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It surveys processes across personal computer, grid, and cloud computing, outlines analysis types such as processing, reduction, transformation, graphical exploration, quality assurance, and statistical approaches including ANOVA, regression, descriptive methods, temporal and spatial analyses, and nonparametric or advanced modeling techniques.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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typical data analyses  Reproducibility & provenance  Workflows in general  \n Informal workflows  \n Formal workflows  \nLearning Objectives  \nAfter completing this lesson, the participant will be able to:  \n Understand a subset of typical analyses used  Define a workflow  \n Understand the concepts informal and formal workflows  Discuss the benefits of workflows  \nThe Data Life Cycle  \nData Analyses  \nProcesses:  \n Conducted via personal computer, grid, cloud computing  \n Statistics, model runs, parameter estimations, graphs/ plots, etc .  \nTypes of Analyses  \nProcessing: sub setting, merging, manipulating  \n Reduction: important for high-resolution datasets  \n Transformation: unit conversions, linear and nonlinear algorithms  \nTypes of Analyses  \n Graphical analyses  \n Visual exploration of data: search for patterns  \n Quality assurance: outlier detection  \nStatistical Analyses  \nConventional Statistics  \n Experimental data  \n Examples: ANOVA, MANOVA, linear and nonlinear regression  \n Rely on assumptions: random sampling, random & normally distributed error, independent error terms, homogeneous variance  \nDescriptive Statistics  \n Observational or descriptive data  \n Examples: diversity indices, cluster analysis, quadrant variance, distance methods, principal component analysis, correspondence analysis  \nFrom Oksanen (2011) Multivariate Analysis of Ecological Communities in R: vegan tutorial  \nTypes of Analyses  \n Statistical analyses ( continued)  Temporal analyses: time series  \n Spatial analyses: for spatial autocorrelation  \n Nonparametric approaches useful when conventional assumptions violated or underlying distribution unknown  \n Other mis . analyses: risk assessment, generalized linear models, mixed models, etc .  \n Analyses of very large datasets  Data mining and discovery  Online data processing  \nAfter Data Analysis  \n Re-analysis of outputs  \n Final visualizations: charts, graphs, simulations, etc  \nScience is iterative: The process that results in the final product can be complex","cbCaijw5hBe6pY4t","https://ap.wps.com/l/cbCaijw5hBe6pY4t","pdf",1477734,35,"English","# Lesson Topics\n## Review of typical data analyses\n## Reproducibility & provenance\n## Workflows in general\n# The Data Life Cycle\n## Data Analyses\n## Types of Analyses\n# Statistical Analyses\n## Conventional Statistics\n## Descriptive Statistics\n## Temporal and Spatial Analyses\n# After Data Analysis\n## Re-analysis of outputs\n## Final visualizations","[{\"question\":\"What are the main learning objectives of the lesson?\",\"answer\":\"Participants learn typical analyses, the definition of a workflow, the differences between informal and formal workflows, and the benefits of workflows.\"},{\"question\":\"How are data analyses typically carried out in this lesson?\",\"answer\":\"Analyses are conducted via personal computer, grid, or cloud computing and include statistics, model runs, parameter estimations, and graphs or plots.\"},{\"question\":\"What happens after performing data analysis?\",\"answer\":\"The materials emphasize re-analysis of outputs and producing final visualizations such as charts, graphs, and simulations, noting that science is iterative.\"}]","Data Analysis and Workflows | PDF",1792976029,88]