The whys and wherefores of SciSci 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 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. 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. ILLUSTRATION: NICOLE SAMAY 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.

中文翻译：

---

科学的科学

SciSci 的原因和原因 科学科学 (SciSci) 以跨学科方法为基础，该方法使用大数据集来研究开展科学活动的机制——从研究问题的选择到职业轨迹和领域内的进步。在评论中，Fortunato 等人。解释基本原理是，通过对有影响力的科学的先驱有更深入的了解，将有可能制定系统和政策，以提高每位科学家的成功能力并增强整个科学的前景。科学，这个问题 p。eaao0185 背景 关于学术投入和产出的数字数据的可用性日益增加——来自研究资金、生产力、以及对论文引用和科学家流动的合作——为探索科学的结构和演变提供了前所未有的机会。科学科学 (SciSci) 提供了对跨不同地理和时间尺度的科学主体之间相互作用的定量理解：它提供了对创造力和科学发现起源的潜在条件的见解，其最终目标是开发具有加速科学发展的潜力。在过去十年中，SciSci 受益于大量自然科学家、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，可以捕捉科学、机构和劳动力的发展。SciSci 的价值主张是，通过对推动科学成功的因素有更深入的了解，我们可以更有效地解决环境、社会和技术问题。进步 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、论文和思想网络。这种表示揭示了通过研究协作网络和通过研究引文网络发现的影响性发现的路径来表征新科学领域出现的模式。微观模型追踪了引用积累的动态，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面临的选择和权衡。例如，测量表明，学者们是规避风险的，更喜欢研究与其当前专业知识相关的主题，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。总体而言，影响最大的科学基于先前工作的传统组合，但具有不寻常的组合。最后，随着研究中心转移到团队中，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那么流行的想法来颠覆科学和技术。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。展望 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，因为它有助于确定负责科学发现的基本机制。这些跨学科数据驱动的努力补充了相关领域的贡献，如科学计量学、经济学和科学社会学。尽管 SciSci 寻求适用于各个科学领域的长期普遍规律和机制，但未来的一个基本挑战是解决不同领域和国家之间不可否认的文化、习惯和偏好差异。这种变化使得一些跨领域的见解难以理解，相关的科学政策也难以实施。问题、数据、每个学科的特定技能和技能表明，可以从特定领域的 SciSci 研究中获得进一步的见解，这些研究建模和确定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 寻找科学结构和动态背后的普遍和特定领域的规律。插图：NICOLE SAMAY 确定科学的基本驱动力并开发预测模型以捕捉其演变有助于设计可以改善科学事业的政策——例如，通过增强科学家的职业道路，对主持研究、发现的组织进行更好的绩效评估新型有效的融资工具，甚至确定沿科学前沿的有前途的区域。科学的科学使用关于科学生产的大规模数据来搜索通用和特定领域的模式。在这里，我们回顾了这个跨学科领域的最新发展。