmsc. The models are fitted with increasing thinning until thin=100, which value was used to generate the results of the paper. The fitted models are saved in the file "models_thin_100_samples_250_chains_4.Rdata".</p> <p>· <strong>S3_plot_Omega_matrices.R. </strong>This script loads the fitted models and plots the association matrices (Fig. 2 of the paper). The csv file containing the values used to construct Fig. 2 is also given (figure2Cdata.csv and figure2Ddata.csv).</p> <p>· <strong>S4_show_VP_Beta_Gamma.R. </strong>This script loads the fitted models and extracts information on the variance partitionings (VP; Figs. S3 and S4 of the paper), the relationships between response types and environmental predictors (beta; Fig. S2 of the paper), and the relationships between response types and species-level traits (gamma; Fig. S5 of the paper). The csv file containing the values used to construct Fig. S2 (figureS2Adata.csv and figureS2Bdata.csv), Fig. S3 (figureS3data.csv), Fig. S4 (figureS4data.csv) and Fig S5 (figureS5data.csv) are also given.</p> <p>· <strong>S5_conditional_cross_validation.R</strong>. This script performs 10-fold cross validation to the data to test the predictive power related to the modelled plant traits. The script performs both regular (unconditional) cross-validation where all data are masked for the test fold, and conditional cross-validation where only the trait data (but not the abundance data) are masked for the test fold.</p> <p>· <strong>S6_show_conditional_cross_validation_results.R. </strong>This script plots the results of cross-validation (Fig. 3 of the paper). The csv file containing the values used to construct Fig. 3 is also given (figure3Adata.csv and figure3Bdata.csv).</p> <p>· <strong>S7_scenario_predictions.R</strong>. This script performs the scenario simulations described and shown in Fig. 4 of the paper. The csv file containing the values used to construct Fig. 4 is also given (figure4Bdata.csv and figure4Cdata.csv).</p>
Induced polarization and hydraulic tomography joint inversion results on a synthetic model
<p>These data are supplementary material for the following publication: Lukas Römhild, Gianluca Fiandaca, Peter Bayer (2024): Joint inversion of induced polarization and hydraulic tomography data for hydraulic conductivity imaging, <em>Geophysical Journal International</em>, Volume 238, Issue 2, August 2024, Pages 960–973, <a href="https://doi.org/10.1093/gji/ggae197">https://doi.org/10.1093/gji/ggae197</a></p> <p>The data set presents induced polarization (IP) and hydraulic tomography (HT) inversion results for a simple synthetic model. In addition, a joint inversion approach for both data types was implemented, so that individual and joint inversion results can be compared. We also show how the joint inversion procedure has the potential to correct a petrophysical bias within the IP inversion by incorporating the hydraulic data. The effect of different HT setups is also assessed by implementing different source and receiver positions.</p> <p>The file directory contains the original synthetic model, a subfolder for the synthetic data of IP and HT, and a subfolder for the individual HT and IP, as well as the joint inversion results. The HT results contain three different setups (1m, 2m-inside, 2m-outside). The IP results are shown for five different assumptions of petrophysical bias (-2, -1, 0, +1, +2). The joint inversion results contain all possible combinations of these test cases. For more information on the methodology, we refer to the paper (see above, currently under review).</p>
CMB / Lowermost Mantle Joint Tomographic Model
