CC* BRICCs
Overview
Smaller institutions of higher education and community colleges contribute greatly towards the progression of economic and computing workforce in the nation. Flourishing curiosity in fields such as computer networking and cloud computing has led to the support of advanced cyberinfrastructure practices in research and academic projects at these smaller institutions of higher education. The Building Research Innovation at Community Colleges (BRICCs) approach serves as an uncommon opportunity to study and analyze campus computing practices at smaller institutions and community colleges. This approach focuses on learning about current problems, collaborating with smaller institutions to facilitate solutions, and presenting its conclusion to the broader research community. In order to champion partnerships between experienced cyberinfrastructure professionals and smaller institutions, BRICCs will host virtual and in-person community workshops. These workshops will serve to broaden the effect of cutting-edge cyberinfrastructure on campus computing. Learning resources, support for future funding, workshop reports, and networking models that reflect the research and academic goals of smaller institutions will be created by BRICCs. The approach serves as a cooperative space for discourse regarding challenges in the progression of cyberinfrastructure adoption in educational and research settings.
Our Purpose
The BRICCs approach encompasses the unified efforts of various institutions, ranging from R1 universities to community colleges. It will create and fortify relationships between larger universities and smaller institutions of higher education to ensure the progression of computing and economic development. BRICCs offers the opportunity to expand and develop a comprehensive platform to the national level.
Founding BRICCs Team
- Dhruva Chakravorty (Principal Investigator, Texas A&M University)
- Sarah Janes (Co-Principal Investigator, San Jacinto College)
- Timothy Cockerill (Co-Principal Investigator, University of Texas at Austin)
This project is supported by NSF award number 2019136