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814 results for “Time Analysis”
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
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Data from: Vegetation growth responses to climate change: A cross-scale analysis of biological memory and time-lags using tree ring and satellite data
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Data from: Research and exploratory analysis driven - time-data visualization (read-tv) software
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Data from: Girth increment changes in response to soil water availability in lowland dipterocarp forest in Borneo: an individualistic time-series analysis
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BayesW time-to-event analysis posterior outputs and summary statistics
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Data for: Developmental plasticity in anurans: meta-analysis reveals effects of larval environments on size at metamorphosis and timing of metamorphosis
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Time series methods for the analysis of soundscapes and other cyclical ecological data
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A Subset of CyberShake Ground Motion Time Series for Response History Analysis
<p>A subset of CyberShake numerically simulated ground motions that were selected and vetted for use in engineering response history analyses.</p> <p>v1.0.1: update readme file</p>
Analysis data for ""Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance""
<p>Analysis data for the manuscript: "Integration of meta-omics data reveals how microbial ecosystems respond to disturbance"</p> <p>Files used with the repository: https://git-r3lab.uni.lu/malte.herold/laots_niche_ecology_analysis/</p> <p>The archive was split into multiple parts for uploading to zenodo which need to be joined in order to extract the files:</p> <pre><code class="language-bash">cat resultsdir_laots.tar.gz.part_* > resultsdir_laots.tar.gz tar xvfz resultsdir_laots.tar.gz</code></pre> <p>Version 2 contains additional files generated in the revision.</p> <p> </p>
Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"
<p>Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR <= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS</p> <p>This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della</p>
Replication package for Nonparametric Analysis of Time-Inconsistent Preferences
<p>Stata datasets, R and Julia files.</p>
Dataset related to "Girth analysis and design of periodically time-varying SC-LDPC codes"
<p>This archive contains the parity-check matrices of the codes considered in the paper "Girth analysis and design of periodically time-varying SC-LDPC codes". Two representations are given: the exponent matrix in time-varying form and the transposed syndrome former matrix in the "alist" format, described in <a href="http://www.inference.org.uk/mackay/codes/alist.html">http://www.inference.org.uk/mackay/codes/alist.html</a>.</p> <p>The archive also contains the following MATLAB support functions:<br> <br> - "expTIfromexpTV.m" returns the exponent matrix in time-invariant form, given the exponent matrix in time-varying form<br> - "expTVfromexpTI.m" returns the exponent matrix in time-varying form, given the exponent matrix in time-invariant form<br> - "alist2sparse.m" returns a sparse binary matrix variable, given a string containing the name of the alist format file<br> - "sparse2alist.m" writes the alist format file for the input sparse binary matrix, with file name specified as second input<br> </p>
Data from: Using time series analysis to characterize evolutionary and plastic responses to environmental change: a case study of a shift toward earlier migration date in sockeye salmon
Environmental change can shift the phenotype of an organism through either evolutionary or nongenetic processes. Despite abundant evidence of phenotypic change in response to recent climate change, we typically lack sufficient genetic data to identify the role of evolution. We present a method of using phenotypic data to characterize the hypothesized role of natural selection and environmentally driven phenotypic shifts (plasticity). We modeled historical selection and environmental predictors of interannual variation in mean population phenotype using a multivariate state-space model framework. Through model comparisons, we assessed the extent to which an estimated selection differential explained observed variation better than environmental factors alone. We applied the method to a 60-year trend toward earlier migration in Columbia River sockeye salmon Oncorhynchus nerka, producing estimates of annual selection differentials, average realized heritability, and relative cumulative effects of selection and plasticity. We found that an evolutionary response to thermal selection was capable of explaining up to two-thirds of the phenotypic trend. Adaptive plastic responses to June river flow explain most of the remainder. This method is applicable to other populations with time series data if selection differentials are available or can be reconstructed. This method thus augments our toolbox for predicting responses to environmental change.
