Fostering collaboration through learning communities: a case report on engaging with All of Us data among library professionals, faculty, and students
DOI:
https://doi.org/10.5195/jmla.2026.2335Keywords:
All of Us, Interdisciplinary Collaboration, Data Services, Learning Communities, Group Learning, Teaching-focused Institutions, Less-resourced InstitutionsAbstract
Background: Research data services (RDS) have expanded in academic libraries but can be challenging to develop, particularly in teaching-intensive and less-resourced institutions. Learning communities offer a promising model for building skills, fostering collaboration, and aligning services with local needs.
Case Presentation: This case report describes the development and implementation of three learning communities—a library group, a faculty group, and a student group—at a teaching-focused institution. These communities brought together library professionals, faculty, and students from diverse disciplines—including health sciences, education, data science, and engineering—to collaboratively explore the All of Us dataset. By working with the same dataset, participants were able to move quickly from abstract concepts to hands-on practice, while developing a shared understanding of tools, workflows, and challenges. The learning communities also served as platforms for building institutional capacity in data-intensive research.
Conclusions: The learning communities model proved to be an effective strategy for fostering cross-disciplinary collaboration, promoting data literacy, and building institutional readiness to support research using the All of Us dataset. By centering on local expertise, learning communities provide a sustainable, resource-conscious framework for developing RDS. This approach also demonstrates how academic libraries can act as conveners and catalysts for equitable data engagement. Lessons learned from this case may inform similar efforts at other institutions seeking to build collaborative, inclusive models for engaging with various data resources.
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