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Lessons learned and recommendations for data coordination in collaborative research: The CSER consortium experience.

Kathleen D Muenzen1, Laura M Amendola2, Tia L Kauffman3

  • 1Department of Biomedical Informatics and Medical Education, Division of Biomedical and Health Informatics, University of Washington Medical Center, Seattle, WA, USA.

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|June 16, 2022
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Summary

Data coordination in multi-site research requires early planning and continuous communication. This study shares lessons learned and recommendations for harmonizing survey and genomic data in collaborative research consortia.

Keywords:
clinical researchdata coordinationdata governancedata harmonizationdata managementdata sharingmedical genomicsresearch collaborationresearch informatics

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Area of Science:

  • Data Science
  • Genomics
  • Bioinformatics

Background:

  • Integrating data across heterogeneous research environments is a significant challenge in multi-site, collaborative research.
  • Achieving dataset interoperability is crucial for maximizing the benefits of collaborative work, despite variations in data collection protocols.
  • Limited standards exist to guide the data coordination process from project conception to completion.

Purpose of the Study:

  • To describe the experiences of the Clinical Sequence Evidence-Generating Research (CSER) consortium Data Coordinating Center (DCC) in coordinating harmonized survey and genomic sequencing data.
  • To identify lessons learned and formulate recommendations for future research consortia regarding data coordination.
  • To address challenges in data harmonization, communication, informatics, compliance, and analytics in multi-site research.

Main Methods:

  • The CSER Data Coordinating Center (DCC) coordinated harmonized survey and genomic sequencing data from seven clinical research sites (2020-2022).
  • Input was gathered from multiple consortium working groups and CSER leadership.
  • Lessons learned were identified and distilled into recommendations for future research consortia.

Main Results:

  • Fourteen lessons learned were identified across communication, harmonization, informatics, compliance, and analytics.
  • Eleven recommendations were developed for future research consortia in planning, communication, informatics, and analytics.
  • Early planning and budgeting for data coordination are crucial to minimize downstream complications.

Conclusions:

  • Clear, reciprocal, and continuous communication between stakeholders and the DCC is vital for a secure informatics ecosystem.
  • Proactive interrogation of data governance approaches is important, especially for research spanning clinical and research domains.
  • Implementing these recommendations can enhance data coordination and interoperability in collaborative research projects.