Data Governance in Multimodal Behavioral Research

Abstract

In the digital era, multimodal behavioral research has emerged as a pivotal discipline, integrating diverse data sources to comprehensively understand human behavior. This paper defines and distinguishes data governance from mere data management within this context, highlighting its centrality in assuring data quality, ethical handling, and participant protection. Through a meticulous review of the literature and empirical experience, we identify key implementation strategies and elucidate the benefits and risks of data governance frameworks in multimodal research. A demonstrative case study illustrates the practical applications and challenges, revealing enhanced data reliability and research integrity as tangible outcomes. Our findings underscore the critical need for robust data governance, pointing to future advancements in the field, including the development of adaptive governance frameworks, innovative big data analytics solutions, and user-friendly tools. These enhancements are poised to amplify the utility of multimodal data, propelling behavioral science forward.