Abstract
Recent reproducibility case studies have raised concerns showing that much of
the deposited research has not been reproducible. One of their conclusions was
that the way data repositories store research data and code cannot fully
facilitate reproducibility due to the absence of a runtime environment needed
for the code execution. New specialized reproducibility tools provide
cloud-based computational environments for code encapsulation, thus enabling
research portability and reproducibility. However, they do not often enable
research discoverability, standardized data citation, or long-term archival
like data repositories do. This paper addresses the shortcomings of data
repositories and reproducibility tools and how they could be overcome to
improve the current lack of computational reproducibility in published and
archived research outputs.
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