25-LB: Are You a Good Steward of Data?

The Problem: There is an urgent need for innovative solutions for new and improved treatments and therapies. Often clinical research data is “silo-ed” within an organization, preventing the broader research community from leveraging aggregated information from individuals and studies globally. This isolation of data limits the discoveries possible within a disease preventing the discovery of new scientific links between disease areas. Inconsistent and ambiguous data representation leads to a lack of transparency, traceability and reproducibility, in turn fostering inefficiencies and inaccuracies in research that loses time and increases costs. Simply put Are we being good stewards of our study data?

Data Standards and Data Sharing as part of the solution: Describing data consistently using data standards can bring clarity to data that amplifies its value. The T1D Data Standards Project builds on existing CDISC diabetes standards by describing concepts relevant to T1D in the areas of: • Pediatrics and Devices (Released Sep 2020) • Exercise and Nutrition (Due Q2-2021) • Screening, Staging and Monitoring for Pre-Clinical Type 1 Diabetes (Due Q3-2021). The team of CDISC standards experts, T1D experts from CDISC member organizations and T1D subject matter experts from academia and industry followed a consensus-driven process to ensure the final standards are fit for use within the research community, reinforced by the open community review.

How data standards help overcome some of the challenges: Publicizing the availability and promoting adoption of consistent standards brings clarity to data, reduces data collection, organization and analysis time, and facilitates interoperability that promotes data re-use and operational efficiencies. When researchers speak the same harmonized language, talking and understanding each other is much easier leading to the sharing of common, understandable data within the research community, thereby promoting scientific and therapeutic advances general confidence in the data.