Tutorials on Data Management CC image by MarcSmith

Tutorials on Data Management CC image by Marc_Smith on Flickr Lesson 12: Analysis and Workflows

Topics • • • Review of typical data analyses Reproducibility & provenance Workflows in general Computer-based scientific workflows (SWF) Benefits of SWF Examples of SWF and associated tools Analysis and Workflows

Learning Objectives • After completing this lesson, the participant will be able to: o Understand a subset of typical analyses used o Define a workflow o Define a SWF o Discuss the benefits of workflows in general and SWF in particular o Locate resources for using SWF Analysis and Workflows

The Data Life Cycle Plan Collect Analyze Integrate Assure Discover Describe Preserve Analysis and Workflows

Data Analyses • Conducted via personal computer, grid, cloud computing • Statistics, model runs, parameter estimations, graphs/plots etc. Analysis and Workflows

Types of Analyses • Processing: subsetting, merging, manipulating ◦ Reduction: important for high-resolution datasets ◦ Transformation: unit conversions, linear and nonlinear algorithms 0711070500276000 0711070600276000 0711070700277003 0711070800282017 0711070900285000 0711071000293000 071100301000 0711071200304000 Date time 11 -Jul-07 11 -Jul-07 5: 00 6: 00 7: 00 8: 00 9: 00 10: 00 11: 00 12: 00 air temp C 27. 6 27. 7 28. 2 28. 5 29. 3 30. 1 30. 4 precip mm 000 003 017 000 000 Recreated from Michener & Brunt (2000) Analysis and Workflows

Types of Analyses • Graphical analyses o Visual exploration of data: search for patterns o Quality assurance: outlier detection Scatter plot of August Temperatures Strasser, unpub. data Analysis and Workflows Box and whisker plot of temperature by month Strasser, unpub. data

Types of Analyses • Statistical analyses Conventional statistics Example of Principle Component Analysis o Experimental data o Examples: ANOVA, MANOVA, linear and nonlinear regression o Rely on assumptions: random sampling, random & normally distributed error, independent error terms, homogeneous variance Descriptive statistics o Observational or descriptive data o Examples: diversity indices, cluster analysis, quadrant variance, distance methods, principal component analysis, correspondence analysis Analysis and Workflows From Oksanen (2011) Multivariate Analysis of Ecological Communities in R: vegan tutorial

Types of Analyses • Statistical analyses (continued) o Temporal analyses: time series o Spatial analyses: for spatial autocorrelation o Nonparametric approaches useful when conventional assumptions violated or underlying distribution unknown o Other misc. analyses: risk assessment, generalized linear models, mixed models, etc. • Analyses of very large datasets o Data mining & discovery o Online data processing Analysis and Workflows

After Data Analysis • Re-analysis of outputs • Final visualizations: charts, graphs, simulations etc. Science is iterative: The process that results in the final product can be complex Analysis and Workflows

Reproducibility • Reproducibility at core of scientific method • Complex process = more difficult to reproduce • Good documentation required for reproducibility CC image by Felix 63 on Flickr o Metadata: data about data o Process metadata: data about process used to create, manipulate, and analyze data Analysis and Workflows

Process Metadata • Information about process used to get to data outputs • Related concept: data provenance o Origins of data o Good provenance = able to follow data throughout entire life cycle o Allows for • Replication & reproducibility • Analysis for potential defects, errors in logic, statistical errors • Evaluation of hypotheses Analysis and Workflows

Workflows in General • Formalization of process metadata • Precise description of scientific procedure • Conceptualized series of data ingestion, transformation, and analytical steps • Three components o Inputs: information or material required o Outputs: information or material produced & potentially used as input in other steps o Transformation rules/algorithms (e. g. analyses) Analysis and Workflows

Workflows in General • Simplest form of workflow: flow chart Data import into R Quality control & data cleaning Analysis: mean, SD Graph production Analysis and Workflows

Workflows in General • Simplest form of workflow: flow chart Transformation Rules Data import into R Quality control & data cleaning Analysis: mean, SD Graph production Analysis and Workflows

