Research Skills for Geoscientists

Course materials

This site hosts the learning materials for the first term of GEOL2347 Research Skills for Geoscientists.

Term overview

This term will help you to think like a geoscientist. You will learn to construct a scientific question, to collect useful evidence, interpret and present it honestly, and to construct and communicate convincing arguments.

You will become familiar with R, the most widely used software for statistical analysis; and git, the standard tool for managing scientific data and software.

In the first four weeks, we will use a mini-project to introduce fundamental concepts and techniques, and as an opportunity to get familiar with R and GitHub.

In weeks 5–10, your group will select a research question and apply more sophisticated techniques to produce a convincing report that presents your own evidence-based take on a contested topic.

What’s here?

The top menu bar (collapsed behind ≡ on narrow screens) contains links to:

In many sessions, you will write and execute your own R code in browser-based exercises. These exercises are linked in the tables below.

Your browser will (usually) remember what you type between visits. But you should always copy your finished code into an .R file in your GitHub repo and commit it, to maintain a persistent record of your work.

Phase 1: Biomass Mini-Project (Weeks 1–4)

Does UK biomass power help reach net zero, and should we keep paying for it? You’ll explore real energy data, build figures, test assumptions, and write a policy briefing.

Week Content Session Application Session
1 — Meet the data
2 Your first ggplot Making the biomass case
3 — Changing the assumptions
4 Policy briefings for and against AI use Write, review, reveal

Phase 2: Your Project (Weeks 5–10)

Choose your own sustainable energy question and investigate it.

Week Content Session Application Session
5 Designing your investigation Planning your investigation
6 Check your assumptions First tests
7 Comparing groups Deepening your analysis
8 Models and their limits Fitting and breaking models
9 — Checking your work
10 Common pitfalls Write and submit