Quantifying patterns of research-interest evolution

Abstract

2Our quantitative understanding of how scientists choose and shift their research focus over time is highly consequential, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded [1][2][3][4][5][6][7][8][9]. Despite extensive investigations of various factors that influence a scientist's choice of research topics [8][9][10][11][12][13][14][15][16][17][18][19][20][21], quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of publication records, finding that research interest change follows a reproducible pattern characterized by an exponential distribution. We identify three fundamental features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research interest evolution [5,22]. We develop a random walk based model, allowing us to accurately reproduce the empirical observations. This work presents a quantitative analysis of macroscopic patterns governing research interest change, discovering a high degree of regularity underlying scientific research and individual careers."The essential tension" hypothesis set forth by Thomas Kuhn [5] has vividly highlighted the conflicting demands of scientific careers that require both exploration and exploitation [4,8,22].Indeed, career advancement, from promotion to obtaining grants, demands a steady stream of publications, which is often achieved through uninterrupted yet incremental contributions to existing, established research agenda. In contrast, frequent changes in research topics invite risk of failure and decreased productivity. The disciplinary boundaries, arising from such factors as implicit culture, tacit and accumulated knowledge [23,24] and peer recognition [3,25], together with intensifying specialization in science and engineering disciplines [26], make radical shifts, such as moving from chemical biology to high energy physics, extremely unlikely, if at all possible. On the other hand, although steady and focused research portfolio helps scientists stay productive, it potentially undermines chances for originality [8]. Indeed, innovative and novel insights often emerge from encountering new challenges and opportunities associated with venturing into new topics and/or incorporating them into existing research agenda [4,15,20,27,28].Given the broad impact on individual careers and strong implications for science and innovation policy, there is an urgent need for quantitative approaches to understanding the nature of research interest change undertaken by individual scientists throughout their careers. This becomes ever more so with the accelerating scale and complexity of scientific enterprise [2,26,29,30]. A variety of microscopic factors have been identified that drive a scientist's choice of research problems, 3 ranging from age...

Explore with caution: mapping the evolution of scientific interest in physics

“…In a nutshell, our results, even if largely in line with previous research, depict a more nuanced portrait of the evolution of research interests than previously thought [2,5,37,38]. Taking into account the first and second levels of the PACS classification we demonstrated that physicists indeed explore during their career but only in the proximity of their initial research topic.…”

“…These observations are in line with previous work done with different measures and metrics [37,38]. However, they are in contrast with the work done by Foster et al [2] and Jia et al [5]. The first group focused on a different research area (Biomedical Chemistry) and studied 133 awardees of scientific prizes.…”

“…They found that i) the statistical features of such networks are quite stationary across time ii) there is an overall increase in connectivity between different fields iii) the unfolding of such increase is hierarchical (closer topics get connected first than far ones) iv) the networks are dominated by topics belonging to subfields of Condense Matter and General Physics, and v) there is an increase in the importance of Interdisciplinary Physics. Jia et al [5] also studied the APS dataset focusing on PACS. However, they considered the evolution of interest between topics in the careers of single authors.…”

Science of science

“…Scientific awards and accolades appear to function as primary incentives to resist conservative tendencies and encourage betting on exploration and surprise (3). Despite the many factors shaping what scientists work on next, macroscopic patterns that govern changes in research interests along scientific careers are highly reproducible, documenting a high degree of regularity underlying scientific research and individual careers (14). …”

Collective iteration behavior for online social networks

“…We only consider one possible preferential interaction, not comparing with the models such as random-walk-based model [47,48] and structural-based network model [41,42], and the properties of statistical physics still need to be deeply discussed. Besides, it also should be examined the effect of Dunbar number to social signature.…”