Scientific computation is emerging as absolutely central to the scientific method, but the prevalence of very relaxed practices is leading to a credibility crisis. Reproducible computational research, in which all details of computations—code and data—are made conveniently available to others, is a necessary response to this crisis. Results from a 2009 survey of the Machine Learning community (NIPS participants) designed to elucidate factors that affect data and code sharing will be presented. Intellectual property concerns create a significant barrier to sharing, and I will also present work on the “Reproducible Research Standard” giving open licensing options designed to create an intellectual property framework for scientists consonant with longstanding scientific norms and facilitating reproducible research.