Research Data Management
The promise of high throughput ‘-omics’ can only be realized if it is coupled with a parallel evolution of data silos into integrated high quality data lakes. The consistent application of a set of core data management principles, including a pragmatic master data management approach, an ingestion framework with sensible annotation and a curation strategy, will facilitate predictive analytics in the era of data science. This chapter outlines how data resources can be findable (F), accessible (A), reusable (R) and interoperable (I), or FAIR, as described by the US National Institutes of Health (NIH) Big Data to Knowledge Framework. We propose a four part approach: assemble, describe, predict and understand, which will enable a data ecosystem for sustainable biomedical research.