The massive growth of global research activity in recent years has spurred studies exploring how productive this expansion has been and what the future may hold. Although the creativity and serendipity of individual discoveries will remain difficult to model, quantitative research has revealed regularities in the rates of discovery and the outcome of published findings over time (1). Some of these studies demonstrate that innovation has been decreasing, which may reflect the output of a scientific enterprise whose system of input has supported the pursuit of research focused on “low-hanging fruit” (2). Coincident with this decline has been the recent global recession, a circumstance that called for accountability of economic and social returns from public investments in research (3). There is now a great demand for insight into how the system of science works. On page 127 of this issue, Wang et al. (4) offer one approach to assess, and perhaps even augment, scientific productivity.
An emerging area of interest in research on the “science of science” is the prediction of future impact. Impact prediction is consequential for the evaluation of research grants, the dispensing of scholarly awards, and the determination of faculty salaries, among other decisions. As predictions improve, they will play a larger role in directing choices about what areas public and private capital will choose to research, develop, and produce. But how can we predict the future?
A number of recent studies have honed our understanding of the factors that influence future citations to an article or researcher. For example, citations accrue to articles published in scientific journals over time according to a well-behaved log-normal distribution, with a rise in citations at the point of publication followed by a gradual decay (5). There is also a strong “first-mover” advantage to the receipt