Reproducible Research

I'm currently preparing a series of lessons for neuroscience PhD students which will cover the use of essential software tools for research. The modules will cover what I believe are the most important pieces of software for students to become proficient data managers and efficient paper and report writers.
All the tools listed are open source and allow for collaborative document management. The core principles are well described in Christopher Gandrud's excellent book:
Reproducible Research - with R & RStudio

In preparing the lessons, it's been interesting to survey the tools that I and other researchers use regularly and look at why we use these tools. Here's an outline of the lessons:

  • Document Management
    • LaTeX
    • LaTeX extensions (templates, knitr, texcount)
    • Collaborative Editing (ShareLaTex, Etherpad)
    • Bibliographic Managament (BibTex, Zotero)
    • Version Management with Git
    • Cloud Storage and Security (Owncloud, encfs)

  • Statistics
    • R and R-studio
    • Shiny and knitr
  • Programming for Researchers
    • Why you'll need to program and why it's no harder than cooking
    • OS basics and BASH
    • MATLAB and why (not) to use it
    • Python
  • Graphics
    • Image formats: vector graphics vs bitmaps
    • SVG and the fine art of Inkscape
    • Dynamic graphics with Processing
  • Web Publishing
    • HTML is your friend
    • Intellectual Property and Privacy
    • Running your own blog
    • Integrating with LaTeX
  • Hardware
    • Basic Electronics
    • Arduino
    • Principles of Neuroimaging
    • Putting it all together
The resources for these lessons will be published as open documents in the future. I'd be interested to hear your comments if there are other subjects that you'd add to this plan.

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