Open data science
Here you will find information specific to OHI assessments and for open data science workflows in general. OHI assessments depend upon open data science tools and practices, as described in Lowndes et al. 2017. The following training materials can be used to lead workshops or for self-paced learning. All are under development, but are openly available for use as we work.
- Open data science means that methods, data, and code are available so that others can access, reuse, and build from it without much fuss. This training is not specific to OHI, but provides foundational skills for how to do reproducible research with R, RStudio, Git, and GitHub, as we describe in Lowndes et al. 2017.
- OHI scores are calculated using an open data science workflow and collaborative open software that we call the OHI Toolbox. This training will introduce you to the OHI Toolbox and how to use it to calculate OHI scores. There is also guidance for teams as they prepare to use the Toolbox.
The OHI Manual is a guide for the Conduct phase of the OHI process. It focuses on preparation and use of the OHI Toolbox, which is used to calculate OHI scores.
OHI Goals represent ocean-derived benefits to people. This page lists each goal and sub-goal, along with the philosophy of the goal and an ‘ideal’ approach to how it would be represented. There is also practical guidance for modelling, and examples from completed assessments, including global and OHI+ assessments.
Related news posts
These are some stories and references about open data science from our News page.
- Open data science for marine management
- Better science faster — our Nature Ecology & Evolution pub
- The importance of open data science tools in science: a list of references
- Resources for R and Data Science
Data science training archive
These are other tutorials our team has created that are useful for developing open data science skills and leading OHI assessments. Also see our eco-data-science group.
Git and Github
R and RStudio
- Spatial analysis in R: Rasters
- Spatial analysis in R: Vectors
- Data visualization using ggmap - cheatsheet
- A primer on coordinate reference systems
- Data visualization using ggplot2
- Data visualization using ggplot2 - cheatsheet
- Dealing with color in R