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Updated: Jun 14, 2025

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

Oscar E Ruiz1, Joost B Wagenaar2, Bella Mehta3

  • 1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.

Experimental Neurology
|June 7, 2025
PubMed
Summary
This summary is machine-generated.

Building effective data harmonization frameworks is crucial for large team science projects. This perspective offers guidance on creating data pipelines for integrated analysis across diverse research teams.

Keywords:
Big dataChronic painCommon data elementsData harmonizationData integrationJoint diseaseMetadata standardTeam science

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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.