A Neuroinformatics (NI) core will be established at Kansas State University (KSU) to support the primary projects, programs, and cores across C-NAP. The NI core will provide secure, fast, and efficient access for data sharing, analytics, computational modeling, and high-performance computing (HPC). This core is particularly important for supporting the management, analysis, and modeling of large data sets collected in neuroscience studies such as neuroimaging (fMRI, EEG, diffusion tensor imaging), eye tracking, driving simulator behavior, and other quantitative behavioral measurements that are often conducted in real-time. NI core access will be available to all primary and affiliated members of C-NAP. The NI core will be comprised of web servers, and dual data servers (initially each specified at ~100TB raw capacity) to provide redundancy and scalability, with plans to purchase additional storage servers (or upgrade the initial ones) each following year to an expected 1PB aggregate total as C-NAP needs grow. These servers will be linked with high-speed (minimum 10GbE) networks to the KSU and WSU campuses and at 40GbE to the Beocat cluster, one of the largest supercomputing clusters in the state of Kansas. The NI core will be used for longer-term archiving and data sharing, to meet NSF/NIH requirements, with access by authenticated users through the Own Cloud (or equivalent) storage system. This will promote the development and continuation of collaborative research activities by providing a secure space for data storage and access by groups of users. The core will be directed by Dr. Daniel Andresen, who is the Director of the KSU Institute for Computational Research in Engineering and Science, an expert in the area of high performance computing and an Extreme Science and Engineering Discovery Environment (XSEDE) Level 3 Service Provider. Technical support will be provided by a full-time data technician and graduate student. The overarching goal of the NI core is to promote the ability of C-NAP researchers to compete for extramural funding by incorporating technologies that are needed to answer the most challenging questions facing modern neuroscience researchers.
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