Data-driven discovery requires scientific data ecosystems
Scientific instruments are generating extraordinary volumes of increasingly diverse digital data. Concurrently, analytics methods with large-scale simulations are evolving at a rapid pace. This is why digitization is the backbone that powers modern biomedicine.
For scientists to contribute to disruptive innovation, they need to efficiently find, access, share and integrate these petabytes of data across their global research systems. To analyze these data using the latest tools, massive data workflows must move back and forth on a converged infrastructure that interconnects distributed devices, resources on commercial clouds and HPC centers—all without downtime.
Building successful scientific data ecosystems since 2002
Integration requires experts with interdisciplinary skills.
Digitization requires building complex scientific data ecosystems with their components thoughtfully integrated and synchronized. These systems must:
- Provide efficient access to shared research applications, workflows and analytics software tools.
- Converge data from multiple distributed devices with centralized resources on commercial clouds and high-performance computing centers.
- Deploy a shared network capable of processing massive scientific data workflows—in a scalable manner.
- Enforce data strategies for effective data management, harmonized data access, integrated data sets and consistent bioinformatics analytics.
- Deploy large centralized computing capabilities in facilities able to execute large-scale simulations and data analyses.
- Encourage a culture of “our data,” not “my data”.