  Government funding agencies and foundations tend to perceive novelty as
necessary for scientific impact and hence prefer to fund novel instead of
incremental projects. Evidence linking novelty and the eventual impact of a
grant is surprisingly scarce, however. Here, we examine this link by analyzing
920,000 publications funded by 170,000 grants from the National Science
Foundation (NSF) and the National Institutes of Health (NIH) between 2008 and
2016. We use machine learning to quantify grant novelty at the time of funding
and relate that measure to the citation dynamics of these publications. Our
results show that grant novelty leads to robust increases in citations while
controlling for the principal investigator's grant experience, award amount,
year of publication, prestige of the journal, and team size. All else held
constant, an article resulting from a fully-novel grant would on average double
the citations of a fully-incremental grant. We also find that novel grants
produce as many articles as incremental grants while publishing in higher
prestige journals. Taken together, our results provide compelling evidence
supporting NSF, NIH, and many other funding agencies' emphases on novelty.
