Our ability to collect data at every stage of the translational pipeline creates great opportunities for formulating hypotheses both “upstream” (towards clinical implementation) and “downstream” (back to basic discovery). Translational researchers therefore must integrate information at multiple scales to both generate and test hypotheses—to some extent they must all be comfortable with the basics of “big data” analyses. This increased focus on data-driven science requires an understanding of basic experimental and clinical data collection—understanding that likely cannot efficiently be gathered through traditional apprenticeship models. Thus, new curricula are required to ensure that next-generation scientists have a new combination of skills required for integrating data to catalyze discovery. © 2017 American Society for Clinical Pharmacology and Therapeutics
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