Feature ArticleA Comparison of Hypothesis-Driven and Data-Driven Research A Case Study in Multimodal Data Science in Gut-Brain Axis ResearchDreisbach, Caitlin PhD, RN; Maki, Katherine PhD, APRN Author Information Author Affiliations: Data Science Institute, Columbia University, New York, NY (Dr Dreisbach); and Translational Biobehavioral and Health Disparities Branch, National Institutes of Health Clinical Center (Dr Maki), Bethesda, MD. This research was supported by intramural research funds from the National Institutes of Health Clinical Center (K.M.). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Corresponding author: Caitlin Dreisbach, PhD, RN, 61 Claremont Ave, New York, NY 10027 ([email protected]). CIN: Computers, Informatics, Nursing ():10.1097/CIN.0000000000000954, October 14, 2022. | DOI: 10.1097/CIN.0000000000000954 Buy PAP Metrics Abstract 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. Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.