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，使投资于科学和工程的组织能够识别和预测创新研究的出现。这种能力将使各组织能够有效地分配资源，以促进这些领域的迅速和有效的研究进程。需要几个关键属性：该工具应该能够实时操作，代表尽可能广泛的科学活动样本，并支持对分配资源的成本效益分析。智力价值：本研究旨在通过关注两个科学和方法问题来支持此类工具的开发。首先，该项目研究了科学活动早期指标的潜力，如使用数据和搜索查询日志。其次，该项目旨在开发模型，以这些早期指标为基础，实时识别和预测新出现的趋势。该项目利用了两个成熟项目的努力，即MESUR项目（www.mesur.org）和Eigenfactor项目（www.eigenfactor.org）。在过去的两年里，MESUR项目已经捕捉到了世界的一个重要样本。通过从世界上一些最重要的出版商、聚合者和大学联盟获得的超过10亿篇文章级使用事件的集合，我们可以了解美国的科学活动。特征因子项目已经展示了数学网络模型（参见b谷歌的PageRank）的强大功能，它可以根据科学引用的格子结构对学科和期刊进行排名，这些引用记录了标普研究的集体历史。对科学活动“流动”的预测已被用来绘制科学活动的详细地图，从而可能确定科学创新的潜在焦点。该项目扩展了特征因子模型，以包括MESUR的实际、实时科学活动指标。在此基础上，该项目制定了一套早期指标，可以实时发现科学创新的出现——在这些趋势在引文数据中可见之前——并将这些指标与公共政策和决策联系起来。该项目还开发了解释和预测框架，将个人行为的观察与科学创新等新兴的集体现象联系起来。由于研究的重点是是否有可能开发出分析和预测工具，以表明科学创新最有可能发生的原因、方式和地点，现有的eigenfactor.org服务将被利用来生产免费可用的、可扩展的工具，对科学创新领域进行排名、分析、预测和图表。更广泛的影响：这个研究项目提供免费的、可扩展的服务，形成一个科学创新的“早期预警”系统，有望使公众更好地理解科学是一个复杂的、动态的系统。这些服务应促进公众参与，努力建立一个更多样化、更创新的研究环境，以迎接21世纪的挑战。因此，这项工作应该支持一个“更健康”的科学评估体系，通过承认塑造科学景观的更大的影响和贡献的多样性来促进创新。