EAGER: Discovering Knowledge from Scientific Research Networks

通过挖掘和分析各种来源的科学数据，可以大大加快科学研究和发现过程的进步。除了模拟和观察产生的实验数据外，还有另一类科学数据;即科学研究和过程数据，例如完整和正在进行的研究项目的讨论和成果，形式为技术报告、研究论文、讨论论坛、邮件列表、研究博客等，以及研究活动之间的联系。这些数据可以被分析以发现许多重要的特征，这些特征不仅对科学发现有价值，而且对使发现过程更有效、高效和富有成效也有价值。而且，发现？虚拟社区？具有相似需求、兴趣和要求的用户可以提出潜在的合作、软件工具等建议。该项目开发了一个名为DiscKNet的基础设施（从科学研究网络中发现知识）挖掘丰富的科学研究网络，以发现新的趋势，新的研究领域，潜在的合作等。它需要构建一个基于科学出版物，讨论论坛，邮件列表，来自超级计算中心的报告，研究博客，会议页面和社交媒体（如Facebook和Twitter等）中的共同兴趣小组。数据挖掘技术在该网络上的设计，开发和应用通过识别高影响力的工具和技术，超级计算中心活动的趋势和使用模式，软件工具的常见问题，以及潜在的富有成效的科学合作机会。该项目为科学家、实验者、研究中心提供了一个建立新社区的平台。对于教育，它帮助教授，教育工作者，研究人员找到正确的群体进行当前的讨论和未来的合作。

Advancement in scientific research and discovery process can be significantly accelerated by mining and analysis of scientific data generated from various sources. In addition to the experimental data produced from simulations and observations, there is another category of scientific data; namely, scientific research and process data, such as the discussions and outcomes of complete and in-progress research projects, in the form of technical reports, research papers, discussion forums, mailing lists, research blogs, etc, and the connections between research activities. This data can be analyzed to discover many important features valuable not only for scientific discovery, but also for making the discovery process more effective, efficient, and productive. Furthermore, discovering?virtual communities? with similar needs, interests, and requirements can suggest potential collaborations, software tools, etc. This project develops an infrastructure called DiscKNet (Discovering Knowledge from Scientific Research Networks) to mine the enriched scientific research network for emerging trends, new research areas, potential collaborations, etc. It entails constructing a scientific research network based on scientific publications, discussion forums, mailing lists, reportsfrom supercomputing centers, research blogs, conference pages and common interest groups in social media such as Facebook and Twitter, etc. The design, development, and application of data mining techniques on this network lead a scientific discovery process through the identification of high impact tools and techniques, trends and usage patterns in supercomputing center activity, common issues with software tools, and potential fruitful scientific collaboration opportunities. The project provides a platform for scientists, experimentalists, research centers to build new communities. For education it assists professors, educators, researchers to find the right groups for current discussion and future collaboration.