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EAGER: A Longitudinal Study of Knowledge Diffusion and Societal Impact of Nanomanufacturing Research & Development: Harnessing Data for Science and Engineering

EAGER: A Longitudinal Study of Knowledge Diffusion and Societal Impact of Nanomanufacturing Research & Development: Harnessing Data for Science and Engineering
EAGER:纳米制造研究的知识传播和社会影响的纵向研究
批准号:
1832926
负责人:
Hsinchun Chen
金额:
$15.84万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-08-01 至 2021-07-31

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中文摘要
翻译
自2000年实施国家纳米技术倡议以来,在理解纳米尺度上发生的独特物理现象以及设计和制造新的纳米尺度材料、结构、器件和系统方面取得了重大进展。然而,为了实现这些纳米技术进步对国家的好处,也必须在纳米制造方面取得进步,这些进步必须反过来转化为工业生产。 这个早期概念探索性研究资助(EAGER)项目将使用先进的数据分析框架,调查纳米制造研究的知识传播和社会影响。研究结果有望为政府、行业和研究人员提供信息,以确定研究差距,以及如何将资源和投资用于解决将最有前途的纳米技术发现扩大到工业生产的障碍。该项目具有高度探索性,因此适合EAGER计划。研究成果将广泛传播。该项目响应NSF的大理念-利用数据为21世纪世纪科学和工程。该项目开发新的分析模型和框架,以研究和分析纳米制造研究的成果。纳米技术研究大多是基于小规模调查的高水平行业范围的审查,大多数成员都可以获得。该项目是数据科学驱动的,基于现有的大型数据库和最先进的数据分析,包括期刊文章,专利,许可证和技术转让数据集,并公开提供。该项目开发了一个框架,系统地收集了过去15-20年来从大学技术转让办公室、公共新闻数据库、专利和出版物中产生的相关数据,并分析了研究投资和具体项目对知识传播的影响及其商业影响。从这些数据中,该项目开发了一种新颖的、可扩展的文本挖掘、主题建模、趋势分析和网络分析,以量化、评估和可视化每项研究工作的长期知识扩散。预期的结果是了解,(1)nanomanufacturing知识扩散路径从学术出版物专利商业化,(2)的影响,联邦投资nanomanufacturing研究,促进长期的知识扩散,和(3)之间的差异,大规模的合作项目和小规模的个人项目,促进长期的知识扩散。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Since the National Nanotechnology Initiative was implemented in 2000, significant progress has been made in understanding the unique physical phenomena that occur at the nano-scale and in designing and fabricating new nano-scale materials, structures, devices and systems. To realize the benefits to the nation of these advances in nanotechnology, however, advances must also be made in nanomanufacturing, and these advances must translate, in turn, into production by industry. This EArly-concept Grant for Exploratory Research (EAGER) project will investigate the knowledge diffusion and societal impact of nanomanufacturing research, using advanced data analytics frameworks. The study results are expected to inform government, industry, and researchers in identifying research gaps and how resources and investments could be directed to address obstacles to scaling the most promising nanotechnology discovery to industrial production. The project is highly exploratory and hence suitable for the EAGER program. The research results will be broadly distributed. The project responds to the NSF Big Idea--Harnessing Data for 21st Century Science and Engineering.This project develops new analytical models and frameworks to study and analyze the outputs of research in nanomanufacturing. Nanotechnology studies are mostly high-level industry-wide reviews based on smaller-scale survey and are mostly available to members. This project is data science-driven, based on existing large databases and state-of-the-art data analytics including journal articles, patents, licenses and technology transfer datasets and made publicly available. The project develops a framework that systematically collects relevant data generated in the past 15-20 years from university technology transfer offices, public news databases, patents and publications, and analyzes the influence of research investments and specific projects on knowledge diffusion and its commercial impact. From the data, the project develops a novel, scalable text mining, topic modeling, trend analysis, and network analysis to quantify, evaluate, and visualize each research effort's long-term knowledge diffusion. The expected outcomes are to understand, (1) nanomanufacturing knowledge diffusion path from academic publications to patents to commercialization, (2) impact of federal investment in nanomanufacturing research in facilitating long-term knowledge diffusion, and (3) the difference between large-scale collaborative projects and small-scale individual projects facilitating long-term knowledge diffusion. The broad set of analytical techniques developed from this research are expected to further advance the scientific use of these methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1007/s11051-019-4608-0
发表时间: 2019-08
期刊: Journal of Nanoparticle Research
影响因子: 2.5
作者: [Lanfen Wu;Hongyi Zhu;Hsinchun Chen;M. Roco]
通讯作者: Lanfen Wu;Hongyi Zhu;Hsinchun Chen;M. Roco
CICI: UCSS: Enhancing the Usability of Vulnerability Assessment Results for Open-Source Software Technologies in Scientific Cyberinfrastructure: A Deep Learning Perspective
  • 批准号:
    2319325
  • 项目类别:
    Standard Grant
  • 资助金额:
    $60.0万
  • 财政年份:
    2023
  • 负责人:
    Hsinchun Chen
  • 依托单位:
EAGER: SaTC-EDU: Artificial Intelligence and Cybersecurity Research and Education at Scale
  • 批准号:
    2038483
  • 项目类别:
    Standard Grant
  • 资助金额:
    $29.77万
  • 财政年份:
    2020
  • 负责人:
    Hsinchun Chen
  • 依托单位:
SaTC: CORE: Small: Cybersecurity Big Data Research for Hacker Communities: A Topic and Language Modeling Approach
  • 批准号:
    1936370
  • 项目类别:
    Standard Grant
  • 资助金额:
    $51.06万
  • 财政年份:
    2019
  • 负责人:
    Hsinchun Chen
  • 依托单位:
CICI: SSC: Proactive Cyber Threat Intelligence and Comprehensive Network Monitoring for Scientific Cyberinfrastructure: The AZSecure Framework
  • 批准号:
    1917117
  • 项目类别:
    Standard Grant
  • 资助金额:
    $99.8万
  • 财政年份:
    2019
  • 负责人:
    Hsinchun Chen
  • 依托单位:
海外基金