Explore the vast range of titles available.

Novelty is an essential feature of creative ideas, yet the building blocks of new ideas are often embodied in existing knowledge. From this perspective, balancing atypical knowledge with conventional knowledge may be critical to the link between innovativeness and impact. Our analysis of 17.9 million papers spanning all scientific fields suggests that science follows a nearly universal pattern: The highest-impact science is primarily grounded in exceptionally conventional combinations of prior work yet simultaneously features an intrusion of unusual combinations. Papers of this type were twice as likely to be highly cited works. Novel combinations of prior work are rare, yet teams are 37.7% more likely than solo authors to insert novel combinations into familiar knowledge domains.

Einstein believed that mentors are especially influential in a protégé’s intellectual development, yet the link between mentorship and protégé success remains a mystery. We marshaled genealogical data on nearly 40,000 scientists who published 1,167,518 papers in biomedicine, chemistry, math, or physics between 1960 and 2017 to investigate the relationship between mentorship and protégé achievement. In our data, we find groupings of mentors with similar records and reputations who attracted protégés of similar talents and expected levels of professional success. However, each grouping has an exception: One mentor has an additional hidden capability that can be mentored to their protégés. They display skill in creating and communicating prizewinning research. Because the mentor’s ability for creating and communicating celebrated research existed before the prize’s conferment, protégés of future prizewinning mentors can be uniquely exposed to mentorship for conducting celebrated research. Our models explain 34–44% of the variance in protégé success and reveals three main findings. First, mentorship strongly predicts protégé success across diverse disciplines. Mentorship is associated with a 2×-to-4× rise in a protégé’s likelihood of prizewinning, National Academy of Science (NAS) induction, or superstardom relative to matched protégés. Second, mentorship is significantly associated with an increase in the probability of protégés pioneering their own research topics and being midcareer late bloomers. Third, contrary to conventional thought, protégés do not succeed most by following their mentors’ research topics but by studying original topics and coauthoring no more than a small fraction of papers with their mentors.

Science’s changing demographics raise new questions about research team diversity and research outcomes. We study mixed-gender research teams, examining 6.6 million papers published across the medical sciences since 2000 and establishing several core findings. First, the fraction of publications by mixed-gender teams has grown rapidly, yet mixed-gender teams continue to be underrepresented compared to the expectations of a null model. Second, despite their underrepresentation, the publications of mixed-gender teams are substantially more novel and impactful than the publications of same-gender teams of equivalent size. Third, the greater the gender balance on a team, the better the team scores on these performance measures. Fourth, these patterns generalize across medical subfields. Finally, the novelty and impact advantages seen with mixed-gender teams persist when considering numerous controls and potential related features, including fixed effects for the individual researchers, team structures, and network positioning, suggesting that a team’s gender balance is an underrecognized yet powerful correlate of novel and impactful scientific discoveries.

Replicability tests of scientific papers show that the majority of papers fail replication. Moreover, failed papers circulate through the literature as quickly as replicating papers. This dynamic weakens the literature, raises research costs, and demonstrates the need for new approaches for estimating a study’s replicability. Here, we trained an artificial intelligence model to estimate a paper’s replicability using ground truth data on studies that had passed or failed manual replication tests, and then tested the model’s generalizability on an extensive set of out-of-sample studies. The model predicts replicability better than the base rate of reviewers and comparably as well as prediction markets, the best present-day method for predicting replicability. In out-of-sample tests on manually replicated papers from diverse disciplines and methods, the model had strong accuracy levels of 0.65 to 0.78. Exploring the reasons behind the model’s predictions, we found no evidence for bias based on topics, journals, disciplines, base rates of failure, persuasion words, or novelty words like “remarkable” or “unexpected.” We did find that the model’s accuracy is higher when trained on a paper’s text rather than its reported statistics and that n-grams, higher order word combinations that humans have difficulty processing, correlate with replication. We discuss how combining human and machine intelligence can raise confidence in research, provide research self-assessment techniques, and create methods that are scalable and efficient enough to review the ever-growing numbers of publications—a task that entails extensive human resources to accomplish with prediction markets and manual replication alone.

Fast growing scientific topics have famously been key harbingers of the new frontiers of science, yet, large-scale analyses of their genesis and impact are rare. We investigated one possible factor connected with a topic’s extraordinary growth: scientific prizes. Our longitudinal analysis of nearly all recognized prizes worldwide and over 11,000 scientific topics from 19 disciplines indicates that topics associated with a scientific prize experience extraordinary growth in productivity, impact, and new entrants. Relative to matched non-prizewinning topics, prizewinning topics produce 40% more papers and 33% more citations, retain 55% more scientists, and gain 37 and 47% more new entrants and star scientists, respectively, in the first five-to-ten years after the prize. Funding do not account for a prizewinning topic’s growth. Rather, growth is positively related to the degree to which the prize is discipline-specific, conferred for recent research, or has prize money. These findings reveal new dynamics behind scientific innovation and investment. Scientific revolutions have famously inspired scientists and innovation but large-scale analyses of scientific revolutions in modern science are rare. Here, the authors investigate one possible factor connected with a topic’s extraordinary growth—scientific prizes.

Small world networks have received disproportionate notice in diverse fields because of their suspected effect on system dynamics. The authors analyzed the small world network of the creative artists who made Broadway musicals from 1945 to 1989. Using original arguments, new statistical methods, and tests of construct validity, they found that the varying 'small world' properties of the systemic-level network of these artists affected their creativity in terms of the financial and artistic performance of the musicals they produced. The small world network effect was parabolic; performance increased up to a threshold, after which point the positive effects reversed. Reprinted by permission of the University of Chicago Press. © All rights reserved

The science of science (SciSci) is based on a transdisciplinary approach that uses large data sets to study the mechanisms underlying the doing of science—from the choice of a research problem to career trajectories and progress within a field. In a Review, Fortunato et al. explain that the underlying rationale is that with a deeper understanding of the precursors of impactful science, it will be possible to develop systems and policies that improve each scientist's ability to succeed and enhance the prospects of science as a whole. Science , this issue p. eaao0185 Identifying fundamental drivers of science and developing predictive models to capture its evolution are instrumental for the design of policies that can improve the scientific enterprise—for example, through enhanced career paths for scientists, better performance evaluation for organizations hosting research, discovery of novel effective funding vehicles, and even identification of promising regions along the scientific frontier. The science of science uses large-scale data on the production of science to search for universal and domain-specific patterns. Here, we review recent developments in this transdisciplinary field.

We have used 19.9 million papers over 5 decades and 2.1 million patents to demonstrate that teams increasingly dominate solo authors in the production of knowledge. Research is increasingly done in teams across nearly all fields. Teams typically produce more frequently cited research than individuals do, and this advantage has been increasing over time. Teams now also produce the exceptionally high-impact research, even where that distinction was once the domain of solo authors. These results are detailed for sciences and engineering, social sciences, arts and humanities, and patents, suggesting that the process of knowledge creation has fundamentally changed.

Users online tend to select information that support and adhere their beliefs, and to form polarized groups sharing the same view-e.g. echo chambers. Algorithms for content promotion may favour this phenomenon, by accounting for users preferences and thus limiting the exposure to unsolicited contents. To shade light on this question, we perform a comparative study on how same contents (videos) are consumed on different online social media-i.e. Facebook and YouTube-over a sample of 12M of users. Our findings show that content drives the emergence of echo chambers on both platforms. Moreover, we show that the users' commenting patterns are accurate predictors for the formation of echo-chambers.