The Computational Modeling and Analysis (CMA) Core provides access to state of the art high-performance computing (HPC), cloud computing (CP), Networking, and advanced analytical software, while providing services to aid investigators in neuroscience and biomedical research to develop more effective data analytics pipelines. Major equipment (cloud and high-performance computing), training, and technical assistance are available on a fee-for-service basis. Smaller equipment is free-of-charge. All users are welcome with priority given to Neuroscience investigators.
The core is designed to unlock accessible, scalable, and modern computing solutions with significant benefits to faculty, postdocs and students:
- Removing Barriers to High-Performance and Cloud Computing: The Core provides a vital one-stop hub for advanced computing supported by a significant computational infrastructure currently available at UNR (Pronghorn), quality consultation, collaboration, and training, as well as a unique Streamlined Support Structure (S3) designed to lower the barrier to utilization encountered by COBRE investigators in using UNR’s state-of-the-art cyberinfrastructure in order to elevate the scope, scale, and quality of their computational and data analytics pipelines.
- Enhancing neuroscience research: The Core provides targeted engineering support for neuroscience investigators to establish novel pipelines for utilizing advanced data analytics platforms enabled by the rapid adoption of artificial intelligence (AL) and machine learning (ML) in the biomedical domain. These services will be combined within one core with previously implemented services to support advanced statistical design and computational modeling (previously part of the Neuroimaging Core).
- Research rigor and reproducibility: The data analytics services of the core provide expertise in both pre (design) and post (analysis) statistical analyses for researchers to ensure scientific productivity and research quality.
- Research scalability: The Core will address this issue in three ways: (1) by providing the COBRE investigators and broader neuroscience and biomedical community with expert advice and consultation on appropriate statistical, analytics, and AI-based models suitable for their research; (2) providing a bridge and the cyberinfrastructure for investigators to utilize the resources; (3) offering regular scientific computing, statistical analytics, and data science courses for students, postdoctoral scholars, and faculty in designing data-intensive experiments, operating the cyber infrastructures, and integrating advanced data analytics pipelines appropriate to their research.