Our lessons/material

This is not a strict division, but lessons can be classified as to what is initially useful to yourself, to your group, and to the whole community.

Lessons marked with (*) are part of a regular 6 half-day workshop.

• For yourself and larger

  • Introduction to version control (*)
  • Collaborating and sharing using GitHub without command line
  • Jupyter notebooks (*)
  • Automated testing (*)
  • Modular code development
  • Modular code development type-along (*)

• For your group and larger

  • Collaborative distributed version control (*)
  • Git branch design
  • Documentation (*)

• For the community

  • Social coding and open software (*)
  • Reproducible research (*)

• Instructor training and manuals

  • Instructor training
  • CodeRefinery manuals, our guides and hints on running CodeRefinery

• Installation guides

  • Installation instructions (see your workshop page for which ones are actually needed for you)

• Special topics

  • CMake workshop (based on ENCCS version)
  • Mixed Martial Arts: Interfacing Fortran, C, C++, and Python

• Old material (material is outdated and could be revived by somebody)

  • DevOps, automation, and continuous integration
  • Integrated development environments

Video recordings

A number of video recordings of CodeRefinery lessons and workshops and Research Software Hour sessions can be found on our YouTube channel.

Other lessons by our partners and the CodeRefinery community

You can expect these to be open-source, reusable, in a git repository, and with a community you can join and give contributions to:

• Python for Scientific Computing, by Aalto Scientific Computing, UiO, UiT, KTH. Not a basic Python course, but taking your from basic programming to the tools needed for scientific computing.
• Linux shell tutorial. By Aalto Scientific Computing, level: intermediate, covers basics but focus on scripting.
• Introduction to MPI
• Introduction to MPI: Derived from the above.
• Intermediate MPI
• Introduction to High-Performance Computing

Lessons we contributed to as instructors or helpers

• Introduction to Conda for (Data) Scientists: A lesson in the Carpentries Incubator.