Some scientists are more likely to explore unfamiliar research topics while others tend to exploit existing ones, yet it is unclear how these choices connect to academic performance. This is evidenced by contradictory accounts of correlations between topic switch and performance in previous studies that often rely on single metrics and neglect confounding factors. To disentangle the complexity, we complement the switch frequency metric with switch distances, extensively control for confounding factors, and establish a link between past switch behaviors to future performance. We discover that `conservative adventurers' who switch topics frequently but do so to `close' domains measured by frequency and distance metrics, have notably better future performance than those who behave differently under these two metrics. The behavioral pattern of `conservative adventurers' is uncommon and seems to suggest an effective balance between exploration and exploitation. Moving beyond mere correlations, we ask whether `conservative adventuring' could be a strategy to adopt. While it is almost impossible to know whether any behavioral change in our scenario is an intentional strategy adjustment, it is plausible to assume that the rare ones who drastically change to `conservative adventurers' from the opposite behavior group shift their research strategies on purpose, given the observation that most scientists maintain consistent research agendas. These `drastic changers' experience considerable future improvements. Our findings are based on three datasets covering a total of 31,780,857 papers in biomedicine, physics, and chemistry, from 1800 to 2021, and shed light on understanding and planning of scientific careers, especially for junior scientists.
The APS dataset includes information of all papers published in the Physical Review series of journals between 1893 and 2020---more than 670,000 publications. Each paper is associated with up to five 6-digit PACS codes.
The PubMed dataset, constructed from the Microsoft Academic Graph (MAG) by requiring the URLs of the papers to be from PubMed, contains 13 billion papers from 1800-2021.
The ACS dataset comprises chemical publications published in 86 American Chemical Society journals from 1879 to 2021, and is constructed by requiring MAG that the papers' URLs come from ACS.