TLS- Measuring and Tracking Research Knowledge Integration

TLS-测量和跟踪研究知识整合

• 批准号: 0830207
• 负责人: Alan Porter
• 金额: $ 39.27万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830207&HistoricalAwards=false
• 关键词: TLS; Measuring; Tracking; Research; Knowledge

Enhanced Science of Science and Innovation Policy depends on better metrics. You can't manage what you can't measure. This is particularly true for interdisciplinary research, which currently has few generally agreed-upon measures with acceptable degrees of accuracy. Researchers at the University of Sussex have begun to address this problem by developing a conceptual framework to gauge research diversity. The current project builds upon that conceptual framework to empirically test metrics that gauge the interdisciplinarity of particular bodies of research. One proposed key measure assesses the degree to which particular research papers, or collections of such, integrate research knowledge from diverse research domains. A second measure determines the degree of specialization of collections of research papers (e.g., those published by a particular research center or those of a research area such as quantum dots). The resulting measures help track and characterize the emergence of new (interdisciplinary) research areas. The project described in this proposal seeks to generate analytical algorithms for indicators of interdisciplinarity. It also seeks to visually depict knowledge interchanges among areas of research activity. Such science maps can help identify and characterize focused areas of research--domains--that are sources of knowledge used by other domains. They can also show the extent of intellectual and social networking among both domains and contributing institutions. This project will, through US-UK collaboration, develop effective means to apply and test these new metrics. The proposed project focuses on nanoscience and nanoengineering ("nano"), an emergent research domain of considerable significance that extends well beyond traditional disciplinary boundaries. Georgia Tech has assembled a substantial nano dataset that will serve as the main testbed for computing and assessing indicator variations. The research team will generate indicator sets and maps of selected nano sub-topics (e.g., molecular motors research). These indicator sets and maps will be shared with researchers and R&D managers to gauge their validity and utility. Taking into account feedback, the research team will then develop a taxonomy of nanotechnology research activity based on identification of coherent research sub-areas.Broader Impacts: These tools better enable scientists, science managers, and Federal science and regulatory agencies to gauge and track cross-domain knowledge transfers. Failure to recognize the full extent and complexity of these patterns could result in major funding and regulatory mistakes. The new indicators and accompanying maps help identify leverage points likely to spark advances in science, technology, and innovation. They can also facilitate graduate education by identifying convergent knowledge domains - potentially emerging "interdisciplines." Better understanding of research landscapes can help orient graduate curricula and spotlight promising dissertation topics. More accurate interdisciplinarity measures also contribute to an ongoing National Academies initiative to bolster interdisciplinary research across the US.

增强科学和创新政策的科学取决于更好的指标。您无法管理无法测量的内容。 对于跨学科研究尤其如此，该研究目前几乎没有达成一致的措施，可以接受的准确性程度。 苏塞克斯大学的研究人员已经通过开发一个概念框架来评估研究多样性来解决这个问题。 当前的项目建立在该概念框架的基础上，以经验测试衡量特定研究机构的跨学科性的指标。 一项提出的关键措施评估了特定的研究论文或此类收集的程度，将研究知识整合到不同的研究领域。 第二种措施决定了研究论文集合的专业程度（例如，特定研究中心或研究领域（例如量子点）发表的研究论文的专业程度）。 最终的措施有助于跟踪和表征新（跨学科）研究领域的出现。 本提案中描述的项目旨在为跨学科指标生成分析算法。 它还试图以视觉描述研究活动领域之间的知识互动。 这样的科学地图可以帮助识别和表征研究的重点领域（包括其他领域使用的知识来源）。 他们还可以显示域内和贡献机构之间的智力和社交网络程度。 该项目将通过US-UK合作，开发有效的手段来应用和测试这些新指标。 拟议的项目着重于纳米科学和纳米工程（“ Nano”），这是一个具有相当重要意义的新兴研究领域，远远超出了传统的学科边界。 佐治亚理工学院已经组装了一个大量的纳米数据集，该数据集将用作计算和评估指标变化的主要测试台。 研究团队将生成选定纳米子主题的指标集和地图（例如，分子汽车研究）。 这些指标集和地图将与研究人员和研发经理共享，以评估其有效性和实用性。考虑到反馈，研究小组将基于识别一致研究子区域的识别来开发纳米技术研究活动的分类法。Broader的影响：这些工具可以更好地使科学家，科学经理以及联邦科学和监管机构能够衡量和跟踪交叉域知识转移。 不认识到这些模式的全部程度和复杂性可能会导致重大资金和监管错误。 新的指标和随附的地图有助于确定可能引发科学，技术和创新进展的利益。 他们还可以通过确定融合的知识领域 - 潜在的“跨学科”来促进研究生教育。 更好地了解研究景观可以帮助定向研究生课程，并聚焦有希望的论文主题。 更准确的跨学科措施也有助于一项正在进行的国家学院倡议，以增强整个美国的跨学科研究。