ABSTRACT

This project seeks to improve understanding of how the scientific community can adapt to the increasing use of computing and large-scale data resources. One challenge is ensuring that computational results - such as those from simulations - are "reproducible," that is, the same results are obtained when one re-uses the same input data, methods, software and analysis conditions. In 2019, the National Academies of Science, Engineering, and Medicine (NASEM) issued a report on "Reproducibility and Replication in Science" with a series of recommendations. The project will assess the implications of these recommendations on the scientific discovery process for computationally- and data-enabled research.

The following research questions will guide this study: (1) what reproducibility issues are surfaced by the NASEM recommendations and what constraints and requirements do they imply for computational infrastructure?; and (2) what are the implications of these issues and constraints on the computational infrastructure ecosystem as a whole? To explore and illustrate answers to these questions, we will employ diverse scientific use cases chosen to cover different ways researchers interact with computational infrastructure. Formalisms will also be applied to the use cases to articulate the role of computational infrastructure in enabling transparency and reproducibility, and to elucidate how computational infrastructure can conform to the NASEM report recommendations. The overall aim is to articulate avenues for future research at the intersection of transparency, reproducibility and computational infrastructure that supports scientific discovery.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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