At the completion of a computational chemistry or biology research project a large amounts of raw experimental or computational data have typically been consumed to generate new insights or data sets produced by a myriad of relevant programs. A paper is then written up, sent for review, accepted for publication and then dutifully reported in the literature. This has been the workflow for decades, but it begs a range of key questions: What is the fate of the data used experimental or otherwise? What happens to the derived information be it structural, sequence or energetic information? What versions of program(s) were used and what are their fates? Most importantly, is the reported outcome from the computational study reproducible based on the information provided? Too often the answer to many of these questions is no. As a community what can we do to address this issue? Is this something that needs to be driven by scientific societies, journals, and/or funding agencies or by a change in culture? In this presentation we will review the issues involved and provide thoughts about how many of these could be addressed to insure the long-term integrity of computationally derived results.

Long term viability of computational chemistry/biology research

256th National Meeting (2018)

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