Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management

Abstract

Abstract The application of new technologies in scientific research, particularly automated sensing of plant phenotypic performance, has resulted in a deluge of data and raised the question of how these data can be efficiently managed and shared. Many studies have examined the benefits and constraints of data sharing in different disciplines. We focus on plant phenotyping due to the increasing volume of digital data generated in multi‐disciplinary plant phenotyping research. Data sharing and reuse practices in plant phenotyping research have not been widely explored. Study results show that data sharing in plant phenotyping research occurs mostly through direct personal requests based on trust relationships and technical supplements (appendices) to publications, and researchers are willing to share data if incentives and policies are aligned to overcome the barriers. This paper provides empirical evidence to guide the establishment of incentive systems and policy frameworks that support FAIR (findability, accessibility, interoperability, and reusability) data, promote behavioral change, and enhance data sharing for the advancement of science and innovation by research communities, institutions, policymakers, and funders.