title: Publication metrics and success on the academic job market.
abstract: The number of applicants vastly outnumbers the available academic faculty positions. What makes a successful academic job market candidate is the subject of much current discussion [1-4]. Yet, so far there has been no quantitative analysis of who becomes a principal investigator (PI). We here use a machine-learning approach to predict who becomes a PI, based on data from over 25,000 scientists in PubMed. We show that success in academia is predictable. It depends on the number of publications, the impact factor (IF) of the journals in which those papers are published, and the number of papers that receive more citations than average for the journal in which they were published (citations/IF). However, both the scientist's gender and the rank of their university are also of importance, suggesting that non-publication features play a statistically significant role in the academic hiring process. Our model (www.pipredictor.com) allows anyone to calculate their likelihood of becoming a PI.
authors.affiliation: Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.
authors.forename: David
authors.initials: D
authors.lastname: van Dijk
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.
authors.forename: Ohad
authors.initials: O
authors.lastname: Manor
authors.affiliation: Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), E-08003 Barcelona, Spain. Electronic address: lucas.carey@upf.edu.
authors.forename: Lucas B
authors.initials: LB
authors.lastname: Carey
mesh_terms: D001185:Artificial Intelligence; D005178:Faculty; D006801:Humans; D055811:Journal Impact Factor; D008962:Models, Theoretical; D011336:Probability; D012108:Research Personnel / Q000706:statistics & numerical data*; D012737:Sex Factors; D014495:Universities / Q000706:statistics & numerical data*