Computing plays an increasingly important role in (neuro)science and is central to many aspects of the lab’s workflow. The safety of your hard-won data, the integrity and reproducibility of your analyses and results, the ongoing development and sharing of the best protocols and procedures, and the speed with which you can accomplish your goals all depend critically on correct use of the lab’s computing resources. 

Because many of these are shared (experimental machines) and/or inherently collaborative (lab database, codebase) it is especially important to be aware of the issues below. Experience with some of the more advanced concepts and tools is a highly valued skill in many labs and workplaces; mastery of these will set you apart from many of your peers.

[[computing:computerlist|List of computers]]

====== Getting Started ======

  * [[computing:labcomputers|About the lab computers]]
  * [[computing:matlabsetup|Setting up MATLAB]]
  * [[computing:octavesetup|Setting up Octave]]

====== Code and Project Management ======

  * [[computing:versioncontrol|Version control]]
  * [[computing:provenance|Provenance and traceability]]

====== Data Management ======

  * [[computing:datapromotion|Data promotion]]

====== Software ======

More detailed guides/tutorials for the below can be found in the [[analysis:dataanalysis|DataAnalysi]]s section:

  * MATLAB
  * FieldTrip
  * MClust

Others:

  * Git/GitHub
  * LaTeX
  * SolidWorks
  * Leica
  * NanoZ