EAGER: Identifying and Capitalizing on Schools of Thought as a Basis for Virtual Communities in Computer Science and Engineering Research

随着计算机科学和工程研究社区继续其半个世纪以来不间断的指数增长，社区寻找社区和协作的旧方式正在分崩离析。研究成果的产生和分享的速度比过去快得多，但建立关系的机会比以往任何时候都要少--而人际关系是研究企业的一个重要组成部分。以数据为导向的研究会议已经发展得太大了，已经不能有效地实现它们的许多最初目的，例如促进社区和共同目标；会见新的潜在同行合作者并亲自与他们建立关系；为初级研究人员提供机会与资深研究人员联系并建立对职业生涯如此重要的松散关系；以及提供机会不仅了解最新的研究结果(现在可以在互联网上立即获得)，而且还提供机会与产生这些结果的研究人员互动，并超越出版物的表面进行探索。新冠肺炎正在加速这一趋势。大多数会议都选择了虚拟，这使所有人都更容易接触到会议，而且往往是免费的；但一对一互动的机会大大减少，甚至没有，联网机会和社区建设也是如此。为了解决这些问题，这个探索性的数据科学项目将分析现有研究合作的大量可用数据，并创建方法来确定特定的潜在新关系和合作，这些关系和合作有可能提高科学创新的速度和可信度，同时鼓励想法、方法和技术的交叉培养。作为该项目的一部分，研究人员将对两名本科生进行数据科学方面的培训。该项目将借鉴社交网络社区检测和表征、推荐系统和社交网络可视化的技术，根据公开可获得的关于研究人员之间的合作和其他关系的信息，产生新的方法来识别和表征现有的小型紧密联系的研究社区或“学派”。该项目将根据对过去非常成功的合作的特点的分析，提出将互补的思想流派聚集在一起的方法。由此产生的新的虚拟社区将仍然足够小，可以进行广泛的一对一互动，这将支持促进现有联系和形成新的联系。该项目的方法将在一个主要的计算机科学会议上进行初步测试，该项目的代码将公开提供。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As the computer science and engineering research community continues its unbroken half-century streak of exponential growth, the community's old ways of finding community and collaboration are falling apart. Research results are generated and shared at a much faster rate than in the past, but relationship-building opportunities are lower than ever – and human relationships are an essential component of the research enterprise. Already data-oriented research conferences have grown too large to be effective for many of their original purposes, such as fostering community and shared purpose; meeting new potential peer collaborators and building relationships with them in person; providing an opportunity for junior researchers to connect to senior researchers and build the loose ties that are so important for careers; and providing an occasion to not only learn about the latest research results (which are now instantly available on the internet) but also to interact with the researchers who produced those results and probe beyond the surface of publications. COVID-19 is accelerating that trend. Most conferences have chosen to become virtual, which makes them more accessible to all, often at no cost; but the chance to interact one-on-one is greatly reduced or even absent, as are networking opportunities and community-building. To address these problems, this exploratory data science project will analyze the extensive data available about existing research collaborations and create methods to identify specific potential new relationships and collaborations that have the potential to enhance the pace and trustworthiness of scientific innovation, while encouraging cross-fertilization of ideas, methods, and techniques. As an integral part of the project, the investigator will train two undergraduates in data science.Drawing on techniques from social network community detection and characterization, recommender systems, and social network visualization, the project will produce new methods to identify and characterize existing small close-knit research communities, or "schools of thought" based on publicly available information about collaborations and other relationships between researchers. The project will propose methods to bring together complementary schools of thought, based on an analysis of the characterization of past highly successful collaborations. The resulting new virtual communities will still be small enough for extensive one-on-one interaction, which will support both the promotion of existing bonds and the formation of new ones. The project's methods will be pilot tested at a major computer science conference, and the project's code will be made publicly available.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.