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