Research Data Services
The Research Data Services (RDS) division collaborates with researchers to identify, build and serve their research computing and data needs, which include compute, storage maintenance and research support.
The division also offers on-premise and public cloud computing resources and solutions, often tailor-made to academic research needs. It offers on-premise services in the 19,000-square-foot, 3.5 MW (with potential capability of 13 MW), high-speed, network-connected data center. RDS also supports education with year-round internship opportunities for undergraduate students interested in software development, project management and other research computing experiences.
“We quietly work behind the scenes to anticipate what infrastructure will be needed for tomorrow’s science and to deliver research computing services with a high degree of up time and good customer service to researchers.”
Christine Kirkpatrick, RDS Division Director
EXPERTISE
- Artificial Intelligence
- Advanced Computing
- Cloud Computing
- Research Data Management
- Scientific Workflows
SERVICES
SDSC Divisions
GO FAIR U.S.
The Global Open FAIR initiative is dedicated to implementing FAIR data principles to ensure that data is Findable, Accessible, Interoperable and Reusable. SDSC's GO FAIR coordination office offers a suite of FAIR data consulting including FAIR data stewardship plans, FAIR data endpoint hosting and FAIRification of processes—ensuring that data is FAIR-born and usable by machine learning and AI technologies.
Open Storage Network
The OSN strives to provide low-cost, sustainable distributed storage cloud for the research community, facilitating the sharing and transfer of large scientific datasets among research institutions. By leveraging a network of storage nodes, it enables high-bandwidth data movement, simplifies data sharing processes and improves collaboration within the scientific community.
NIAID Data Landscaping
The National Institute of Allergy and Infectious Diseases Data Landscaping and FAIRification project benefits biomedical researchers and the broader community to generate and analyze infectious, allergic and immunological data. Using the FAIR principles as a guide, the project team provides guidance on approaches to enhance the quality of metadata within NIAID- and NIH-supported repositories and resources that harbor data and metadata.
National Science Data Fabric
NSDF is a groundbreaking initiative to democratize access to large-scale scientific data. By connecting a network of research institutions, it provides a shared infrastructure for data storage, movement and processing. This eases scientists’ access to and use of vast datasets, accelerating scientific discovery and enabling new avenues of research across various disciplines.
FAIR in ML, AI Readiness, and Reproducibility
The FARR (FAIR in ML, AI Readiness and Reproducibility) Research Coordination Network aims to advance the development and adoption of FAIR principles within the context of machine learning and artificial intelligence. FARR focuses on enhancing data and research object management, improving AI research and fostering a collaborative and trustworthy AI ecosystem. By addressing these challenges, FARR aims to accelerate scientific discovery and ensure responsible and ethical development of AI technologies.
