Learning Hub Curriculum
Support Your Learning in Data Science and Open Science
The material NCEAS has developed for in-person courses in open science and data science can be great reference material to use at your own pace. Select materials are listed below. If you are looking for or curious about materials from a specific previous training, click the button at the bottom of this page to view our complete archive.
Select Materials
An introduction to a set of tools used together in the R environment that make research more communicable and reproducible.
Topics:
- Literate analysis (RMarkdown)
- Code versioning (git/GitHub)
- Data tidying/reformatting (tidyr/dplyr)
- Data documentation and publishing
- Publication graphics (ggplot2, leaflet)
Enable data reuse through better data management
- Metadata - what is it and how to write a quality data description
- Data modeling - tidy data for efficient access and storage
- Data publishing, citation, and credit
Build reproducible scientific workflows
- Data munging with R tidyverse
- Working collaboratively - git and GitHub
- Writing functions in R
- Building packages for publishing reproducible research
Communicate results effectively
- Literate analysis with RMarkdown
- Publishing analytical web pages with GitHub pages
- Data visualization with ggplot and leaflet
A more in depth look of computing and analysis techniques for synthesis research.
Topics:
- Open data policies
- Literate analysis (RMarkdown)
- Code versioning (git/GitHub)
- Data tidying/reformatting (tidyr/dplyr)
- Data documentation and publishing
- Publication graphics (ggplot2, leaflet)
- Collaboration and team science
- Reproducibility and Provenance
A more in extensive exploration of computing and analysis techniques and their application to synthesis research.
Topics:
- R for data manipulation, analysis, and visualization
- Linux/UNIX command line environment
- Meta-analysis and systematic reviews
- postgres databases and SQL
- Spatial analysis in R
- Parallel computing in R
Suite of ten customizable presentation files and supporting learning material focused on data management topics across all stages of the Data Life Cycle. The skillbuilding hub also comprises a database of best data management practices.
Topics:
- Why manage data
- Data sharing
- Data management planning
- Data entry and manipulation
- Quality control and assurance
- Protecting your data
- Metadata
- Data citation
- Analysis and workflows
- Legal and policy issues