Collective dynamics behind success.

Understanding the collective dynamics behind the success of ideas, products, behaviors, and social actors is critical for decision-making across diverse contexts, including hiring, funding, career choices, and the design of interventions for social change. Methodological advances and the increasing availability of big data now allow for a broader and deeper understanding of the key facets of success. Recent studies unveil regularities beneath the collective dynamics of success, pinpoint underlying mechanisms, and even enable predictions of success across diverse domains, including science, technology, business, and the arts.

InnovationInsights: A Visual Analytics Approach for Understanding the Dual Frontiers of Science and Technology.

Science has long been viewed as a key driver of economic growth and rising standards of living. Knowledge about how scientific advances support marketplace inventions is therefore essential for understanding the role of science in propelling real-world applications and technological progress. The increasing availability of large-scale datasets tracing scientific publications and patented inventions and the complex interactions among them offers us new opportunities to explore the evolving dual frontiers of science and technology at an unprecedented level of scale and detail.

Identify hidden spreaders of pandemic over contact tracing networks.

The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Due to the continuous emergence of new virus variants, even if vaccines have been widely used, the detection of asymptomatic infected persons is still important in the epidemic control.

Data, measurement and empirical methods in the science of science.

The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits.

SciSciNet: A large-scale open data lake for the science of science research.

The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets.

Weak ties, failed tries, and success.

A large-scale study provides a causal test for a cornerstone of social science.

Public use and public funding of science.

Knowledge of how science is consumed in public domains is essential for understanding the role of science in human society. Here we examine public use and public funding of science by linking tens of millions of scientific publications from all scientific fields to their upstream funding support and downstream public uses across three public domains-government documents, news media and marketplace invention. We find that different public domains draw from various scientific fields in specialized ways, showing diverse patterns of use.

Potentially long-lasting effects of the pandemic on scientists.

Two surveys of principal investigators conducted between April 2020 and January 2021 reveal that while the COVID-19 pandemic’s initial impacts on scientists’ research time seem alleviated, there has been a decline in the rate of initiating new projects. This dimension of impact disproportionately affects female scientists and those with young children and appears to be homogeneous across fields. These findings may have implications for understanding the long-term effects of the pandemic on scientific research.

Understanding the onset of hot streaks across artistic, cultural, and scientific careers.

Across a range of creative domains, individual careers are characterized by hot streaks, which are bursts of high-impact works clustered together in close succession. Yet it remains unclear if there are any regularities underlying the beginning of hot streaks. Here, we analyze career histories of artists, film directors, and scientists, and develop deep learning and network science methods to build high-dimensional representations of their creative outputs.

Author Correction: Quantifying the dynamics of failure across science, startups and security.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Revealing the predictability of intrinsic structure in complex networks.

Structure prediction is an important and widely studied problem in network science and machine learning, finding its applications in various fields. Despite the significant progress in prediction algorithms, the fundamental predictability of structures remains unclear, as networks' complex underlying formation dynamics are usually unobserved or difficult to describe. As such, there has been a lack of theoretical guidance on the practical development of algorithms for their absolute performances.

Quantifying the dynamics of failure across science, startups and security.

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.

Early-career setback and future career impact.

Setbacks are an integral part of a scientific career, yet little is known about their long-term effects. Here we examine junior scientists applying for National Institutes of Health R01 grants. By focusing on proposals fell just below and just above the funding threshold, we compare near-miss with narrow-win applicants, and find that an early-career setback has powerful, opposing effects.

Emergence of scaling in complex substitutive systems.

Diffusion processes are central to human interactions. One common prediction of the current modelling frameworks is that initial spreading dynamics follow exponential growth. Here we find that, for subjects ranging from mobile handsets to automobiles and from smartphone apps to scientific fields, early growth patterns follow a power law with non-integer exponents.

A dataset of publication records for Nobel laureates.

A central question in the science of science concerns how to develop a quantitative understanding of the evolution and impact of individual careers. Over the course of history, a relatively small fraction of individuals have made disproportionate, profound, and lasting impacts on science and society. Despite a long-standing interest in the careers of scientific elites across diverse disciplines, it remains difficult to collect large-scale career histories that could serve as training sets for systematic empirical and theoretical studies.

Toward understanding the impact of artificial intelligence on labor.

Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change.

Large teams develop and small teams disrupt science and technology.

One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence. Increases in team size have been attributed to the specialization of scientific activities, improvements in communication technology, or the complexity of modern problems that require interdisciplinary solutions. This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams.