A guide to developing harmonized research workflows in a team science context.

Area of Science:

• Multidisciplinary research
• Data science
• Biomedical research

Background:

• Large-scale, interdisciplinary team science initiatives are vital for tackling complex scientific problems.
• These projects aim to generate large, harmonized datasets for breakthrough discoveries using advanced methods.
• Harmonizing diverse datasets requires a robust supportive framework built by all involved members.

Purpose of the Study:

• To provide guidance on developing research-centered data collection and analysis pipelines.
• To enable downstream integrated analyses within and across diverse teams.
• To share collective experiences from the REstoring JOINt health and function to reduce pain (RE-JOIN) Consortium.

Main Methods:

• Establishing a shared language for cross-team communication.
• Implementing harmonized methods, protocols, and (meta)data standards.
• Developing common data elements and appropriate infrastructure for data harmonization.

Main Results:

• An effective data harmonization framework requires buy-in, team building, and significant effort from all members.
• Common challenges in data harmonization are often independent of specific research questions or techniques.
• The RE-JOIN Consortium's experience highlights the importance of project-specific frameworks and processes.

Conclusions:

• Developing robust data harmonization frameworks is essential for the success of large team science initiatives.
• Guidance is provided for creating data pipelines that facilitate integrated analyses.
• Collaboration and shared effort are key to overcoming data integration challenges in complex research projects.