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Medical University of South Carolina AI High Performance Computing Core (RRID:SCR_028822)Copy Citation Copied
URL: https://www.musc.edu/innovation/partnerships/clemson-musc-ai-hub
Proper Citation: Medical University of South Carolina AI High Performance Computing Core (RRID:SCR_028822)
Description: Core delivers support, intuitive tools, and broad accessibility for biomedical discovery. Allows teams to move from experimentation to deployment, fostering cross-functional collaboration between AI HPC and the AI Hub, expands access to advanced computing resources, streamline workflows, and empowers a growing network of users. High-performance computer is capable of performing artificial intelligence techniques such as machine learning and deep learning. These resources accelerate healthcare research, help investigators from different fields to work together and share their data and expertise, and ultimately improve population health through rapid translation of research to clinical care.
Synonyms: , Medical University of South Carolina AI High Performance Computing Core Facility, AI High-Performance Computing (AI HPC) Core
Resource Type: access service resource, computational service resource, core facility, service resource
Keywords: ABRF, computing services, access to advanced computing resources, machine learning, deep learning,
Funding: NIH Office of the Director S10OD034280
Resource Name: Medical University of South Carolina AI High Performance Computing Core
Resource ID: SCR_028822
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Source: SciCrunch Registry (RRID:SCR_005400)
Description: Interactive portal for finding and submitting biomedical resources. Resources within SciCrunch have assigned RRIDs which are used to cite resources in scientific manuscripts. SciCrunch Registry, formerly NIF Registry, provides resources catalog. Allows to add new resources. Allows edit existing resources after registration. Curators are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping contents current.
URL: https://rrid.site/rin/sources/SCR_005400
The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
Email: info_at_ dknet.org
