Research Data Management (RDM) has become a key service offered by universities to meet the needs of today’s researchers. But this is just the tip of the iceberg, the beginning of a transformation in the way researchers work, and the tools and services they need to help them.
This talk addresses the next phase of Research Data challenges, beyond simply managing data better. In a short amount of time research has moved from data poor to data rich, and increasingly with good RDM practices in place. It has also moved from a messy burden to a tidy asset. But these are just the early stages in a data revolution. What is coming next?
Three specific themes are introduced to answer this question:
1. From Analysis and Simulation to Deep Learning and Computational Model Discovery: changing the way science is done
2. From Schema to Pragmatics: different ways to describe research data
3. From Sterile Datasets to a Healthy Ecosystem: Data licensing strategies for communities to encourage sharing and reuse.
Each theme is introduced along with examples of the kinds of challenges it poses for supporting research better.