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Welcome to the wiki for data and model analysis!
Data and model analysis @ SRC
This section collects hints and tricks for how to use our in-house technical resources for doing data and model analysis. The page is currently maintained by Ingo and members from the SESLINK group. Updates can be suggested through the Coding club - currently meeting on Tuesdays 16:00.
- Visualization techniques - collection of links from R and Jupyter to produce figures, infographics, ...
- Git version control for paper, data analysis and modeling projects
- NetLogo tips and tricks for designing simulation experiments
- R tips and tricks for statistical analysis
- Gunvor - how to access our computation server
- Jupyter Notebooks on Gunvor
- Linux tips and tricks - how to work with shell commands
- Running simulations in the cloud - just in case Gunvor is not your friend, you might want to consider run heavy analyses somewhere
- Available software at SU and SRC
- Technical documentation - about external servers
- Bibliometric analysis - different tools and strategies
You have not found what you were looking for? Contact Ingo, Andrea, Erik or Romina to help you further!
Potential upcoming topics
- Navigating Gunvor
- Quality assurance
- improve collaboration
- improve reproducibility, example from "Better science in less time"
- making assumptions visible (e.g. underlying maps)
- How to get 'big data'?
- from behind paywals?
- how to deal with that data later on? (when you need to make it available, open access ...)
- licenses
- platforms, portals (e.g. on SU where you can get a doi)
- open data surveys
- SND - swedish data service
Literature
- Good enough practices in scientific computing Wilson et al. 2017
Tutorials
Send us your favourite training websites and cheat sheetes!