EAGER: Long-term View on Nanotechnology R&D as Reflected in Scientific Papers, Patents, and NSF Awards

国家纳米技术倡议(NNI)自2001年以来一直在进行，协调联邦工作的尝试甚至可以追溯到更早的时候。在其历史上，NNI为建立60多个最先进的跨学科研究和教育中心发挥了重要作用，这些研究和教育中心的工作领域广泛，包括卫生、航空、能源和国防。在这个奖项中，亚利桑那大学人工智能实验室的研究人员将对纳米技术研发的20年(1991-2010年)的产出进行纵向审查，包括统计趋势和主题分析。这将使用信息扩散模型来实现，以了解过去20年在这些努力上花费数十亿美元对纳米技术研发的影响和结果。数据来源将包括文章和科学论文、美国专利商标局(USPTO)专利和NSF奖项。这种建模技术通常在流行病和传染病传播的背景下使用，将用于分析新出现的主题和未来可能的知识模式。通过研究信息从科学发现传播到专利和商业产品的模式，该模型可以用来帮助估计授权的专利文件在未来被其他人引用的可能性。这一点很重要，因为在一个快速变化的领域，如纳米技术，过去的表现并不一定是未来成功的最佳预测。这一分析的结果可以帮助利益相关者、政策制定者和资助机构，如NSF，了解资金和知识传播对纳米技术研究和开发的影响。这种理解可以反过来被用作一种工具，帮助影响未来的政策、程序和研发资金。

The National Nanotechnology Initiative (NNI) has been underway since 2001, with attempts at coordinating federal work dating back even earlier. Over its history, NNI has been instrumental in establishing more than 60 state-of-the-art interdisciplinary research and education centers working in fields as diverse as health, aeronautics, energy, and defense. In this award, researchers at the Artificial Intelligence Laboratory at the University of Arizona will conduct a longitudinal examination of the output of the twenty years of nanotechnology research and development (1991-2010), including statistical trends and topic analysis. This will be accomplished using a information diffusion model to understand the influences on nanotechnology R&D and the outcomes resulting from the many billions spent on these efforts over the past two decades. Sources of data will include articles and scientific papers, United States Patent and Trademark Office (USPTO) patents, and NSF awards. This modeling technique, typically used in the context of epidemics and the spread of infectious diseases, will be used to analyze emerging topics and possible future knowledge patterns. By examining the patterns by which information diffuses from scientific discovery into patents and commercial products, the model can be used to help estimate the probability of a granted patent document to be cited by others in the future. This is important as in a rapidly changing field such as nanotechnology, past performance is not necessarily the best predictor of future success.The results of this analysis can help stakeholders, policymakers, and funding agencies such as NSF understand what the impact of funding and knowledge diffusion is on nanotechnology research and development. This understanding can then in turn be used as a tool to help influence future policies, procedures, and R&D funding.