Young scientists don’t have the most impact or make the biggest discoveries
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Shalini Saxena
A scientist’s performance is often judged on
two factors: productivity, as measured through the number of
publications; and impact, as indicated by the number of times those
papers are cited. It's generally thought that impact peaks early in a
scientist's career, while having a mature, established research program
boosts productivity. But little is actually known about how productivity
and impact evolve over time.
In a recent investigation published in Science,
people actually looked at how these measures change throughout the
course of a scientist’s career, finding that our assumptions aren't
always well founded.
In order to understand how performance
measures change over time, the publication profiles of scientists across
multiple disciplines were reconstructed. For each scientist, the number
of publications and impact were recorded. The impact was captured by
the total number of citations 10 years after publication. The most
influential work was identified as the single paper with the most
citations at that time.
The team also grouped the scientists based on
their peak impact, regardless of when it occurred: high maximum impact
(top 5 percent), low maximum impact (bottom 20 percent), and medium
maximum impact (middle 75 percent).
Productivity and impact
The first question tackled was how
productivity changes over time. Productivity, as determined by the
number of publications authored, generally increases during the course
of a scientist’s career. For high-impact scientists, productivity grew
almost threefold. However, for low-impact scientists, productivity
growth was more modest. The team also randomized each scientist's career
by exchanging the impact of all their papers; this let them test
whether trends or timing influenced when the highest-impact work is
done.
Next, the study looked at the times during
which a scientist's most influential publications occurred. For each
scientist, researchers determined the number of years that had passed
between the scientist’s first publication and their highest-impact
paper. This information was used to evaluate the probability that the
highest-impact paper would occur in any given year following the first
publication. Analysis indicates that most scientists publish their
highest-impact paper within the first 20 years of their career; after 20
years that probability drops.
To explore the origin of this pattern,
researchers randomized the order of publications and performed the same
analysis. They found that the impact probability of the fabricated
career is indistinguishable from the original study. This suggests that
the probability is dependent on the year-to-year variations in
productivity through an individual’s career.
Finally, the researchers measured the
probability of the most-cited work coming early or late in the sequence
of published papers. This analysis revealed that impact is randomly
distributed within a scientist’s body of work.
Neither publication time nor publication order
influences this finding. The highest-impact work can occur anywhere in
the sequence of publications with equal probability regardless of
discipline, career length, career time period, number of authors, and
assigned contributions of authors. Further analysis revealed that growth
in average impact during a career in science can be attributed to
growing productivity and that ability or excellence do not influence how
impact evolves over time.
Predicting success
Using this information, the team created a
model that reveals underlying patterns governing the emergence of
scientific success. Since impact varies greatly between scientists, a
unique individual parameter Q was determined for each scientist to
capture this aspect. The Q parameter is stable during an individual’s
career and accurately predicts the evolution of a scientist’s impact,
which comes through factors that include independent recognition and
cumulative citations.
This investigation suggests that it may be
possible to conduct a standardized evaluation of an individual
scientist’s performance in a quantitative manner. This news could be
particularly welcome for academic institutes that rely on measures
like number of publications and number of citations to gauge an
academic’s success. However, we're unsure what impact this predictive
model could have if it is used to judge scientists in their nascent
years.
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