Developing partnerships for academic data science consulting and collaboration units

Abstract

Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value.

What matters to graduate students? Experiences at a statistical consulting center from pre‐ to post‐COVID‐19 pandemic

“…The importance of at least some free access to statistical consulting is evidenced by the increasing proportion of graduate student projects in the SCCs portfolio when this was made possible due to the Graduate School's support. For the SCC's sustainability and growth and for high quality research outcomes, it is essential that funding sources are made available for university-specific priorities, such as providing statistical support and training to students and faculty across the university or support for grant proposal development (Huebner et al, 2023).…”

“…Mentoring graduate student consultants and supporting graduate student research is a key mission for many SCCs. A thriving SCC develops partnerships and networks across the institution, for example, with multidisciplinary teams, individual departments or colleges who seek to provide their members with access to statistical expertise (Huebner et al, 2023). One such partnership may be with the Graduate School to facilitate building capacity in statistical and computational skills of graduate students working on their theses or dissertations, to provide opportunities for graduate students to become expert consultants and collaborators or to support educational events, especially for responsible conduct of research in data science.…”

Tell your story: Metrics of success for academic data science collaboration and consulting programs

“…SDSPs differ in their mission, administrative home and funding models and may engage with different partners (Huebner et al, 2024). The content of any reporting will depend on the purpose and the audience.…”

“…The choice of success metrics depends in part on the strategic plan of the university and the SDSP itself (Huebner et al, 2024;Parker et al, 2021). Building support and enthusiasm for the SDSP may require convincing arguments for resources that resonate with university leaders.…”