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Mount Sinai Data Warehouse
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About Scientific Computing and Data at Mount Sinai
The mission is to enable and accelerate biomedical discovery through collaborative research using high performance computing, biomedical informatics and data science.
Our guiding principle for data is FAIR: Findable, Accessible, Interoperable, and Reusable (and Reproducible). Scientific Computing and Data partners in research projects through:
- High Performance Computing: The computing infrastructure provides resources and assistance to researchers across institutions around the globe. The primary asset for Scientific Computing is the supercomputer Minerva, accessible for processing large-scale computing jobs for research.
- Research Data Services: Researchers planning new studies or continuing longitudinal research can utilize self-service tools or custom services to process, access, or collect data.
- Mount Sinai Data Warehouse: MSDW houses and collects clinical and operational data for use in clinical and translational research, containing over 10 million records and over 2 billion facts.
We engage and collaborate with researchers and clinicians to achieve extraordinary biomedical breakthroughs. Genomics, molecular biology, oncology, and psychiatry are just a few of the fields in which scientists have used our expertise and resources to achieve new discoveries at speeds that were unthinkable only a few years earlier. In addition, researchers are devising more complex experiments, higher fidelity simulations, and attaining a higher level of accuracy.
Digital Concierge Office Hours
The Digital Concierge service is first-come, first-served with no appointment required. Join the open virtual office hours held every Wednesday from 3:30-4:30 PM to ask questions, get answers, and receive assistance with your research service.
Meet Our Team
Led by Dean for Scientific Computing Patricia Kovatch, the Scientific Computing team provides tools, resources, and assistance to researchers across institutions around the globe.
Supported by grant UL1TR004419 from the National Center for Advancing Translational Sciences, National Institutes of Health.