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+---
+title: Reproducibility in Scientific Computing
+authors: Jack Franklin & Marion Weinzierl
+---
+
+# Introduction: What is reproducibility?
+- Reproducing results
+- Portability
+- 
+
+# A likely scenario
+
+- You have just joined a new research group as a Student/Researcher/PI.
+- The group use a custom pipeline/setup to perform their data analysis/simulations.
+- You try to get the setup working on your local system/a new hpc system and...
+    It doesn't work!
+
+# Version Control
+
+- First put things into VC
+- Then any changes/fixes can be tracked
+
+$-- Since git will be covered during the week we shouldn't need to do too much
+$-- here.
+
+# Documentation
+
+- README
+- User Docs
+- Dev Docs
+- Comments?
+
+# Dependencies
+
+- Basic documentation
+- Project files (e.g. project.toml for python etc)
+- System dependencies (nix/guix/docker?)
+
+# Testing
+- Unit tests
+- Integration tests
+- Automating tests (CI etc)
+
+# FAIR Principles
+- Findability
+- Accessibility
+- Interoperability
+- Reuse
+
+$-- Maybe we should look at this retrospectively, and see what elements we covered and
+$-- where we could improve on.
+$-- Also a good way to talk about why these are good principles to start a project with
+$-- since we can show that they avoid most/all the problems that we had to solve
+
+# Conclusion/Outlook
+