PhD Council

Computing Information

Starting your work at Nikhef, you may not be familiar with many of the computing resources and frameworks. The descriptions and links in the lists below may be able to help you out. In addition, this website serves as a source of information on how to obtain and use scientific software at Nikhef.

Mailing lists

Many institutes have a system to send news and announcement emails to a large number of people. If you would like to receive these messages, subscribe to the relevant mailing list.

  • Nikhef automatically subscribes its employees to several mailing lists at the start of their employment. These include the lists Nikhef-in-dienst (Nikhef employees), Nikhef-wide (everyone at Nikhef) and promovendi (PhD students). Other public mailing lists can be found here, and general information about the Nikhef mailing lists can be found here.
  • CERN has e-groups you can subscribe to. They can be found here.

Basic computing tutorials

  • Bash is the default shell for Linux and macOS and is likely to feature in many PhD programmes. A basic tutorial (or refresher for those getting back into the game) can be found here.
  • Vim, Emacs or Nano. The holy editor war rages on. It is useful to know at least one of these terminal-based editors, but which one you choose is up to you. Vim features an interactive tutor program that you can access through the vimtutor command. Emacs has a built-in tutorial you can access by pressing ctrl-h t within the editor itself.
  • Python and C++ are the two most popular programming languages at Nikhef and have huge communities online, complete with tutorials and Q&A websites. The official Python tutorial can be found here, and C++ fans can go here.
  • Git is a distributed version control system that allows for collaborative programming. It is primarily operated via command line, but clients do exist if you prefer a GUI. A great interactive tutorial can be found here.
  • ROOT is a CERN-made modular scientific software framework and is absolutely indispensable for anyone working on HEP data analysis. Searching the web for common problems can be difficult due to the framework’s ambiguous name, but a set of official tutorials can be found here.