Predictive Modeling of the Emergence and Development of Scientific Fields

近几十年来，科学出版的爆炸式增长创造了一种令人尴尬的财富。以比以往任何时候都更容易获得的方式，可以获得更多关于更多主题的更多信息。在PubMed或Web of Science上搜索通常会返回数千个结果。大量出版物的容易获取给研究人员和资助机构都带来了实质性的问题：科学家不可能阅读在他们自己的领域发表的所有文章，政策制定者几乎没有明确的依据来形成质量判断。如果把小麦和谷壳分开的问题每年都呈指数增长，那么如何才能对哪些领域可能发展成为变革性研究做出合理的决定呢？鉴于科研成果的不断涌现，开发一些量化的方法来描述——进而预测——科学研究的增长和发展，就显得比以往任何时候都更为重要。什么因素可以解释特定主题的出版物数量或特定领域的研究人员数量随时间的变化？研究人员之间的什么互动可以解释科学界的成长和变化？各种干预措施——政策举措、市场力量或其他因素——如何改变科学领域出现和发展的动力？智力价值：该研究开发了新的方法来模拟和预测科学研究和创新的过程。这些措施是基于捕捉研究企业各个方面的数学模型。这些工具通过应用于物理和生物医学科学的一系列例子而得到强化。这些例子允许在六个关键领域调查支持和指导科学研究进程的不同模式：来自物理科学的关于弦理论、分子电子学和纳米技术的研究出版物的增长和发展；以及来自生物医学科学的关于H1N1流感、冠状动脉血管成形术和介入心脏病学的研究出版物。这些案例跨越了广泛的尺度，从定义良好的子领域内的研究主题到大型的、学科跨越的努力。更广泛的影响：研究结果的传播包括科学出版物、专家研究人员的研讨会、一个非正式的双周系列研讨会，面向从历史和科学社会学、商业和经济学、图书馆学和计算机科学等广泛学科的学者。此外，还在本科和研究生阶段开设了新课程。最后，这些课程的结构、材料和资源通过麻省理工学院的开放式课程计划在互联网上自由传播。

The explosion of scientific publishing in recent decades has created an embarrassment of riches. More information is available about more subjects in ways that are more accessible than ever before. Searches on PubMed or Web of Science routinely return thousands of hits. The easy accessibility of gigantic numbers of publications creates substantial problems for researchers and granting agencies alike: scientists cannot possibly read everything published in their own fields, and policymakers have few clearbases upon which to form judgments of quality. How can reasonable decisions be made about which fields are likely to blossom into transformative research, if the problem of separating wheat from chaff grows exponentially with each passing year? Given the ever-increasing outpouring, it is more important than ever to develop some quantitative means of describing -- and, in turn, predicting -- the growth and development of scientific research. What factors might explain the changes over time of numbers of publications on a given topic, or numbers of researchers working in a given field? What interactions between researchers might account for a scientific community's growth and change? And how might various interventions -- policy initiatives, market forces, or other factors -- alter the dynamics of how scientific fields emerge and develop?Intellectual Merit: The research develops new measures to model and predict processes of scientific research and innovation. The measures are based on mathematical models that capture various aspects of the research enterprise. These tools are sharpened with application to a suite of examples from the physical and biomedical sciences. These examples permit the investigation of different modes of supporting and steering the course of scientific research in six key areas: the growth and development of research publications on string theory, molecular electronics, and nanotechnology from the physical sciences; and research publications on H1N1 influenza, coronary angioplasty, and interventional cardiology from the biomedical sciences. These cases traverse a wide range of scales, from research topics within well-defined subfields to large, discipline-spanning endeavors.Broader Impact: The dissemination of the results includes scientific publications, workshops with expert researchers, an informal bi-weekly seminar series for scholars across a wide range of disciplines - from history and sociology of science, to business and economics, to library science and computer science. In addition, new courses are offered at the undergraduate and graduate levels. Finally, the structure, materials, and resources for these courses are freely disseminated via the internet via MIT's OpenCourseWare initiative.