Computational modeling and analysis core
The Computational Modeling and Analysis (CMA) core provides access to high-performance computing (HPC), cloud computing (CP), networking, and advanced analytical software, while providing services to help researchers in neuroscience and biomedical research develop more efficient data analysis pipelines. Core hardware (cloud and high-performance computing), training, and technical support are available for a service fee. Small equipment is free. All users are welcome, with priority given to neuroscientists.
The core is designed to unlock accessible, scalable, and modern computing solutions with significant benefits for faculty, postdocs, and students:
- Removing barriers to high performance and cloud computing: The core provides a vital one-stop-shop hub for advanced computing backed by substantial computing infrastructure currently available at NUR (Pronghorn), quality consultation, collaboration and training, and a streamlined support structure ( S3) uniquely designed to lower the barrier to use encountered by COBRE investigators when using UNR’s state-of-the-art cyberinfrastructure to elevate the scope, scale and quality of their compute pipelines and data analysis.
- Improving research in neuroscience: The Core provides targeted technical support to neuroscience researchers to establish new pipelines for the use of advanced data analysis platforms made possible by the rapid adoption of artificial intelligence (AL) and technology. machine learning (ML) in the biomedical field. These services will be combined into a core with services already implemented to support advanced statistical design and computational modeling (which were previously part of the neuroimaging core).
- Research rigor and reproducibility: Core data analysis services provide expertise in pre (design) and post (analysis) statistical analyzes for researchers to ensure scientific productivity and research quality.
- Search scalability: The Core will address this problem in three ways: (1) providing COBRE researchers and the wider neuroscience and biomedical community with expert advice and consultation on statistical, analytical and AI-based models suitable for their research; (2) provide a bridge and the cyberinfrastructure for investigators to use the resources; (3) offer regular courses in scientific computing, statistical analysis and data science to students, postdoctoral researchers and professors in the design of data-intensive experiments, the exploitation of cyberinfrastructures and the integration advanced data analysis pipelines tailored to their research.