The following article is recently out, and it makes quite a few of the arguments that have motivated our use of open source software here at UMD. It's a nice article, well worth reading, especially if you need a shot in the arm to remind you why it's worth going the extra mile to not only release code, but to support it and make it easy to use.
You can find the article in full (freely available, of course :) by following the link below. Enjoy, Ted ====================================================== The Need for Open Source Software in Machine Learning Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander Smola, Pascal Vincent, Jason Weston, Robert Williamson; 8(Oct):2443--2466, 2007. Abstract Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not used, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community. [abs][pdf] http://jmlr.csail.mit.edu/papers/v8/sonnenburg07a.html