Workflows in General • Simplest form of workflow: flow chart Inputs & Outputs Temperature data Data import into R Salinity data “Clean” T & S data Data in R format Quality control & data cleaning Analysis: mean, SD Summary statistics Analysis and Workflows

Workflows in General • Science is becoming more computationally intensive • Sharing workflows benefits science o Scientific workflow systems make documenting workflows easier • Simplest workflows: scripted languages Analysis and Workflows

Scientific Workflows (SWF) • Analytical pipeline • Each step can be implemented in different software systems • Each step & its parameters/requirements formally recorded • Allows reuse of both individual steps and overall workflow Analysis and Workflows

Benefits of SWF • Single access point for multiple analyses across software packages • Keeps track of analysis and provenance: enables reproducibility o Each step & its parameters/requirements formally recorded • Workflow can be stored • Allows sharing and reuse of individual steps or overall workflow o Automate repetitive tasks o Use across different disciplines and groups o Can run analyses more quickly since not starting from scratch Analysis and Workflows

Example of SWF: Kepler • Open-source, free, cross-platform • Drag-and-drop interface for workflow construction • Steps (analyses, manipulations etc) in workflow represented by “actor” • Actors connect from a workflow • Possible applications o Theoretical models or observational analyses o Hierarchical modeling o Can have nested workflows o Can access data from web-based sources (e. g. databases) • Downloads and more information at kepler-project. org Analysis and Workflows

Example of SWF: Kepler Drag & drop components from this list Analysis and Workflows Actors in workflow

Example of SWF: Kepler This model shows the solution to the classic Lotka. Volterra predator prey dynamics model. It uses the Continuous Time domain to solve two coupled differential equations, one that models the predator population and one that models the prey population. The results are plotted as they are calculated showing both population change and a phase diagram of the dynamics. Analysis and Workflows

Example of SWF: Kepler Resulting output Analysis and Workflows

Other SWF Tools: Vis. Trails • Open-source • Workflow & provenance management support • Geared toward exploratory computational tasks o Can manage evolving SWF o Maintains detailed history about steps & data • www. vistrails. org Screenshot example Analysis and Workflows

Other SWF Tools: my. Experiment • Social networking site to support scientists that use SWF • Allows searching for, sharing, reuse of SWF • Can comment on and discuss contributed SWF • Gateway to journals and data repositories • www. myexperiment. org Analysis and Workflows

Best Practices for Data Analysis • Scientists should document workflows used to create results o Data provenance o Analyses and parameters used o Connections between analyses via inputs and outputs • Documentation can be informal (e. g. flowchart) or formal (e. g. Kepler) Analysis and Workflows

Summary • Modern science is computer-intensive o Heterogeneous data, analyses, software • Reproducibility is important • Workflows = process metadata o Necessary for reproducibility, repeatability, validation • SFW offers formal systems for documenting process metadata o Storage, sharing, visualization, reuse Analysis and Workflows

Resources for Data Analysis & SWF • Y. Gil, E. Deelman, M. Ellisman, T. Fahringer, G. Fox et al. Examining the • • • Challenges of Scientific Workflows. Computer 40, 24– 32 (2007). K. Michener, J. Beach, M. Jones, B. Ludäscher, D. Pennington et al. A knowledge environment for the biodiversity and ecological sciences. J. Intel. Info. Sys. 29, 111– 126 (2007). B. Ludäscher, I. Altintas, S. Bowers, J. Cummings, T. Critchlow et al. Scientific Process Automation and Workflow Management. Comp. Sci. Ser. Ch 13 (Chapman and Hall, Boca Raton, 2009). T. Mc. Phillips, S. Bowers, D. Zinn, B. Ludäscher. Scientific workflow design for mere mortals. Fut. Gen. Comp. Sys. 25, 541 -551 (2009). B. Ludäscher, I. Altintas, C. Berkley, D. Higgins, E. Jaeger-Frank et al. Scientific workflow management and the kepler system. Conc. Comp. Prac. Exper. , 18 (2006). W. Michener and J. Brunt, Eds. Ecological Data: Design, Management and Processing. (Blackwell, New York, 2000). Analysis and Workflows

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