Toward a culture of computational reproducibility: Values, principles, and practices
Ensuring that the results of data analysis are both valid and reproducible is a fundamental responsibility of every computational scientist, but both are increasingly difficult in the context of complex analysis workflows and big data. Building off of ideas from software engineering, I will argue that we need to embrace a culture of computational reproducibility. I will outline a set of values that motivate this work and principles that guide the work, and then focus on a set of practices that can help improve reproducibility in computational science. I will conclude by addressing some potential concerns about the impacts of this cultural shift.