Replication is a fundamental tenet of science, but there is increasing fear among scientists that too few scientific studies can be replicated. This has been termed the “replication crisis”(Ioannidis 2005, Schooler 2014). Scientific papers often include inadequate detail to enable replication (Haddaway and Verhoeven 2015, Archmiller et al. 2020), many attempted replications of well-known scientific studies have failed in a wide variety of disciplines (Moonesinghe et al. 2007, Hewitt 2012, Bohannon 2015, Open Science Collaboration 2015), and rates of paper retractions are increasing (Cokol et al. 2008, Steen et al. 2013). Because of this, researchers are working to develop new ways for researchers, research institutions, research funders, and journals to overcome this problem (Peng 2011, Fiedler et al. 2012, Sandve et al. 2013, Stodden et al. 2013).

Because replicating studies with new independent data is expensive, rarely published in high-impact journals, and sometimes even methodologically impossible, computationally reproducible research (most often termed simply “reproducible research”) is often suggested as a pathway for increasing our ability to assess the validity and rigor of scientific results (Peng 2011). Research is reproducible when others can reproduce the results of a scientific study given only the original data, code, and documentation (Essawy et al. 2020). This approach focuses on the research process after data collection is complete,