CDI-Type II: Collaborative Research: Groupscope: Instrumenting Research on Interaction Networks in Complex Social Contexts

社会上许多最重要的职能都是由大团体或团队承担的。应急响应、产品开发、保健、教育和经济活动都是在大型、动态、相互作用的群体网络的背景下进行的。与孤立的小团体或正式组织的研究相比，对这类团体网络的理论和研究要落后得多。群组网络研究的一个主要挑战是收集和分析研究这些大型动态群组网络所需的高分辨率、高容量、观测数据的巨大天体所伴随的困难。该项目的目标是通过应用高级计算应用程序来捕获、管理、注释和分析这些海量的视频、音频和其他数据观测集，以应对这一挑战。由此产生的数据分析系统GroupScope将使对大型动态群体中的社交互动的突破性研究能够比以前更快、更可靠地进行。它将通过将尽可能多的功能自动化到最高程度来实现这一点，包括管理大量的视频、音频和传感器数据、转录、分析关键话语事件的音频、对视频流进行注释和索引以及编码交互。这些第一次通过的分析然后可以由人类分析员补充(他们的分析反过来将提供给机器学习，这将改进计算机化的分析)。GroupScope将与研究应急团队、儿童游乐场行为、分布式团队和产品开发团队的社会科学家合作开发。开发完成后，GroupScope将部署在网络环境中，这是一个基于Web 2.0的网络基础设施，使研究人员社区能够就常见问题进行合作。网络环境将使多名研究人员能够为小团体和大型动态团体和网络分析和编码相同的团体数据。从不同角度进行的多个分析和编码将使人们能够发现不同层次和层次的人类互动之间以前未被怀疑的关系。它们还可以与参与者的调查答复联系起来，使之能够与感知和特征领域联系起来。科学上的许多最根本的进步都来自于新仪器的发展，例如更强大的望远镜或显微镜，可以让科学家观察分子。以同样的方式，GroupScope将阐明现实世界中的组织所执行的关键职能的工作原理，如紧急响应单位、医疗保健团队、证券交易所和军事单位。GroupScope还将应用于培训那些在多团队系统中工作的人，例如灾难的第一反应人员。它可以用来记录和“评分”培训课程，向参与者反馈他们方法的优点和缺点。

Many of the most important functions in society are undertaken by large groups or teams. Emergency response, product development, health care, education, and economic activity are pursued in the context of large, dynamic, interacting networks of groups. Theory and research on such networks of groups is much less developed than research on isolated small groups or formal organizations. A major challenge for research on networks of groups is the difficulties that accompany the collection and analysis of the huge bodies of high resolution, high volume, observational data necessary to study these large, dynamic networks of groups. The goal of this project is to address this challenge by applying advanced computing applications to capture, manage, annotate and analyze these massive observational sets of video, audio, and other data. The resulting data analysis system, GroupScope, will enable breakthrough research into social interaction in large, dynamic groups to be conducted much more quickly and with much higher reliability than was previously possible. It will do this by automating as many functions as possible to the highest degree possible, including managing huge volumes of video, audio, and sensor data, transcription, parsing audio for critical discourse events, annotation and indexing of video streams, and coding interaction. These first pass analyses can then be supplemented by human analysts (and their analyses in turn will feed into machine learning that will improve the computerized analysis). GroupScope will be developed with the collaboration of social scientists studying emergency response teams, children's playground behavior, distributed teams, and product development teams. When developed, GroupScope will be deployed in a cyberenvironment, a Web 2.0 based cyberinfrastructure that enables a community of researchers to collaborate on common problems. The cyberenvironment will enable multiple researchers to analyze and code the same group data for both small groups and large dynamic groups and networks. Multiple analyses and codings working from diverse perspectives will enable discovery of previously unsuspected relationships among different levels and layers of human interaction. They can also be linked to survey responses from participants, enabling linkage to the realm of perceptions and traits. Many of the most fundamental advances in science have come through the development of new instruments, such as more powerful telescopes or microscopes that can allow scientists to view molecules. In the same way GroupScope will shed light on the workings of critical functions performed by real world groups such as emergency response units, health care teams, stock exchanges, and military units. GroupScope will also have applications in the training of those working in multi-team systems, such as first responders to disasters. It can be used to record and "grade" training sessions, giving participants feedback on both strengths and weaknesses of their approaches.