A Comparison of Hypothesis-Driven and Data-Driven Research A Case Study in Multimodal Data Science in Gut-Brain Axis Research

Data science, bioinformatics, and machine learning are the advent and progression of the fourth paradigm of exploratory science. The need for human-supported algorithms to capture patterns in big data is at the center of personalized healthcare and directly related to translational research. This paper argues that hypothesis-driven and data-driven research work together to inform the research process. At the core of these approaches are theoretical underpinnings that drive progress in the field. Here, we present several exemplars of research on the gut-brain axis that outline the innate values and challenges of these approaches. As nurses are trained to integrate multiple body systems to inform holistic human health promotion and disease prevention, nurses and nurse scientists serve an important role as mediators between this advancing technology and the patients. At the center of person-knowing, nurses need to be aware of the data revolution and use their unique skills to supplement the data science cycle from data to knowledge to insight.