<p>Output model for Muir, Tanaka and Tkalčić</p> <p>Description of included data files: </p> <p>corrmat.dat - correlation matrix between slowness & radius perturbation coefficients; order of coefficients is (0,0), (1,-1), (1,0)....(8,8) for slowness, and then the same for radius<br> drpowers.dat - power per degree l for radius perturbation, column 1 = l, column 2 = mean, column 3 = 5%ile, column 4 = 95%ile<br> vppowers.dat - power per degree l for Vp perturbation, column 1 = l, column 2 = mean, column 3 = 5%ile, column 4 = 95%ile (using 13.61 km/s reference velocity)<br> m_dr.dat - summary statistics for radius perturbation coefficients, column 1 = l, column 2 = m, column 3 = mean, column 4 = 5%ile, column 5 = 95%ile<br> m_ds.dat - summary statistics for slowness perturbation coefficients, column 1 = l, column 2 = m, column 3 = mean, column 4 = 5%ile, column 5 = 95%ile<br> spatialcorr.dat - spatial correlation between slowness and radius, column 1 = latitude, column 2 = longitude, column 3 = correlation<br> tomodata.dat - summary statistics for Vp, column 1 = latitude, column 2 = longitude, column 3 = mean, column 4 = std dev<br> topodata.dat - summary statistics for radius, column 1 = latitude, column 2 = longitude, column 3 = mean, column 4 = std dev</p> <p>Spherical harmonics are given by <br> Y^m_l(phi, theta) = sqrt(2)*sqrt(((2l+1)(l-m)!)/(4pi(l+m)!)) cos(m phi) P^m_l(cos(theta)); m>0<br> Y^m_l(phi, theta) = sqrt(2)*sqrt(((2l+1)(l-m)!)/(4pi(l+m)!)) sin(-m phi) P^(-m)_l(cos(theta)); m<0<br> Y^m_l(phi, theta) = sqrt(((2l+1)(l-m)!)/(4pi(l+m)!)) P^(-m)_l(cos(theta)); m=0</p> <p>where P^m_l is the associated legendre function including the Condon-Shortley phase</p>
Data and models in Support of "Joint and Constrained Inversion as Hypothesis Testing Tools"
<p>The model and data files as well as the plotting and run scripts to reproduce the examples in "Joint and Constrained Inversion as Hypothesis Testing Tools".</p>
Joint acoustical-electrical modeling for tight sandstones verified by measurements
<p>These data are the compressional-wave waveform and electrical data obtained by Ba et al. through ultrasonic experimental and conductivity measurements on tight sandstones at different confining pressures and fluid saturations.</p> <p>Details are given in the uploaded introduction document regarding the format of dataset.</p>
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
<p>We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.</p>
Supplementary Material for "Joint Inversion Based on Variation of Information - A Crustal Model of Wilkes Land, East Antarctica"
<p>Inversion_Results_Cluster.nc - contains geographical and vertical information of clusters</p> <p>Inversion_Results_RealData_Run1.nc - contains inverted susceptibilities and densities after the first inversion run with high coupling</p> <p>Inversion_Results_RealData_Run2.nc - contains inverted susceptibilities and densities after the second continuous inversion run with low coupling</p> <p>Plot.ipynb - python script for visualization</p>
Psoriatic arthritis disease subtypes are phenocopied in a novel humanized murine model of psoriasis and joint inflammation
<p><em><span>Data processing and analysis</span></em></p> <p><span>Data normalization, quality control, and analysis was conducted by the UPMC Hillman Cancer Bioinformatics Services. Following the GeoMX NGS pipeline (<span>sequencing saturation 92%±0.12% (mean±S.E.M)), t</span>he resulting read count matrix was passed through robust QC procedure to remove ROIs of low surface area (<5000 µm<sup>2</sup>) or low perfect aligned reads (<80%), and probes of low quality or identified as global or local outliers (fails Grubbs outlier test in ≥ 20% segments). The remaining data were back-ground subtracted and Q3 normalized to house-keeping genes. ROIs were considered positive for CD8 T cells if their CD8 memory T cell deconvolution fraction was > 0 (R, spatialDecon, CellProfileLibrary; Human Adult ImmuneTumor_safeTME profile). Genes differentially expressed between groups of interest were detected using gene-wise linear modeling in R (edgeR, limma v3.50.3), with p-value adjusted for false discovery rate. Contrasts of groups were performed using t-tests at a significance level of 0.05.</span></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.