Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension" team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods.

An interdisciplinary research team led by the University of Maryland, College Park (UMD) and in partnership with the University of Maryland, Baltimore County (UMBC) has entered into a cooperative agreement with the U.S. Army Research Laboratory(ARL) worth up to $68 million. The agreement brings together a large, diverse collaborative of researchers--leveraging the University System of Maryland's national leadership in engineering, robotics, computer science, operations research, modeling and simulation, and cybersecurity--to drive transformational advances in artificial intelligence (AI) and autonomy. The five-year agreement will accelerate the development and deployment of safe, effective, and resilient capabilities and technologies, from wearable devices to unmanned aircraft, that work intelligently and in cooperation with each other and with human actors across multiple environments. The robust effort encompasses three areas of research thrusts, each supported by a team of faculty, staff, and students. The new collaboration builds on a more than 25-year research partnership between UMD and ARL in AI, autonomy, and modeling and simulation to spur the development of technologies that reduce human workload and risk in complex environments such as the battlefield and search-and-rescue operations.