Enhancing Collaboration Across the Research Ecosystem: Using Libraries as Hubs for Discipline-Specific Data Experts


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Computationally-intensive, cross-disciplinary research collaborations are difficult to create and maintain over time, though many yield impressive results. The need for researchers to share, maintain, and manage data is increasing, while also integrating new tools and approaches to make their work more impactful. The University Libraries at Virginia Tech has a team of disciplinary data and informatics consultants working to connect research environments on campus with emerging library services enabling collaboration across disciplines. Partnerships with university-level research service providers, such as high-performance computing (HPC) services and statistical data consulting, have presented interesting use cases and innovative solutions to common problems. While traditional library services may not overlap with high performance computing environments, new library services (such as data management, publishing, curation, archiving, and preservation) provide new avenues for collaboration and situate the libraries in a unique position in relation to research ecosystems. Moving large datasets from HPC environments into research environments present significant barriers to research data sharing between collaborators; working with libraries to make these datasets better organized and documented lowers some of these barriers. Discipline-specific informatics consulting allows researchers to integrate new tools and approaches to solve research questions. Here, we highlight the utilization, need, and scope of informatics and research data management services in and around libraries, while also providing examples of how these services have created new collaborations and adoption of improved research practices surrounding data management and integration of computationally intensive techniques (e.g. bioinformatics, humanistic informatics, etc.). This work lays the foundation for these services in an academic setting and the influence of such on the practice and experience of understanding data.