The life sciences are becoming increasingly data intensive, owing much to the huge improvements seen in large-scale gene sequencing and other molecular “omics” techniques. There is a need for large-scale sustainable and interoperable data management and storage methods that allow secure and easy access to and reuse of these highly complex data. Simultaneously, as omics-focussed life science research projects increasingly depend on more than one type of measurement, there is a widely felt need for the ability to integrate different data types.
