Data Analysis and Workflows

Oct 26, 2026

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Data Analysis and Workflows lesson materials introduce typical data analyses and the role of workflows in making research more reproducible and traceable. Content covers reproducibility and provenance, informal versus formal workflows, and the data life cycle from data analyses through to re-analysis of outputs and final visualizations. 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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages35

Reading Time1 h 28 min

AccessFree to download

Lesson Topics

Review of typical data analyses

Reproducibility & provenance

Workflows in general

The Data Life Cycle

Data Analyses

Types of Analyses

Statistical Analyses

Conventional Statistics

Descriptive Statistics

Temporal and Spatial Analyses

After Data Analysis

Re-analysis of outputs

Final visualizations

Data Analysis and Workflows DataONE Community Engagement & Outreach Working Group Lesson Topics Review of typical data analyses Reproducibility & provenance Workflows in general Informal workflows Formal workflows Learning Objectives 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 The Data Life Cycle Data Analyses Processes: Conducted via personal computer, grid, cloud computing Statistics, model runs, parameter estimations, graphs/ plots, etc . Types of Analyses Processing: sub setting, merging, manipulating Reduction: important for high-resolution datasets Transformation: unit conversions, linear and nonlinear algorithms Types of Analyses 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 From Oksanen (2011) Multivariate Analysis of Ecological Communities in R: vegan tutorial Types of Analyses Statistical analyses ( continued) Temporal analyses: time series Spatial analyses: for spatial autocorrelation Nonparametric approaches useful when conventional assumptions violated or underlying distribution unknown Other mis . analyses: risk assessment, generalized linear models, mixed models, etc . Analyses of very large datasets Data mining and discovery Online data processing 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

FAQs

What are the main learning objectives of the lesson?

Participants learn typical analyses, the definition of a workflow, the differences between informal and formal workflows, and the benefits of workflows.

How are data analyses typically carried out in this lesson?

Analyses are conducted via personal computer, grid, or cloud computing and include statistics, model runs, parameter estimations, and graphs or plots.

What happens after performing data analysis?

The materials emphasize re-analysis of outputs and producing final visualizations such as charts, graphs, and simulations, noting that science is iterative.

Oct 26, 2026

Upload by Kurz