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.