New technologies and approaches are generating large, diverse data sets, and data science offers the tools that are needed to interrogate, analyze, and manage these data sets. Biology, material sciences, and other fields have embraced data science tools and used them to gain insights into, for example, gene–environment interactions, molecular mechanisms of disease, and implications of material characteristics on performance. Chemical sciences and engineering have also used data science tools to, for example, monitor and control chemical processes, predict activity depending on chemical structures or properties, and inform business and research decisions. However, data science applications in the chemical sciences and engineering community have been relatively limited, and many opportunities for advancing the fields have gone unexplored. Accordingly, the National Academies of Sciences, Engineering, and Medicine held a workshop to explore opportunities to use data science to transform chemical sciences and engineering on February 27–28, 2018, in Washington, DC. Stakeholders from academia, government, and industry convened to discuss the challenges and opportunities to integrate data science into chemical sciences and engineering practice and data science training for the future chemical sciences and engineering workforce. This publication summarizes the presentations and discussions from the workshop.
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