Reproducibility as a service

Recent studies demonstrated that the reproducibility of previously published computational experiments is inadequate. Many of these published computational experiments are not reproducible, because they never recorded or preserved their computational environment. This environment consists of artifacts such as packages installed in the language, libraries installed on the host system, file names, and directory hierarchy. Researchers have created reproducibility tools to help mitigate this problem, but they do nothing for the experiments that already exist in online repositories. This situation is not improving, as researchers continue to publish results every year without using reproducibility tools, likely due to benign neglect as it is common to believe publishing the code and data is sufficient for reproducibility. To clarify the gap between what existing reproducibility tools are capable of and this issue with published experiments, we define a framework to distinguish between actions taken by a researcher to facilitate reproducibility in the presence of a computational environment and actions taken by a researcher to enable reproduction of an experiment when that environment has been lost. The difference between these approaches in reproducibility lies in the availability of a computational environment. Researchers that provide access to the original computational environment perform proactive reproducibility, while those who do not enable only retroactive reproducibility. We present Reproducibility as a Service (RaaS), which is, to our knowledge, the first reproducibility tool explicitly designed to facilitate retroactive reproducibility. We demonstrate how RaaS can fix many of the common errors found in R scripts on Harvard's Dataverse and preserve the recreated computational environment.Science, Faculty ofComputer Science, Department ofGraduat