68 research outputs found
Quantifying Long-Term Scientific Impact
The lack of predictability of citation-based measures frequently used to
gauge impact, from impact factors to short-term citations, raises a fundamental
question: Is there long-term predictability in citation patterns? Here, we
derive a mechanistic model for the citation dynamics of individual papers,
allowing us to collapse the citation histories of papers from different
journals and disciplines into a single curve, indicating that all papers tend
to follow the same universal temporal pattern. The observed patterns not only
help us uncover basic mechanisms that govern scientific impact but also offer
reliable measures of influence that may have potential policy implications
Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact
Throughout history, a relatively small number of individuals have made a profound and lasting impact on science and society. Despite long-standing, multi-disciplinary interests in understanding careers of elite scientists, there have been limited attempts for a quantitative, career-level analysis. Here, we leverage a comprehensive dataset we assembled, allowing us to trace the entire career histories of nearly all Nobel laureates in physics, chemistry, and physiology or medicine over the past century. We find that, although Nobel laureates were energetic producers from the outset, producing works that garner unusually high impact, their careers before winning the prize follow relatively similar patterns as ordinary scientists, being characterized by hot streaks and increasing reliance on collaborations. We also uncovered notable variations along their careers, often associated with the Nobel prize, including shifting coauthorship structure in the prize-winning work, and a significant but temporary dip in the impact of work they produce after winning the Nobel. Together, these results document quantitative patterns governing the careers of scientific elites, offering an empirical basis for a deeper understanding of the hallmarks of exceptional careers in science
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
An ability to predict the popularity dynamics of individual items within a
complex evolving system has important implications in an array of areas. Here
we propose a generative probabilistic framework using a reinforced Poisson
process to model explicitly the process through which individual items gain
their popularity. This model distinguishes itself from existing models via its
capability of modeling the arrival process of popularity and its remarkable
power at predicting the popularity of individual items. It possesses the
flexibility of applying Bayesian treatment to further improve the predictive
power using a conjugate prior. Extensive experiments on a longitudinal citation
dataset demonstrate that this model consistently outperforms existing
popularity prediction methods.Comment: 8 pages, 5 figure; 3 table
Quantifying the Benefit of Artificial Intelligence for Scientific Research
The ongoing artificial intelligence (AI) revolution has the potential to
change almost every line of work. As AI capabilities continue to improve in
accuracy, robustness, and reach, AI may outperform and even replace human
experts across many valuable tasks. Despite enormous efforts devoted to
understanding AI's impact on labor and the economy and its recent success in
accelerating scientific discovery and progress, we lack a systematic
understanding of how advances in AI may benefit scientific research across
disciplines and fields. Here we develop a measurement framework to estimate
both the direct use of AI and the potential benefit of AI in scientific
research by applying natural language processing techniques to 87.6 million
publications and 7.1 million patents. We find that the use of AI in research
appears widespread throughout the sciences, growing especially rapidly since
2015, and papers that use AI exhibit an impact premium, more likely to be
highly cited both within and outside their disciplines. While almost every
discipline contains some subfields that benefit substantially from AI,
analyzing 4.6 million course syllabi across various educational disciplines, we
find a systematic misalignment between the education of AI and its impact on
research, suggesting the supply of AI talents in scientific disciplines is not
commensurate with AI research demands. Lastly, examining who benefits from AI
within the scientific workforce, we find that disciplines with a higher
proportion of women or black scientists tend to be associated with less
benefit, suggesting that AI's growing impact on research may further exacerbate
existing inequalities in science. As the connection between AI and scientific
research deepens, our findings may have an increasing value, with important
implications for the equity and sustainability of the research enterprise.Comment: 23 pages, 4 figure
Expertise diversity of teams predicts originality and long-term impact in science and technology
Despite the growing importance of teams in producing innovative and
high-impact science and technology, it remains unclear how expertise diversity
among team members relates to the originality and impact of the work they
produce. Here, we develop a new method to quantify the expertise distance of
researchers based on their prior career histories and apply it to 23 million
scientific publications and 4 million patents. We find that across science and
technology, expertise-diverse teams tend to produce work with greater
originality. Teams with more diverse expertise have no significant impact
advantage in the short- (2 years) or mid-term (5 years). Instead, they exhibit
substantially higher long-term impact (10 years), increasingly attracting
larger cross-disciplinary influence. This impact premium of expertise diversity
among team members becomes especially pronounced when other dimensions of team
diversity are missing, as teams within the same institution or country appear
to disproportionately reap the benefits of expertise diversity. While
gender-diverse teams have relatively higher impact on average, teams with
varied levels of gender diversity all seem to benefit from increased expertise
diversity. Given the growing knowledge demands on individual researchers,
implementation of incentives for original research, and the tradeoffs between
short-term and long-term impacts, these results may have implications for
funding, assembling, and retaining teams with originality and long-lasting
impacts.Comment: 31 pages, 5 figure
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