Search

An analysis of millions of scientific papers and patents reveals a ‘pivot penalty’ when researchers shift direction, with the impact of studies decreasing rapidly the further they move from their previous work.

Artificial-intelligence algorithms could identify scientists who need support with translating their work into real-world applications and more. Leaders must step up.

Gao and Wang develop a measurement framework that demonstrates the widespread use and benefits of AI in science. Nonetheless, there is a substantial gap between AI education and application across disciplines.

Studying production, funding and consumption of science, the authors find a strong alignment between what the public consumes and what is impactful, as well as an alignment between funding and collective public use.

Little is known about the long-term effects of early-career setback. Here, the authors compare junior scientists who were awarded a NIH grant to those with similar track records, who were not, and find that individuals with the early setback systematically performed better in the longer term.

A model demonstrates that people who eventually succeed and those who do not may initially appear similar, but are characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality of each subsequent attempt to succeed.

Despite their ubiquitous nature across a wide range of creative domains, it remains unclear if there is any regularity underlying the beginning of successful periods in a career. Here, the authors develop computational methods to trace the career outputs of artists, film directors, and scientists and explore how they move in their creative space along their career trajectory.

Jin et al. find that early growth patterns in substitutive systems follow power laws rather than exponentials. Big data analyses reveal key mechanisms governing substitutions, helping to explain the observed power-law early growth.

Data show that apart from their prize-winning work, the careers of Nobel laureates follow the same patterns as those of the majority of scientists.

Using a large-scale analysis of publication records and a random-walk model, Jia and colleagues show that the evolution of scientists’ research interests throughout their careers is characterized by a regular and reproducible pattern.

COVID-19 has not affected all scientists equally. A survey of principal investigators indicates that female scientists, those in the ‘bench sciences’ and, especially, scientists with young children experienced a substantial decline in time devoted to research. This could have important short- and longer-term effects on their careers, which institution leaders and funders need to address carefully.

The metaverse can improve the accessibility of scientific laboratories and meetings, aid in reproducibility efforts and provide new opportunities for experimental design. But researchers and research institutions must plan ahead and be ready to mitigate potential harms.

The likelihood of linking within a complex network is of importance to solve real-world problems, but it is challenging to predict. Sun et al. show that the link predictability limit can be well estimated by measuring the shortest compression length of a network without a need of prediction algorithm.

An analysis of Web of Science data spanning more than 100 years reveals the rapid growth and increasing multidisciplinarity of physics — as well its internal map of subdisciplines.

The career trajectories of around 30,000 artists, film directors and scientists show that individuals in each ___domain have ‘hot streaks’ during which their works have increased impact, despite showing no increase in productivity.

A bibliometric analysis of the past and present of AI research suggests a consolidation of research influence. This may present challenges for the exchange of ideas between AI and the social sciences.

Liu and coauthors review the major data sources, measures and analysis methods in the science of science, discussing how recent developments in these fields can help researchers to better predict science-making outcomes and design better science policies.

The pandemic has caused disruption to many aspects of scientific research. In this Comment the authors describe the findings from surveys of scientists between April 2020 and January 2021, which suggests there was a decline in new projects started in that time.

Success is being increasingly studied as a collective phenomenon. Here the authors review how this perspective has deepened our understanding of success, uncovering regularities and predictive signals across diverse domains, as well as biases that challenge meritocratic views of success.

Analyses of the output produced by large versus small teams of researchers and innovators demonstrate that their work differs systematically in the extent to which it disrupts or develops existing science and technology.

An analysis of the number of physicists and their career paths reveals the changing landscape of the physics subdisciplines, highlighting the connections between different fields and the effects of large collaborations.