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

Oscar E Ruiz; Joost B Wagenaar; Bella Mehta; Ilias Ziogas; Lyndie Swanson; Kim C Worley; Yenisel Cruz-Almeida; Alisa J Johnson; Jyl Boline; Jacqueline Boccanfuso; RE-JOIN Consortium; Maryann E Martone; Nele A Haelterman

doi:10.1016/j.expneurol.2025.115333

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

Exp Neurol. 2025 Jun 5:392:115333. doi: 10.1016/j.expneurol.2025.115333. Online ahead of print.

Authors

Affiliations

¹ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
² Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
³ Department of Rheumatology, Hospital for Special Surgery, New York, NY, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
⁴ SPARC K-Core, La Jolla, CA, USA.
⁵ College of Science and Engineering, Houston Christian University, Houston, TX, USA.
⁶ Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA.
⁷ Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Pain Research Center, National Center for Complementary & Integrative Health, NIH, Bethesda, MD, USA.
⁸ SPARC K-Core, La Jolla, CA, USA; Informed Minds, Inc, USA.
⁹ SPARC K-Core, La Jolla, CA, USA; Department of Neuroscience, University of California, San Diego, San Diego, CA, USA. Electronic address: mmartone@ucsd.edu.
¹⁰ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. Electronic address: nhaelter@bcm.edu.

Abstract

Large, interdisciplinary team science initiatives are increasingly leveraged to uncover novel insights into complex scientific problems. Such projects typically aim to produce large, harmonized datasets that can be analyzed to yield breakthrough discoveries using cutting-edge scientific methods. Successfully harmonizing and integrating datasets generated by different technologies and research groups is a considerable task, which requires an extensive supportive framework that is built by all members involved. Such a data harmonization framework includes a shared language to communicate across teams and disciplines, harmonized methods and protocols, (meta)data standards and common data elements, and the appropriate infrastructure to support the framework's development and implementation. In addition, a supportive data harmonization framework also entails adopting processes to decide on which elements to harmonize and to help individual team members implement agreed-upon data workflows in their own laboratories/centers. Building an effective data harmonization framework requires buy-in, team building, and significant effort from all members involved. While the nature and individual elements of these frameworks are project-specific, some common challenges typically arise that are independent of the research questions, scientific techniques, or model systems involved. In this perspective, we build on our collective experiences as part of the REstoring JOINt health and function to reduce pain (RE-JOIN) Consortium to provide guidance for developing research-centered data collection and analysis pipelines that enable downstream integrated analyses within and across diverse teams.

Keywords: Big data; Chronic pain; Common data elements; Data harmonization; Data integration; Joint disease; Metadata standard; Team science.