| Summary: | The movement toward open science, data sharing, and increased transparency is being propelled by the need to rapidly address critical scientific challenges, such as the global COVID-19 public health crisis. This movement has supported growth in fields, such as artificial intelligence (AI), which has demonstrated potential to accelerate science, engineering, and medicine in new and exciting ways. To further advance innovation around these new approaches, the National Academies of Sciences, Engineering, and Medicine's Board on Research Data and Information convened a public virtual workshop on October 14-15, 2020, to address how researchers in different domains are utilizing data that undergo repeated processing, often in real-time, to accelerate scientific discovery. Although these topics were not originally part of the workshop, the impact of COVID-19 prompted the planning committee to add sessions on early career researchers' perspectives, as well as rapid review and publishing activities as a result of the pandemic. Participants also explored the advances needed to enable future progress in areas such as AI, cyberinfrastructure, standards, and policies. This publication summarizes the presentations and discussion of the workshop
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