Integrating data science into the translational science research spectrum: A substance use disorder case study. Issue 1 (19th August 2020) - Slade, Emily; Dwoskin, Linda P.; Zhang, Guo-Qiang; Talbert, Jeffery C.; Chen, Jin; Freeman, Patricia R.; Kantak, Kathleen M.; Hankosky, Emily R.; Fouladvand, Sajjad; Meadows, Amy L.; Bush, Heather M.
Abstract: The availability of large healthcare datasets offers the opportunity for researchers to navigate the traditional clinical and translational science research stages in a nonlinear manner. In particular, data scientists can harness the power of large healthcare datasets to bridge from preclinical discoveries (T0) directly to assessing population-level health impact (T4). A successful bridge from T0 to T4 does not bypass the other stages entirely; rather, effective team science makes a direct progression from T0 to T4 impactful by incorporating the perspectives of researchers from every stage of the clinical and translational science research spectrum. In this exemplar, we demonstrate how effective team science overcame challenges and, ultimately, ensured success when a diverse team of researchers worked together, using healthcare big data to test population-level substance use disorder (SUD) hypotheses generated from preclinical rodent studies. This project, called Advancing Substance use disorder Knowledge using Big Data (ASK Big Data), highlights the critical roles that data science expertise and effective team science play in quickly translating preclinical research into public health impact.