BACKGROUND The increasing availability of digital data on scholarly inputs and outputs—from research funding, productivity, and collaboration to paper citations and scientist mobility—offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales: It provides insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science. In the past decade, SciSci has benefited from an influx of natural, computational, and social scientists who together have developed big data–based capabilities for empirical analysis and generative modeling that capture the unfolding of science, its institutions, and its workforce. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems. ADVANCES Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. Microscopic models have traced the dynamics of citation accumulation, allowing us to predict the future impact of individual papers. SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon. For example, measurements indicate that scholars are risk-averse, preferring to study topics related to their current expertise, which constrains the potential of future discoveries. Those willing to break this pattern engage in riskier careers but become more likely to make major breakthroughs. Overall, the highest-impact science is grounded in conventional combinations of prior work but features unusual combinations. Last, as the locus of research is shifting into teams, SciSci is increasingly focused on the impact of team research, finding that small teams tend to disrupt science and technology with new ideas drawing on older and less prevalent ones. In contrast, large teams tend to develop recent, popular ideas, obtaining high, but often short-lived, impact. OUTLOOK SciSci offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas because it facilitates the identification of fundamental mechanisms responsible for scientific discovery. These interdisciplinary data-driven efforts complement contributions from related fields such as sciento-metrics and the economics and sociology of science. Although SciSci seeks long-standing universal laws and mechanisms that apply across various fields of science, a fundamental challenge going forward is accounting for un...

Research teams are the fundamental social unit of science, and yet there is currently no model that describes their basic property: size. In most fields teams have grown significantly in recent decades. We show that this is partly due to the change in the character of team-size distribution. We explain these changes with a comprehensive yet straightforward model of how teams of different sizes emerge and grow. This model accurately reproduces the evolution of empirical team-size distribution over the period of 50 years. The modeling reveals that there are two modes of knowledge production. The first and more fundamental mode employs relatively small, core teams. Core teams form by a Poisson process and produce a Poisson distribution of team sizes in which larger teams are exceedingly rare. The second mode employs extended teams, which started as core teams, but subsequently accumulated new members proportional to the past productivity of their members. Given time, this mode gives rise to a power-law tail of large teams (10-1000 members), which features in many fields today. Based on this model we construct an analytical functional form that allows the contribution of different modes of authorship to be determined directly from the data and is applicable to any field. The model also offers a solid foundation for studying other social aspects of science, such as productivity and collaboration.Comment: Published in PNAS. Model tested on astronomy. Published version and Supplemental Information available at http://www.pnas.org/cgi/doi/10.1073/pnas.130972311

The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.

Since its creation in 1991, arXiv has become central to the diffusion of research in a number of fields. Combining data from the entirety of arXiv and the Web of Science (WoS), this article investigates (a) the proportion of papers across all disciplines that are on arXiv and the proportion of arXiv papers that are in the WoS, (b) the elapsed time between arXiv submission and journal publication, and (c) the aging characteristics and scientific impact of arXiv e-prints and their published version. It shows that the proportion of WoS papers found on arXiv varies across the specialties of physics and mathematics, and that only a few specialties make extensive use of the repository. Elapsed time between arXiv submission and journal publication has shortened but remains longer in mathematics than in physics. In physics, mathematics, as well as in astronomy and astrophysics, arXiv versions are cited more promptly and decay faster than WoS papers. The arXiv versions of papers-both published and unpublished-have lower citation rates than published papers, although there is almost no difference in the impact of the arXiv versions of published and unpublished papers.

This study comprises a suite of analyses of words in article titles in order to reveal the cognitive structure of Library and Information Science (LIS). The use of title words to elucidate the cognitive structure of LIS has been relatively neglected. The present study addresses this gap by performing (a) co-word analysis and hierarchical clustering, (b) multidimensional scaling, and (c) determination of trends in usage of terms. The study is based on 10,344 articles published between 1988 and 2007 in 16 LIS journals. Methodologically, novel aspects of this study are: (a) its large scale, (b) removal of non-specific title words based on the "word concentration" measure (c) identification of the most frequent terms that include both single words and phrases, and (d) presentation of the relative frequencies of terms using "heatmaps". Conceptually, our analysis reveals that LIS consists of three main branches: the traditionally recognized libraryrelated and information-related branches, plus an equally distinct bibliometrics/scientometrics branch. The three branches focus on: libraries, information, and science, respectively. In addition, our study identifies substructures within each branch. We also tentatively identify "information seeking behavior" as a branch that is establishing itself separate from the three main branches. Furthermore, we find that cognitive concepts in LIS evolve continuously, with no stasis since 1992. The most rapid development occurred between 1998 and 2001, influenced by the increased focus on the Internet. The change in the cognitive landscape is found to be driven by the emergence of new information technologies, and the retirement of old ones.