Science of science.
Science of science.
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DOI：
10.1126/science.aao0185
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发表时间：
2018-03-02
期刊：
影响因子：
--
通讯作者：
Barabási AL
中科院分区：
文献类型：
--
作者：
Fortunato S;Bergstrom CT;Börner K;Evans JA;Helbing D;Milojević S;Petersen AM;Radicchi F;Sinatra R;Uzzi B;Vespignani A;Waltman L;Wang D;Barabási AL
学术投入和产出的数字数据越来越多的可用性从研究资金，生产力和合作到论文引用和科学家流动性为探索科学的结构和演变提供了前所未有的机会。科学的科学（SciSci）提供了跨不同的地理和时间尺度的科学代理人之间的相互作用的定量理解：它提供了创造力和科学发现的起源的基础条件的见解，与开发有可能加速科学的工具和政策的最终目标。在过去的十年中，SciSci受益于自然科学家，计算科学家和社会科学家的涌入，他们共同开发了基于大数据的实证分析和生成建模能力，这些能力捕捉了科学，其机构和劳动力的发展。SciSci的价值主张是，随着对推动科学成功的因素的深入了解，我们可以更有效地解决环境，社会和技术问题。科学可以被描述为一个复杂的，自组织的，不断发展的学者，项目，论文和想法的网络。这种表示揭示了通过研究协作网络来表征新科学领域出现的模式，以及通过研究引用网络来揭示有影响力的发现的路径。微观模型追踪了引文积累的动态，使我们能够预测个别论文的未来影响。SciSci揭示了科学家在推进自己的职业生涯和科学视野时所面临的选择和权衡。例如，测量结果表明，学者们不愿承担风险，更喜欢研究与他们目前的专业知识有关的主题，这限制了未来发现的潜力。那些愿意打破这种模式的人从事风险更高的职业，但更有可能取得重大突破。总的来说，最具影响力的科学是建立在先前工作的传统组合基础上的，但具有不寻常的组合。最后，随着研究的重心转移到团队，SciSci越来越关注团队研究的影响，发现小团队往往会利用旧的和不太流行的新想法来破坏科学和技术。相比之下，大型团队倾向于开发最近流行的想法，获得高，但往往是短暂的，影响。SciSci提供了对科学家，机构和思想之间关系结构的深入定量理解，因为它有助于识别负责科学发现的基本机制。这些跨学科的数据驱动的努力补充了相关领域的贡献，如科学计量学和科学的经济学和社会学。尽管SciSci寻求适用于各个科学领域的长期普遍规律和机制，但未来的一个根本挑战是解释不同领域和国家之间在文化，习惯和偏好方面不可否认的差异。这种差异使得一些跨领域的见解难以理解，相关的科学政策难以实施。每个学科特有的问题，数据和技能之间的差异表明，可以从特定领域的SciSci研究中获得进一步的见解，这些研究模型和识别适应各个研究领域需求的机会。科学的复杂性。科学可以被视为一个不断扩大和发展的思想、学者和论文网络。SciSci寻找科学结构和动力学的普遍和特定领域的规律。
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. 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. 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 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. The complexity of science. Science can be seen as an expanding and evolving network of ideas, scholars and papers. SciSci searches for universal and domain-specific laws underlying the structure and dynamics of science.
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影响因子：
13.6
作者：
Doria Arrieta OA;Pammolli F;Petersen AM
通讯作者：
Petersen AM
影响因子：
13.6
作者：
Clauset A;Arbesman S;Larremore DB
通讯作者：
Larremore DB
DOI：
10.1177/053901847501400602
发表时间：
1975-01-01
影响因子：
1.1
作者：
BOURDIEU, P
通讯作者：
BOURDIEU, P
影响因子：
3.7
作者：
Bettencourt, Luis M. A.;Kaiser, David I.;Kaur, Jasleen
通讯作者：
Kaur, Jasleen
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R