Reproducibility in Computational and Data-enabled Science

As computation becomes central to scientific research and discovery new questions arise regarding the implementation, dissemination, and evaluation of methods that underlie scientific claims. I present a framework for conceptualizing the affordances that support scientific inference including computational reproducibility, transparency, and generalizability of findings. For example, reproducibility in computational research can be interpreted most narrowly as a simple trace of computational steps that generate scientific findings, and most expansively as an entirely independent implementation of an experiment that tests the same hypothesis as previously published work. Standards for determining a scientific finding are necessarily adapting to computationally-and data-enabled research. Finally, the social context for these innovations raises important questions regarding incentives to engage in new research practices and the ethics of these practices themselves.