Science of science
Santo Fortunato,Carl T. Bergstrom,Katy Börner,James A. Evans,Dirk Helbing,Staša Milojević,Alexander M. Petersen,Filippo Radicchi,Roberta Sinatra,Roberta Sinatra,Brian Uzzi,Alessandro Vespignani,Alessandro Vespignani,Ludo Waltman,Dashun Wang,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási +17 more
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TLDR
The Science of Science (SciSci) as discussed by the authors provides a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales, providing 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.Abstract:
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 scientometrics 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 undeniable differences in culture, habits, and preferences between different fields and countries. This variation makes some cross-domain insights difficult to appreciate and associated science policies difficult to implement. The differences among the questions, data, and skills specific to each discipline suggest that further insights can be gained from domain-specific SciSci studies, which model and identify opportunities adapted to the needs of individual research fields.read more
Citations
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Unequal effects of the COVID-19 pandemic on scientists.
Kyle Myers,Wei Yang Tham,Yian Yin,Nina Cohodes,Jerry G. Thursby,Marie Thursby,Marie Thursby,Peter Schiffer,Joseph T. Walsh,Joseph T. Walsh,Karim R. Lakhani,Dashun Wang +11 more
TL;DR: 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 under COVID-19.
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The Diversity-Innovation Paradox in Science
Bas Hofstra,Vivek Kulkarni,Sebastian Munoz-Najar Galvez,Bryan He,Dan Jurafsky,Daniel A. McFarland +5 more
TL;DR: This paper used text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations, and are the innovations of under-represented groups adopted and rewarded?
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Historical comparison of gender inequality in scientific careers across countries and disciplines.
TL;DR: In this paper, a bibliometric analysis of academic publishing careers by reconstructing the complete publication history of over 1.5 million gender-identified authors whose publishing career ended between 1955 and 2010, covering 83 countries and 13 disciplines.
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The preeminence of ethnic diversity in scientific collaboration
TL;DR: In this article, the authors analyzed over 9 million papers and 6 million scientists to study the relationship between research impact and five classes of diversity: ethnicity, discipline, gender, affiliation, and academic age.
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Data-driven modeling and learning in science and engineering
TL;DR: This paper reviews the application of data-driven modeling and model learning procedures to different fields in science and engineering and finds the traditional approach seemed to be highly satisfactory.
References
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