Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders.

Biomedical discovery is fraught with challenges stemming from diverse data types and siloed analysis. In this study, we explored common biomedical data tasks and pain points that could be addressed to elevate data quality, enhance sharing, streamline analysis, and foster collaboration across stakeholders. We recruited fifteen professionals from various biomedical roles and industries to participate in sixty-minute semi-structured interviews, which involved an assessment of their challenges, needs, and tasks as well as a brainstorm exercise to validate each professional's research process. We applied a qualitative analysis of individual interviews using an inductive-deductive thematic coding approach for emerging themes. We identified a common set of challenges related to procuring and validating data, applying new analysis techniques and navigating varied computational environments, distributing results effectively and reproducibly, and managing the flow of data across phases of the data lifecycle. Our findings emphasize the importance of secure data sharing and facilities for collaboration throughout the discovery process. Our identified pain points provide researchers with an opportunity to align workstreams and enhance research data lifecycles to conduct biomedical discovery. We conclude our study with a summary of key actionable recommendations to tackle multiomic data challenges across the stages and phases of biomedical discovery.