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137
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Dataset results
137 results for “Mixed Model”
Mixed model-based deconvolution of cell-state abundances along a one-dimensional trajectory [csd-eQTL]
<p><strong>README:</strong></p> <p>The full summary data of the cell-state-dependent eQTLs for GTEx Esophagus Mucosa (n=497) are stored in the .parquet format.</p> <p>An example of the file name:</p> <p><strong>"GTEx_Esophagus_Mucosa_bin1.cis_qtl_pairs.1.parquet.gz"</strong> means the summary data of csd-eQTLs for bin1 of chromosome 1.</p>
Numerical experimental data about mixing time step of tracer-aided model
<p>This is the numerical experimental data about mixing time step of tracer-aided model related to the response letter of manuscript HYDROL49160 in Journal of Hydrology</p>
The Effectiveness of the Health Promoting School Intervention Model in Reducing Overweight and Obesity Among School Children in Indonesia: a Mixed-Methods Protocol Study
ClinicalTrials.gov study NCT06601348. IPD Sharing: NO. Countries: 1. Publications: 6.
Investigation of the Effectiveness of a Biopsychosocial-Based Exercise Model in Rheumatic Diseases: A Mixed Methods Research With Patients' Perspectives
ClinicalTrials.gov study NCT05344131. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Model for Safety Culture by Empowering the Family at Home: a Mixed-methods Study Protocol
ClinicalTrials.gov study NCT05487482. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Telerehabilitation, Face-to-Face Exercises, and a Mixed Model in Chronic Non-Specific Neck Pain
ClinicalTrials.gov study NCT07302958. IPD Sharing: NO. Countries: 1. Publications: 4.
Data from: A new analytical approach to landscape genetic modeling: least-cost transect analysis and linear mixed models
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Data from: Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components
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Data from: A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies
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Data from: Linkage into care among newly diagnosed HIV-positive individuals tested through outreach and facility-based HIV testing models in Mbeya, Tanzania: a prospective mixed-method cohort study
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Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
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Data from: Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors
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Bayesian stable isotope mixing models effectively characterize the diet of an Arctic raptor
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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: Factors contributing to the accumulation of reproductive isolation: A mixed model approach
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Data from: Richards’s equation and nonlinear mixed models applied to avian growth: why use them?
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Datasets: New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys
<p>These data set contains data from 11 surveys of radio-collared using remotely piloted aircraft systems (RPAS), thermal <br> imaging and automated detection.The MODELDEV .csv contains data points used to develop the modified Horvitz Thompson estimator models in the paper "New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys" which is accepted into publication in the journal Ecology and Evolution as of 11/06/2020. The TESTING .csv contains data points used for model testing in in the same publication. </p>
Raw data and results for the paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>*Data* (comets_condBoot_data.zip)</p> <p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p> <p>*Results:* (comets_scenarioXXX_results.zip, XXX=1,.. 6)</p> <p>6 simulation scenarios were assessed in the paper. Each file corresponds to 1 of 6 folders, one for each scenario:<br> - scenario 1 - pdemax.rich/results<br> - scenario 2 - pdemax.sparse/results<br> - scenario 3 - pdhillhigh.rich/results<br> - scenario 4 - pdhillhigh.sparse/results<br> - scenario 5 - pdhillhigh.n20rich/results<br> - scenario 6 - pdhillhigh.n20sparse/results</p> <p>In each "results" subfolder, the results for each bootstrap method and each dataset are written to a separate file, eg for simulation 1 in the first scenario:<br> - case bootstrap: scenarioHill1_bootstrapCase_sim1.res <br> - non-parametric bootstrap: scenarioHill1_bootstrapNP_sim1.res<br> - conditional non-parametric bootstrap: scenarioHill1_bootstrapNPc_sim1.res<br> - parametric bootstrap: scenarioHill1_bootstrapPar_sim1.res<br> The folder also contains:<br> - the saemix estimates for the 200 simulations: scenarioHill1_fitOrig.res<br> - tables with the bias and SE for the different bootstraps over the set of simulations, used to evaluate the methods: rbiasSEboot200.res, rbiasSEboot.res, rbiasWRsampleEstimates.res</p> <p> </p>
Data from: A novel iterative mixed model to remap three complex orthopedic traits in dogs
Hip dysplasia (HD), elbow dysplasia (ED), and rupture of the cranial (anterior) cruciate ligament (RCCL) are the most common complex orthopedic traits of dogs and all result in debilitating osteoarthritis. Herein we reanalyzed previously reported data: the Norberg angle (a quantitative measure of HD) in 921 dogs, ED in 113 cases and 633 controls, and RCCL in 271 cases and 399 controls and their genotypes at ~185,000 single nucleotide polymorphisms. A novel fixed and random model with a circulating probability unification (FarmCPU) function, with marker-based principal components and a kinship matrix to correct for population stratification, was used. A Bonferroni correction at p<0.01 resulted in a pgenome of 6.96 ×10-8. Six loci were identified; three for HD and three for RCCL. An associated locus at CFA28:34,369,342 for HD was described in the same dogs using a conventional mixed model. No loci were identified for RCCL in the previous report but the two loci for ED in the previous report did not reach genome-wide significance using the FarmCPU model. These results were supported by simulation which demonstrated that the FarmCPU held no power advantage over the linear mixed model for the ED sample but provided additional power for the HD and RCCL samples. Candidate genes for HD and RCCL are discussed. When using FarmCPU software, we recommend a resampling test, that a positive control be used to determine the optimum pseudo quantitative trait nucleotide-based covariate structure of the model, and a negative control be used consisting of permutation testing and the identical resampling test as for the non-permuted phenotypes.
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.
ScienceDex guides
Understand access before you commit
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.