Data from: Microevolution in time and space: SNP analysis of historical DNA reveals dynamic signatures of selection in Atlantic cod
Little is known about how quickly natural populations adapt to changes in their environment and how temporal and spatial variation in selection pressures interact to shape patterns of genetic diversity. We here address these issues with a series of genome scans in four overfished populations of Atlantic cod (Gadus morhua) studied over an 80-year period. Screening of >1000 gene-associated single-nucleotide polymorphisms (SNPs) identified 77 loci that showed highly elevated levels of differentiation, likely as an effect of directional selection, in either time, space or both. Exploratory analysis suggested that temporal allele frequency shifts at certain loci may correlate with local temperature variation and with life history changes suggested to be fisheries induced. Interestingly, however, largely nonoverlapping sets of loci were temporal outliers in the different populations and outliers from the 1928 to 1960 period showed almost complete stability during later decades. The contrasting microevolutionary trajectories among populations resulted in sequential shifts in spatial outliers, with no locus maintaining elevated spatial differentiation throughout the study period. Simulations of migration coupled with observations of temporally stable spatial structure at neutral loci suggest that population replacement or gene flow alone could not explain all the observed allele frequency variation. Thus, the genetic changes are likely to at least partly be driven by highly dynamic temporally and spatially varying selection. These findings have important implications for our understanding of local adaptation and evolutionary potential in high gene flow organisms and underscore the need to carefully consider all dimensions of biocomplexity for evolutionarily sustainable management.
Data from: Large-scale parentage analysis reveals reproductive patterns and heritability of spawn timing in a hatchery population of steelhead (Oncorhynchus mykiss)
Understanding life history traits is an important first step in formulating effective conservation and management strategies. The use of artificial propagation and supplementation as such a strategy can have numerous effects on the supplemented natural populations and minimizing life history divergence is crucial in minimizing these effects. Here, we use single nucleotide polymorphism (SNP) genotypes for large-scale parentage analysis and pedigree reconstruction in a hatchery population of steelhead, the anadromous form of rainbow trout. Nearly complete sampling of the broodstock for several consecutive years in two hatchery programmes allowed inference about multiple aspects of life history. Reconstruction of cohort age distribution revealed a strong component of fish that spawn at 2 years of age, in contrast to programme goals and distinct from naturally spawning steelhead in the region, which raises a significant conservation concern. The first estimates of variance in family size for steelhead in this region can be used to calculate effective population size and probabilities of inbreeding, and estimation of iteroparity rate indicates that it is reduced by hatchery production. Finally, correlations between family members in the day of spawning revealed for the first time a strongly heritable component to this important life history trait in steelhead and demonstrated the potential for selection to alter life history traits rapidly in response to changes in environmental conditions. Taken together, these results demonstrate the extraordinary promise of SNP-based pedigree reconstruction for providing biological inference in high-fecundity organisms that is not easily achievable with traditional physical tags.
Forecasting hourly emergency department arrival using time series analysis
<p></p> Background/aims <p>The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients.</p> Methods <p>Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals.</p> Results <p>The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained.</p> Conclusions <p>The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.</p> <p></p><p></p><p></p>
FIGURE 8. Ultrametric time tree obtained from Analysis 5 in Flightless Notaris (Coleoptera: Curculionidae: Brachycerinae: Erirhinini) in Southwest China: monophyly, mtDNA phylogeography and evolution of habitat associations
FIGURE 8. Ultrametric time tree obtained from Analysis 5 by using BEAST software to date evolutionary events of the Notaris + Tournotaris clade. Numbers at nodes and on the scale below are million years before present. Node bars represent 95% confidence interval of the age estimate. Alternating snowflake and sun symbols denote Pleistocene climatic fluctuations.
Per-chalcopyrite particle morphological analysis data obtaining from three time-lapse micro-CT images and the PhreeqcRM simulation data
<p>The file "overall_particles.xlsx" includes all the image-based quantifications of all chalcopyrite particles extracted from three time-lapse micro-CT images. These image-based quantifications include specific surface area, volume, liberation ratio, mass before leaching, in the middle of leaching, and after leaching.</p> <p> </p> <p>The file "PhreeqcRM simulation.xlsx" includes the PhreeqcRM simulation results using experimental and image-based data.</p> <p> </p> <p>The file "<a href="https://zenodo.org/api/records/15239075/draft/files/Laplace_Solver-master-main.zip/content" target="_blank" rel="noopener noreferrer">Laplace_Solver-master-main.zip</a>" includes the diffusion simulation source code.</p>
Exploring Factors Promoting Dependency Updates with Survival Time Analysis and Logistic Regression
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Fig. 7. Bayesian Skyline Plot analysis showing population size over time. The x in Echinoderes galadrielae Grzelak & Sørensen 2022, sp. nov.
Fig. 7. Bayesian Skyline Plot analysis showing population size over time. The x-axis is the time to the present in years, while the y-axis is the product between the effective population size (Ne) and the generation length (t) in a log scale. The mean estimate (black solid line) and 95% highest probability density limits (grey area) are shown.
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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.