Tutorials on Data ManagementL10 Analysis Workflows参考.pptx

Tutorials on Data Management;Review of typical data analyses Reproducibility provenance Workflows in general Informal workflows Formal workflows;After completing this lesson, the participant will be able to: Understand a subset of typical analyses used Define a workflow Understand the concepts informal and formal workflows Discuss the benefits of workflows ; ;Conducted via personal computer, grid, cloud computing Statistics, model runs, parameter estimations, graphs/plots etc. ;Processing: subsetting, merging, manipulating Reduction: important for high-resolution datasets Transformation: unit conversions, linear and nonlinear algorithms ;Graphical analyses Visual exploration of data: search for patterns Quality assurance: outlier detection ;Statistical analyses Conventional statistics Experimental data Examples: ANOVA, MANOVA, linear and nonlinear regression Rely on assumptions: random sampling, random normally distributed error, independent error terms, homogeneous variance Descriptive statistics Observational or descriptive data Examples: diversity indices, cluster analysis, quadrant variance, distance methods, principal component analysis, correspondence analysis ;Statistical analyses (continued) Temporal analyses: time series Spatial analyses: for spatial autocorrelation Nonparametric approaches useful when conventional assumptions violated or underlying distribution unknown Other misc. analyses: risk assessment, generalized linear models, mixed models, etc. Analyses of very large datasets Data mining discovery Online data processing ;Re-analysis of outputs Final visualizations: charts, graphs, simulations etc. ;Reproducibility at core of scientific method Complex process = more difficult to reproduce Good documentation required for reproducibility Metadata: data about data Process metadata: data about process used to create, manipulate, and analyze data ;Process metadata: Information about process (analysis, data organization,