The hidden benefits of open-source software

November 29, 2015
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(This article was first published on Hyndsight » R, and kindly contributed to R-bloggers)

I’ve been having discussions with colleagues and university administration about the best way for universities to manage home-grown software.

The traditional business model for software is that we build software and sell it to everyone willing to pay. Very often, that leads to a software company spin-off that has little or nothing to do with the university that nurtured the development. Think MATLAB, S-Plus, Minitab, SAS and SPSS, all of which grew out of universities or research institutions. This model has repeatedly been shown to stifle research development, channel funds away from the institutions where the software was born, and add to research costs for everyone.

I argue that the open-source model is a much better approach both for research development and for university funding. Under the open-source model, we build software, and make it available for anyone to use and adapt under an appropriate licence. This approach has many benefits that are not always appreciated by university administrators.

  1. It leads to a far greater impact on international practice than anything else you can to do promote your new methodology or algorithms. Surely this is something we want to do as university-based researchers. My forecasting algorithms have had a big impact, not because I wrote a few papers in statistical journals, but because I also wrote some R packages that allow anyone to try out my methods on their own data.
  2. As a result of other researchers being able to implement your ideas easily, your work gets cited much more frequently. Anyone who uses your open source software is obliged to cite the software product and the underlying research papers that describe the methods and algorithms. Citations are used as a crude measure of prestige within universities. If you want to get promoted, having lots of citations helps. Citations also feed into university rankings.
  3. Instead of charging for the software, you can charge for a consulting service to help people use, modify and integrate your software into other systems. In my own group, this approach helps fund two post-docs and several research assistants. I think we easily generate more dollars in consulting income every year than we would ever get from software sales, while simultaneously changing the way forecasting is conceived and implemented all over the world.
  4. More skilled jobs are created as we need consultants to undertake new projects as the service grows. We are training people to tackle and solve difficult data science problems. In contrast, commercial software vendors employ people to generate sales.
  5. The consulting projects lead to new research ideas and new software tools that help fuel the ongoing research enterprise. A condition of all my consulting projects is that any new ideas and software that arise as a result can be published.
  6. This approach makes the research we do more useful. We tackle research problems that are motivated by consulting projects, and will therefore tend to be more relevant and applicable than if we just did research in isolation.
  7. When a large number of researchers follow this model, we have wonderful repositories of open-source software such as CRAN. This reduces research costs, and allows quick implementation and adaption of other people’s research ideas.

 

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