Current tools supporting researchers are largely discrete, requiring scientists to manually connect the dots.
Collaborative research creates numerous data silos, which limit productivity and introduce errors.
Goals and Objectives
With projects growing in size and complexity, using common cloud, process, and analytics infrastructure can empower researchers while addressing both IP and data security requirements.
Interconnected apps, automated workflows, and intelligent analytics allow researchers to focus on insights and discovery.
Technology Deployed
Interconnected lab applications in a common cloud, including experimental design, execution, data sharing, and analysis applications
Secure collaborative infrastructure
Machine learning and automated workflows
Cognitive computing supporting research data and publication mining
Use Case Summary
Transparent experiment management across the entire laboratory value chain
Effective research sharing with external partners
Cognitive support on global research progress and insights