Although the importance of multiple studies corroborating a given result is acknowledged in virtually all of the sciences (Figure 1), the modern use of "reproducible research" was originally applied not to corroboration, but to transparency, with application in the computational sciences. Computer scientist Jon Claerbout coined the term and associated it with a software platform and set of procedures that permit the reader of a paper to see the entire processing trail from the raw data and code to figures and tables (4). This concept has been carried forward into many data-intensive domains, including epidemiology (5), computational biology (6), economics (7), and clinical trials (8). According to a U.S. National Science Foundation (NSF) subcommittee on replicability in science (9), "reproducibility refers to the ability of a researcher to duplicate the results of a prior study using the same materials as were used by the original investigator. That is, a second researcher might use the same raw data to build the same analysis files and implement the same statistical analysis in an attempt to yield the same results…. Reproducibility is a minimum necessary condition for a finding to be believable and informative."
Jul-28-2019, 22:43:16 GMT