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.

科学和创新政策的增强取决于更好的衡量标准。你无法管理你无法衡量的东西。 对于跨学科研究来说尤其如此，目前跨学科研究几乎没有公认的准确度可接受的措施。 苏塞克斯大学的研究人员已经开始通过开发一个衡量研究多样性的概念框架来解决这个问题。 当前的项目建立在该概念框架的基础上，以实证方式测试衡量特定研究机构的跨学科性的指标。 提出的一项关键指标评估特定研究论文或此类论文集整合不同研究领域的研究知识的程度。 第二个衡量标准确定研究论文集的专业化程度（例如，由特定研究中心发表的论文或量子点等研究领域的论文）。 由此产生的措施有助于跟踪和描述新（跨学科）研究领域的出现。 该提案中描述的项目旨在生成跨学科指标的分析算法。 它还试图直观地描述研究活动领域之间的知识交流。 这样的科学地图可以帮助识别和描述重点研究领域（领域），这些领域是其他领域使用的知识来源。 它们还可以显示两个领域和贡献机构之间的知识和社交网络的程度。 该项目将通过美英合作开发有效的方法来应用和测试这些新指标。 拟议的项目重点关注纳米科学和纳米工程（“纳米”），这是一个具有相当重要意义的新兴研究领域，远远超出了传统学科的界限。 佐治亚理工学院已经收集了一个大量的纳米数据集，该数据集将作为计算和评估指标变化的主要测试平台。 研究团队将生成所选纳米子主题（例如分子马达研究）的指标集和地图。 这些指标集和地图将与研究人员和研发经理共享，以衡量其有效性和实用性。考虑到反馈，研究团队将在确定相关研究子领域的基础上制定纳米技术研究活动的分类法。 更广泛的影响：这些工具更好地使科学家、科学管理者以及联邦科学和监管机构能够衡量和跟踪交叉研究-领域知识转移。 未能认识到这些模式的全部范围和复杂性可能会导致重大的融资和监管错误。 新指标和随附地图有助于确定可能激发科学、技术和创新进步的杠杆点。 他们还可以通过识别趋同的知识领域——潜在的新兴“跨学科”来促进研究生教育。 更好地了解研究前景可以帮助确定研究生课程并突出有前途的论文主题。 更准确的跨学科测量也有助于国家科学院正在进行的一项倡议，以加强美国各地的跨学科研究。