TLS: COLLABORATIVE RESEARCH: Tracking Scientific Innovation from Usage Data: Models and Tools to Support a Science of Science

TLS：协作研究：从使用数据跟踪科学创新：支持科学的模型和工具

• 批准号: 0915005
• 负责人: Carl Bergstrom
• 金额: $ 21.7万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915005&HistoricalAwards=false
• 关键词: TLS; COLLABORATIVE; RESEARCH; Tracking; Scientific

This project develops a set of tools that allow organizations investing in Science and Engineering to identify and predict the emergence of innovative research. Such a capacity would permit organizations to efficiently allocate resources to stimulate rapid and effective research process in these areas. Several key attributes are needed: the tool should be able to operate in real-time, be representative of the widest possible sample of scientific activity, and support a cost-benefit analysis of allocated resources. Intellectual merit: This research aims to support the development of such tools by focusing on two scientific and methodological issues. First, the project studies the potential of early indicators of scientific activity such as usage data and search query logs. Second, the project aims to develop models that can, on the basis of such early indicators, identify and predict emerging trends in real-time.The project leverages the efforts of two well-established projects, namely the MESUR project (www.mesur.org) and the Eigenfactor project (www.eigenfactor.org). The MESUR project has, over the course of the past 2 years, captured a significant sample of the world?s scientific activity, via a collection of more than 1 billion article-level usage events acquired from some of the world's most significant publishers, aggregators and university consortia. The Eigenfactor project has demonstrated the power of mathematical network models (cf. Google's PageRank) to rank disciplines and journals according to the lattice work of scientific citations that records the collective history of S&E research. Predictions of the "flow" of scientific activity have been used to produce detailed maps of scientific activity that may identify potential foci of scientific innovation.This project expands the Eigenfactor models to include MESUR's indicators of actual, real-time scientific activity. On that basis the project develops a set of early indicators that can detect the emergence of scientific innovation in real-time - before such trends are visible in citation data - and relates these indicators to public policy and decision making. The project also develops explanatory and predictive frameworks that connect observations of individual behavior with emergent, collective phenomena such as scientific innovation. Since the focus of the research is whether it is possible to develop analytic and predictive tools that indicate why, how and where scientific innovation is most likely to occur, the existing eigenfactor.org services will be leveraged to produce freely available, expandable tools that rank, analyze, predict and chart areas of scientific innovation.Broader Impact: this research project produces freely available, expandable services to form an "early warning" system for scientific innovation that are expected to lead to a better public understanding of science as a complex, dynamic system. Such services should foster public participation in efforts to establish a more diverse, innovative research landscape that can meet the challenges of the 21st century.This work should thereby support a "healthier" system of scientific evaluation that fosters innovation by acknowledging a greater diversity of influences and contributions that shape the scientific landscape.

该项目开发了一套工具，使组织投资于科学和工程，以识别和预测创新研究的出现。这样的能力将使组织能够有效地分配资源来刺激这些领域的快速有效研究过程。需要几个关键属性：该工具应能够实时运行，代表最广泛的科学活动样本，并支持对分配资源的成本效益分析。知识分子优点：这项研究旨在通过关注两个科学和方法论问题来支持此类工具的开发。首先，该项目研究了科学活动的早期指标（例如使用数据和搜索查询日志）的潜力。其次，该项目旨在开发可以根据此类早期指标来识别和预测实时的新兴趋势的模型。该项目利用了两个公认的项目的努力，即Mesur Project（www.mesur）。 org）和eigenfactor项目（www.eigenfactor.org）。在过去的两年中，Mesur项目通过收集超过10亿的文章级使用事件，从世界上一些最重要的出版商，聚合商那里获得了超过10亿个文章级别的使用，从而捕获了世界上的大量科学活动。和大学财团。本本曲目项目已经证明了数学网络模型（参见Google的Pagerank）的力量，根据科学引用的晶格工作，以记录了S＆E研究的集体历史。对科学活动的“流动”的预测已被用来制作科学活动的详细地图，该图可能鉴定出科学创新的潜在焦点。该项目扩展了本本特征模型，包括Mesur的实际，实时科学活动的指标。在此基础上，该项目开发了一组早期指标，可以实时检测科学创新的出现 - 在引用数据中可见此类趋势之前，并将这些指标与公共政策和决策制定联系起来。该项目还开发了解释性和预测框架，这些框架将个人行为的观察与新兴的集体现象（例如科学创新）联系起来。由于该研究的重点是是否有可能开发分析和预测工具，以表明最有可能发生科学创新的原因，如何以及何处，现有的eigenfactor.org服务将被利用，以生成可用的可用，可扩展的工具，以排名排名，分析，预测和图表科学创新的领域。BROADER的影响：该研究项目可免费提供，可扩展的服务，形成科学创新的“预警”系统，预计将使人们对科学作为复杂的科学有更好的了解，动态系统。这样的服务应促进公众参与建立更多样化的创新研究领域的努力，以应对21世纪的挑战。因此，这项工作应该支持一种“更健康的”科学评估体系，从而通过承认更多的影响力来促进创新以及塑造科学景观的贡献。