Despite persistent efforts in revealing the temporal patterns in scientific
careers, little attention has been paid to the spatial patterns of scientific
activities in the knowledge space. Here, drawing on millions of papers in six
disciplines, we consider scientists' publication sequence as "walks" on the
quantifiable epistemic landscape constructed from large-scale bibliometric
corpora by combining embedding and manifold learning algorithms, aiming to
reveal the individual research topic dynamics and association between research
radius with academic performance, along their careers. Intuitively, the
visualization shows the localized and bounded nature of mobile trajectories. We
further find that the distributions of scientists' transition radius and
transition pace are both left-skewed compared with the results of controlled
experiments. Then, we observe the mixed exploration and exploitation pattern
and the corresponding strategic trade-off in the research transition, where
scientists both deepen their previous research with frequency bias and explore
new research with knowledge proximity bias. We further develop a bounded
exploration-exploitation (BEE) model to reproduce the observed patterns.
Moreover, the association between scientists' research radius and academic
performance shows that extensive exploration will not lead to a sustained
increase in academic output but a decrease in impact. In addition, we also note
that disruptive findings are more derived from an extensive transition, whereas
there is a saturation in this association. Our study contributes to the
comprehension of the mobility patterns of scientists in the knowledge space,
thereby providing significant implications for the development of scientific
policy-making.Comment: article paper, 47 pages, 29 figures, 4 table