Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data...

Area of Science:

• Biomedical data science
• Computational biology
• Bioinformatics

Background:

• Biomedical discovery faces hurdles due to diverse data types and fragmented analysis.
• Existing data workflows hinder data quality, sharing, and collaborative efforts.

Purpose of the Study:

• To identify and analyze common pain points in biomedical data tasks.
• To explore strategies for enhancing data quality, sharing, analysis, and collaboration.

Main Methods:

• Qualitative analysis of semi-structured interviews with fifteen biomedical professionals.
• Inductive-deductive thematic coding to identify emerging themes in research challenges and needs.

Main Results:

• Common challenges include data procurement, validation, applying new analysis techniques, navigating computational environments, and reproducible results distribution.
• Secure data sharing and collaborative facilities are crucial throughout the discovery process.
• Pain points identified offer opportunities to optimize research data lifecycles.

Conclusions:

• Actionable recommendations are provided to address multi-omic data challenges across biomedical discovery stages.
• Improving data management and collaboration can significantly enhance the efficiency and reproducibility of biomedical research.
• Addressing identified pain points is essential for advancing biomedical discovery and data lifecycle management.