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

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，以促进这些领域的快速和有效的研究进程。需要有几个关键属性：该工具应能够实时运行，代表尽可能广泛的科学活动样本，并支持对所分配资源进行成本效益分析。知识价值：这项研究的目的是通过关注两个科学和方法问题来支持开发此类工具。首先，该项目研究科学活动早期指标的潜力，如使用数据和搜索查询日志。第二，该项目旨在开发能够根据这些早期指标实时查明和预测新趋势的模型，该项目利用了两个行之有效的项目的努力，即MESUR项目（www.mesur.org）和Eigenfactor项目（www.eigenfactor.org）。MESUR项目在过去两年中取得了世界上的重要样本？的科学活动，通过收集从世界上一些最重要的出版商，聚合器和大学联盟获得的超过10亿个文章级使用事件。Eigenfactor项目已经证明了数学网络模型的强大功能（参见Google的PageRank）根据科学引文的点阵工作对学科和期刊进行排名，这些科学引文记录了SE研究的集体历史。对科学活动“流动”的预测已被用于绘制科学活动的详细地图，这些地图可确定科学创新的潜在焦点，该项目扩大了本征因子模型，以包括MESUR的实际、实时科学活动指标。在此基础上，该项目开发了一套早期指标，可以实时检测科学创新的出现-在引用数据中可以看到这种趋势之前-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性和预测性框架，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，因此将利用现有的eigenfactor.org服务来制作免费提供的、可扩展的工具，对科学创新领域进行排名、分析、预测和绘制图表。这一研究项目提供免费提供的、可扩展的服务，以形成一个科学创新“预警”系统，预计将使公众更好地了解科学是一个复杂的动态系统。这些服务应促进公众参与建立一个能够迎接21世纪挑战的更加多样化、创新的研究格局，从而支持一个“更健康”的科学评价体系，通过承认塑造科学格局的影响和贡献的更大多样性来促进创新。

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.