EAGER: Identifying Emergent Opportunities in Science

EAGER：识别科学中的新兴机会

• 批准号: 1142795
• 负责人: Kevin Boyack
• 金额: $ 20万
• 依托单位: SciTech Strategies, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1142795&HistoricalAwards=false
• 关键词: EAGER; Identifying; Emergent; Opportunities; Science

This exploratory project (EMERG) is aimed at developing the capability to predict emerging topics in science from a highly detailed global model of the scientific literature. Identification of emergent opportunities in science is a central issue in academia and practice. Applications range from a simple understanding of the broader context in which individual research is conducted to the direction of research funds toward emerging topics. Previous studies of emergence have had the following shortcomings: they are retrospective (the area of emergence is identified after the fact), narrowly defined (lacking the context of related scientific topics) and/or highly aggregated (field level rather than topic level).The approach is based on a highly detailed global model of science, consisting of hundreds of thousands of micro-communities over a period of nine years (2000-2008). The average size of these micro-communities is 15 papers per year. Micro-communities are linked from year to year using co-citation methods. Some micro-communities are part of long thread-like structures while others may be isolated. At the micro-structure level, science appears to have a high level of discontinuity. The mixture of continuity and discontinuity makes it possible to see emergence at the topical level. A variety of indicators, some structural, some based on the micro-community contents (articles, authors, ages, etc.), and some based on full text analysis, are calculated for each micro-community. The hypothesis is that several, if not all, of the proposed indicators will correlate with emergence. To test this hypothesis, a data from research funding agencies and foundations that identified emergent micro-communities will be collected, together with identifying and tracking the key articles responsible for emergence in those areas. This history will be compared with the results of indicators from the model of science. If successful, the indicators can be applied to a current (rather than retrospective) model, suggesting the particular current micro-communities in science that are emerging or that are likely to emerge in the next year or two.This project provides a completely new method for developing useful knowledge about the micro-structure and dynamics of science and technology from literature databases, whether of scientific literature, patent, grant, or web resources. This work has the potential to transform the way the structure and dynamics of science and technology are understood, and to impact conduct and management of research at the scientists, students, general public and policy maker levels. Project results will be disseminated via web site (http://www.mapofscience.com/emerg.html) and publications.

这一探索性项目(Emerg)旨在发展从高度详细的全球科学文献模型预测科学新主题的能力。识别科学中的新兴机会是学术界和实践界的一个中心问题。应用范围从对个人研究进行的更广泛背景的简单理解，到研究基金对新兴主题的指导。以前关于涌现的研究有以下缺点：它们是回溯性的(涌现的领域是在事实发生后确定的)、狭隘的定义(缺乏相关科学主题的背景)和/或高度聚集的(领域一级而不是专题一级)。该方法基于一个非常详细的全球科学模型，在九年(2000-2008年)期间由数十万个微型社区组成。这些微型社区的平均规模为每年15篇论文。微型社区年复一年地通过共同引用的方法联系在一起。一些微型群落是长线状结构的一部分，而另一些则可能是孤立的。在微观结构层面，科学似乎有很高的间断性。连续性和间断性的混合使得在主题层面上看到涌现成为可能。对于每个微社区，计算了各种指标，有些是结构性的，有些是基于微社区的内容(文章、作者、年龄等)，还有一些是基于全文分析。假设是，拟议的指标中有几个(如果不是所有的话)将与出现相关。为了验证这一假设，将收集来自研究资助机构和基金会的数据，确定新兴微型社区，并确定和跟踪导致这些领域出现的关键文章。这段历史将与科学模型中的指标结果进行比较。如果成功，这些指标可以应用于当前(而不是追溯)模型，建议正在出现或可能在未来一两年内出现的特定当前科学微社区。该项目提供了一种全新的方法，用于从文献数据库中开发关于科学和技术微观结构和动态的有用知识，无论是科学文献、专利、拨款还是网络资源。这项工作有可能改变人们对科学技术的结构和动态的理解方式，并对科学家、学生、普通公众和政策制定者层面的研究进行和管理产生影响。项目成果将通过网站(http://www.mapofscience.com/emerg.html)和出版物)传播。