Tutorials on Data Management

Analysis and Workflows

Tutorials on Data ManagementLesson 12: Analysis and Workflows

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

Topics


• After completing this lesson, the participant will be able to: o Understand a subset of typical analyses usedo Define a workflowo Define a SWFo Discuss the benefits of workflows in general and SWF in particularo Locate resources for using SWF

Learning Objectives


The Data Life CyclePlan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze


• Conducted via personal computer, grid, cloud computing• Statistics, model runs, parameter estimations, graphs/plots

etc.

Data Analyses


• Processing: subsetting, merging, manipulating◦ Reduction: important for high-resolution datasets◦ Transformation: unit conversions, linear and nonlinear algorithms

Types of Analyses

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Date timeair temp precip

C mm11-Jul-07 5:0027.6 00011-Jul-07 6:0027.6 00011-Jul-07 7:0027.7 00311-Jul-07 8:0028.2 01711-Jul-07 9:0028.5 00011-Jul-07 10:0029.3 00011-Jul-07 11:0030.1 00011-Jul-07 12:0030.4 000

Recreated from Michener & Brunt (2000)


• Graphical analyseso Visual exploration of data: search for patternso Quality assurance: outlier detection

Types of Analyses

Box and whisker plot of temperature by monthScatter plot of August Temperatures

Strasser, unpub. dataStrasser, unpub. data


• Statistical analyses Conventional statistics

o Experimental datao Examples: ANOVA, MANOVA, linear and

nonlinear regressiono Rely on assumptions: random sampling, random & normally distributed error, independent error terms, homogeneous variance

Descriptive statisticso Observational or descriptive datao Examples: diversity indices, cluster

analysis, quadrant variance, distance methods, principal component analysis, correspondence analysis

Types of Analyses

From Oksanen (2011) Multivariate Analysis of Ecological Communities in R: vegan tutorial

Example of Principle Component Analysis


• Statistical analyses (continued)o Temporal analyses: time serieso Spatial analyses: for spatial autocorrelationo Nonparametric approaches useful when conventional assumptions

violated or underlying distribution unknowno Other misc. analyses: risk assessment, generalized linear models,

mixed models, etc.• Analyses of very large datasetso Data mining & discoveryo Online data processing

Types of Analyses


• Re-analysis of outputs• Final visualizations: charts, graphs, simulations etc.

After Data Analysis

Science is iterative: The process that results in the final product

can be complex


• Reproducibility at core of scientific method• Complex process = more difficult to reproduce• Good documentation required for reproducibilityo Metadata: data about datao Process metadata: data about process used to create, manipulate,

and analyze data

Reproducibility

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• Information about process used to get to data outputs• Related concept: data provenanceo Origins of datao Good provenance = able to follow data throughout entire life cycleo Allows for

• Replication & reproducibility• Analysis for potential defects, errors in logic, statistical errors• Evaluation of hypotheses

Process Metadata


• Formalization of process metadata• Precise description of scientific procedure• Conceptualized series of data ingestion, transformation, and

analytical steps• Three componentso Inputs: information or material requiredo Outputs: information or material produced & potentially used as

input in other stepso Transformation rules/algorithms (e.g. analyses)

Workflows in General


• Simplest form of workflow: flow chart


Data import into R

Analysis: mean, SD

Graph production

Quality control & data cleaning




Transformation Rules

Data import into R

Analysis: mean, SD

Graph production





Temperature data

Salinity data

Data import into R

Analysis: mean, SD


“Clean” T & S data

Inputs & Outputs

Summary statistics

Data in R format


• Science is becoming more computationally intensive

• Sharing workflows benefits scienceo Scientific workflow systems make documenting workflows easier

• Simplest workflows: scripted languages



• 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

Scientific Workflows (SWF)


• Single access point for multiple analyses across software packages

• Keeps track of analysis and provenance: enables reproducibilityo Each step & its parameters/requirements formally recorded

• Workflow can be stored• Allows sharing and reuse of individual steps or overall

workflowo Automate repetitive taskso Use across different disciplines and groupso Can run analyses more quickly since not starting from scratch

Benefits of SWF


• 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 applicationso Theoretical models or observational analyseso Hierarchical modelingo Can have nested workflowso Can access data from web-based sources (e.g. databases)

• Downloads and more information at kepler-project.org

Example of SWF: Kepler



Drag & drop components from this list

Actors in workflow



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.



Resulting output


• Open-source• Workflow & provenance management support• Geared toward exploratory computational taskso Can manage evolving SWFo Maintains detailed history about steps & data

• www.vistrails.org

Other SWF Tools: VisTrails

Screenshot example


• 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

Other SWF Tools: myExperiment


• Scientists should document workflows used to create resultso Data provenanceo Analyses and parameters usedo Connections between analyses via inputs and outputs

• Documentation can be informal (e.g. flowchart) or formal (e.g. Kepler)

Best Practices for Data Analysis


• Modern science is computer-intensiveo Heterogeneous data, analyses, software

• Reproducibility is important• Workflows = process metadatao Necessary for reproducibility, repeatability, validation

• SFW offers formal systems for documenting process metadatao Storage, sharing, visualization, reuse

Summary


• 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. McPhillips, 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).

Resources for Data Analysis & SWF


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