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While there is plenty of information available about the luminaries of science, here we discuss the relative lack of information about ordinary researchers. Luckily, because of recent advances in name disambiguation, the career histories of everyday scientists can now be analyzed, changing the way we think about scientific creativity entirely. We describe how the process of shuffling a career – moving the works a scientist publishes around randomly in time – helped us discover what we call the “random impact rule,” which dictates that, when we adjust for productivity, the highest impact work in a career can occur at any time. We also see that the probability of landmark work follows a cumulative distribution, meaning that the random impact rule holds true not just for the highest impact work in any career but also for other important works, too. While there is precedent for this rule in the literature – Simonton proposed the “constant probability of success” model in the 1970s – until recently we didn’t have the data on hand to test it. The random impact rule allows us to decouple age and creativity, instead linking periods of high productivity to creative breakthroughs.
We begin by detailing Einstein’s “miracle year,” during which he published four major discoveries. We discuss the debate over the existence of hot streaks in sports, and ask if a hot-streak phenomenon exists in science. To address this question, we look at the relative timing of hit works during a career, which is captured mathematically by a normalized joint probability. We find that hit works are more likely to colocate than they would by chance, indicating that hot streaks do occur. So while the timing of each highest impact work is random, the relative timing of top papers in a scientist’s career shows a high degree of temporal clustering. To account for these patterns, we introduce a slight variation to the Q-model – a brief period of elevated impact. We call this the “hot-streak model.” The model shows us that hot streaks are ubiquitous across creative careers, that they usually occur only once, and that they occur randomly. We then discuss the implications of these findings for scientists and science administrators, using the life of John Fenn as an example of how the hot-streak model can provide a hopeful framework for scientists still waiting for their big break.
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