Changing computational research. The challenges ahead

EDITORIAL The past year has been an interesting one for those interested in reproducible research. There have been great examples of replicability [1, 2] in research communication, and examples of horrifying failure of reproducibility (as described in [3]) with serious questions being raised on the ability of our current system of research communication to guarantee, or even encourage, that published research be reproducible or replicable. When we launched the call for papers for Open Research Computation in late 2010 we saw a clear need for higher standards. Computational research should stand out as an exemplar of just how reproducible research can be, yet it falls short more often than not. With modern computational tools it is entirely possible to provide packages which allow direct replication of results. It is possible to provide data and code in the form of a functional virtual machine image along with automated tests to ensure everything is working as expected. But alongside this we can support the reader’s ability to modify and re-purpose tools, to run them against new data, indeed to support efforts to deliberately break the system to identify its limitations. In short, to do what we are supposed to do as scientists – replicate, reproduce, and test the limits of our models and understanding