Predicting scientific success

Abstract

We research scientists often worry about the future of our careers. Is our research an exciting path or a dead end that will end our careers prematurely? Predicting scientific trajectories is a daily task for hiring committees, funding agencies and department heads who probe CVs searching for signs of scientific potential. One popular measure of success is physicist Jorge Hirsch's h-index 1 , which captures the quality (citations) and quantity (number) of papers, thus representing scientific achievements better than either factor alone. A scientist has an h-index of n if he or she has published n articles receiving at least n citations each 2 . Einstein, Darwin and Feynman, for example, have impressive h-indices of 96, 63 and 53, respectively. According to Hirsch, an h-index of 12 for a physicist — meaning 12 papers with at least 12 citations each — could qualify him or her for tenure at a major university. However, the h-index 3 and similar metrics 4 can capture only past accomplishments, not future achievements 5 . Here we attempt to predict the future h-index of scientists on the basis of features found in most CVs. We maintain that the best way of predicting a scientist's future success is for peers to evaluate scientific contributions and research depth, but think that our methods could be valuable complementary tools. The typical research CV contains information on the number of publications, those in high-profile journals, the h-index and collaborators. One can also infer interdisciplinary breadth, the length and quality of training, the amount of funding received and even the standing of the scientist's PhD adviser. Such factors are taken into account for hiring decisions, but how should they be weighted? Fortunately, obtaining data on the scientific activities of individual researchers has never been easier. Using all of these features, we can begin to probe the scientific enterprise statistically.