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ORCID: 0000-0003-3018-4544

Most representative publications


Disconnects between science and policy are a long-standing concern. Yet, our systematic understanding of the use of science in policy remains limited, partly because of the difficulty in reliably tracing the coevolution of policy and science at a large, global scale. Today, the world faces a common emergency in the COVID-19 pandemic, which presents a dynamic, uncertain, yet extraordinarily consequential policy environment across the globe. We combined two large-scale databases that capture policy and science and their interactions, allowing us to examine the coevolution of policy and science.

Our analysis suggests that many policy documents in the COVID-19 pandemic substantially access recent, peer-reviewed, and high-impact science. And policy documents that cite science are especially highly cited within the policy domain. At the same time, there is a heterogeneity in the use of science across policy-making institutions. The tendency for policy documents to cite science appears mostly concentrated within intergovernmental organizations (IGOs), such as the World Health Organization (WHO), and much less so in national governments, which consume science largely indirectly through the IGOs. This close coevolution between policy and science offers a useful indication that a key link is operating, but it has not been a sufficient condition for effectiveness in containing the pandemic.


Human achievements are often preceded by repeated attempts that fail, but little is known about the mechanisms that govern the dynamics of failure. Here, building on previous research relating to innovation, human dynamics and learning, we develop a simple one-parameter model that mimics how successful future attempts build on past efforts. Solving this model analytically suggests that a phase transition separates the dynamics of failure into regions of progression or stagnation and predicts that, near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Above the critical point, agents exploit incremental refinements to systematically advance towards success, whereas below it, they explore disjoint opportunities without a pattern of improvement. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may initially appear similar, but can be characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality associated with each subsequent attempt.

We collected large-scale data from three disparate domains and traced repeated attempts by investigators to obtain National Institutes of Health (NIH) grants to fund their research, innovators to successfully exit their startup ventures, and terrorist organizations to claim casualties in violent attacks. We find broadly consistent empirical support across all three domains, which systematically verifies each prediction of our model. Together, our findings unveil identifiable yet previously unknown early signals that enable us to identify failure dynamics that will lead ultimately to success or failure. Given the ubiquitous nature of failure and the paucity of quantitative approaches to understand it, these results represent an initial step towards the deeper understanding of the complex dynamics underlying failure.


The COVID-19 pandemic has undoubtedly disrupted the scientific enterprise. Policymakers and institutional leaders have already begun to respond to mitigate the impacts of the pandemic on researchers. However, we lack evidence on the nature and magnitude of the disruptions scientists are experiencing. To gain some insight into the extent of disruptions scientists are experiencing, we conducted a preliminary survey, which was distributed on 13 April 2020, approximately 1 month after the World Health Organization declared COVID-19 a pandemic. We reached out to US- and Europe-based scientists across a wide range of institutions, career stages and demographic backgrounds. Within a week, we received full responses from 4,535 faculty or Principal Investigators.

Results of the survey highlight the sizable and heterogeneous ways the COVID-19 pandemic is affecting the scientific workforce. Scientists report a sharp decline in time spent on research on average, but there is substantial heterogeneity with a significant share reporting no changes or even increases. Some of this heterogeneity is due to field-specific differences, and some is due to gender. However, the largest disruptions are connected to a usually unobserved dimension: childcare, which can account for a significant fraction of gender differences. Amidst scarce evidence about the role of parenting in scientists’ work, these results could have important short- and longer-term effects on their careers, which institution leaders and funders need to address carefully.


Quantitative studies of Nobel laureates’ careers have predominantly focused on the prize-winning work alone. To test if there are indeed systematic differences between the careers of Nobel laureates and ordinary scientists, we studied a unique dataset of entire career histories for nearly all Nobel laureates in physics, chemistry and physiology or medicine from 1900 to 2016 (545 out of 590 laureates, 92.4%).

By testing the burst of most-cited papers and dynamics of team sizes, we find after removing the prize-winning papers, the career of Nobel laureates and ordinary scientists follow the same patterns. Together, our analysis show that apart from their prize-winning work, the careers of Nobel laureates follow the same patterns as those of the majority of scientists.

Other publications

  • Scientific elite revisited: patterns of productivity, collaboration, authorship and impact
    Jichao Li, Yian Yin, Santo Fortunato and Dashun Wang
    Journal of the Royal Society Interface, 17(165): 20200135, Apr 2020.

  • The time dimension of science: Connecting the past to the future
    Yian Yin and Dashun Wang
    Journal of Informetrics, 11(2): 608–621, May 2017.