About the course
Universities, journals, and funding bodies increasingly expect researchers to work in open and reproducible ways. This means making your data FAIR (findable, accessible, interoperable, and reusable), your workflows reproducible, and your science transparent.
This hands-on course helps you build exactly these skills. You will learn practical tools and methods for open, reproducible, and transparent research, with a strong focus on R and related tools. Experts will give specialised lectures and guide you through exercises, and you are encouraged to bring your own data and coding problems so you can work directly on your own research.
Key topics covered:
- Data wrangling, functions, R packages, reproducible documents, GitHub, reproducible environments, and pipeline tools
- Data publishing, storing, sharing, and reusing in relation to GBIF biodiversity data and Darwin Core standards.
- Computer programming using the GBIF API (Application Programming Interface).
- Using artificial intelligence (AI) for supported programming.
Learning outcomes
By the end of the course, participants will be able to:
- Explain the core principles of open, reproducible, and transparent science, and how they relate to current expectations in ecology and environmental research.
- Apply the FAIR principles (findable, accessible, interoperable, reusable) to their own data by organising, documenting, and licensing datasets appropriately.
- Develop reproducible analysis workflows in R, including basic data wrangling, functions, and reproducible documents (e.g. R Markdown/Quarto), and track changes using GitHub.
- Find, access, and reuse biodiversity data from GBIF using Darwin Core standards and simple API queries, and integrate these data into their own projects.
- Use AI-assisted tools and reproducible environments in a critical way to support coding, documentation, and sharing of their research workflows.
Target group
The course is aimed at PhD students (who will be prioritised) as well as early-career researchers (MSc, postdocs) in ecology and related fields who produce their own data, use secondary data, or both.
Language
English
Credits
3 ECTS, issued by the University of Bergen
Assessment
Assessment is based on active participation and completion of mandatory exercises.
Admission and application
Eligibility and prerequisites
Some technical knowledge of programming in R is an advantage.
How to apply
Applications for this course are now closed.
We encourage all course applicants to become Associate Members of BIOCORE to receive updates and information about future events.
Not a member yet? Become a BIOCORE member here.
Selection process
PhD students will be prioritised.
Course fee
Course Fee: Approximately 4000 NOK (includes accommodation and meals).
Note: Participants must organise and pay for their own travel to Finse.