FAIR, but faster: how an end-to-end data lifecycle advances scientific innovation

Clinical trials generate a wealth of data. To maximize the utility of this data, it is critical that data be made FAIR (Findable, Accessible, Interoperable and Reusable) and that we have to tools necessary to properly analyze this data. 

We have previously described retrospective curation efforts that facilitated a broad range of discoveries, but also noted that these curation efforts were costly, time-consuming and not scalable (Arefolov et al., 2021, DOI: 10.1162/dint_a_00106). Here, we describe the expansion of those curation efforts to a complete data management lifecycle supported by key end-to-end processes, that reduces the time to provide high-quality scientific datasets with rich metadata to a broad range of researchers. We also present an integrated ecosystem of applications to support data management operations and scientific analysis activities. Together, these processes and tools serve as key enabling infrastructure for scientific efforts in pharmaceutical research and development.