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 prevalence1,2,3. Increases in team size have been attributed to the specialization of scientific activities3, improvements in communication technology4,5, or the complexity of modern problems that require interdisciplinary solutions6,7,8. 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. Here we analyse more than 65 million papers, patents and software products that span the period 1954–2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast, contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future—if at all. Observed differences between small and large teams are magnified for higher-impact work, with small teams known for disruptive work and large teams for developing work. Differences in topic and research design account for a small part of the relationship between team size and disruption; most of the effect occurs at the level of the individual, as people move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, and suggest that, to achieve this, science policies should aim to support a diversity of team sizes.
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Papers and Patents are becoming Less Disruptive over Time
Theories of scientific and technological change view discovery and invention as endogenous
processes1,2, wherein prior accumulated knowledge enables future progress by allowing researchers to, in Newton’s
words, “stand on the shoulders of giants”3–7. Recent decades have witnessed exponential growth in the
volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major
advances8,9. Yet contrary to this view, studies suggest that progress is slowing in several major fields10,11.
Here, we analyze these claims at scale across 6 decades, using data on 45 million papers and 3.9 million patents
from 6 large-scale datasets, together with a novel quantitative metric—the CD index12—that characterizes how
papers and patents change networks of citations in science and technology. We find that papers and patents are
increasingly less likely to break with the past in ways that push science and technology in new directions. This
pattern holds universally across fields and is robust across multiple different citation- and text-based metrics.
Subsequently, we link this decline in disruptiveness to a narrowing in the use of prior knowledge, allowing us
to reconcile the patterns we observe with the “shoulders of giants” view. We find that the observed declines are
unlikely to be driven by changes in the quality of published science, citation practices, or field-specific factors.
Overall, our results suggest that slowing rates of disruption may reflect a fundamental shift in the nature of
science and technology.
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- Award ID(s):
- 1829302
- NSF-PAR ID:
- 10382242
- Editor(s):
- Sutherland, Mary Elizabeth
- Date Published:
- Journal Name:
- Nature
- Volume:
- 613
- ISSN:
- 0028-0836
- Page Range / eLocation ID:
- 138-144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Technological innovation is a dynamic process that spans the lifecycle of an idea, from scientific research to production. Within this process, there are few key innovations that significantly impact a technology’s development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this paper, we present a framework for identifying the technology’s main evolutionary pathway of a technology. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology’s development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Last, we apply integrated approaches of main path analysis (MPA) -- namely global main path analysis and global key-route main analysis -- for extracting technological trajectories at different technological stages. We illustrate this approach with Dye-Sensitized Solar Cells (DSSCs), a low-cost solar cell belonging to the group of thin film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision-makers manage the technological stages of their innovation processes more effectively.more » « less
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