Stat has just published a special issue on Equitable and Sustainable Models in Statistical and Data Science Consulting and Collaboration, co-edited by Emily H. Griffith and Mara Blake.
Their introduction is reproduced below.
In academic institutions, statistical and data science consulting and collaboration programs provide expertise to a variety of constituents to enhance data-intensive work across their respective campuses. Programs engage graduate students, faculty, staff, and even full-time professional consultants, depending on the unique needs and preferences of the college or university. Practices vary widely across institutions depending on the needs of the individual and the project as well as the available resources.
When delivered by faculty, staff, and/or students trained in both data science research skills (e.g. statistics, data management, data analytics and workflows, machine learning, visualization) and effective practices for inclusion and accessibility, consulting services can build a welcoming, diverse community of researchers who leverage the power of data science in their work while ensuring that the products of this work are developed with attention to accessibility and equity.
In a workshop on models in data science and statistical consulting in May 2023, funded by the Alfred P. Sloan Foundation, we felt distinct excitement around discussing the structure of consulting programs. While the gathered experts in this area noted the presence of literature on how to provide consulting, including the collection of articles on statistical consulting and collaboration published in a special issue of Stat last year, questions remained for practitioners in this area about the many things that might have to happen before working with someone needing data science or statistical help. How will that time be compensated? What administrative unit might provide a home for this type of work? Who will pay for it? What type of person should be hired to provide the consultation? What language should we use to describe this type of work? These questions occupied the participants in the Sloan funded workshop. In the case of this special issue, the unit of analysis is the program offering consulting and collaborations, not the consultation itself.
Despite the importance of this work, the literature has been sparse on best practices, or even any practices, on how to organize these efforts in a sustainable and modern way. This special issue presents a variety of perspectives and experiences with data science consulting for academic research, facilitating a broader understanding of the different approaches and best practices for implementing effective consulting programs in academic environments. The papers included in this special issue cover a variety of important topics, beginning with advocating for consulting and collaboration; followed by funding models; management, Inclusion, and leadership; developing partnerships; and students as consultants.
In each section, we’ve placed the broadest papers first, followed by papers addressing more specific aspects of each topic. We hope that this collection of articles will support those working in statistical and data science consulting and collaboration.
We are thrilled at the rich variety of programs and perspectives represented in this special issue. Publicizing and sharing best practices learned from experience will strengthen statistics and data science research infrastructure and support services within university libraries, academic departments, schools, academies, and institutes, and professional organizations.
We would like to thank the authors of both the submitted and accepted manuscripts for their time and dedication to these important ideas. We would also like to express our appreciation to the reviewers for their work in ensuring the rigor and quality of these articles. We are grateful to the team at Stat for hosting this special issue and highlighting this aspect of the field. Finally, we would like to thank the Alfred P. Sloan Foundation for funding the workshop where many of these ideas were formed and for all the participants in that workshop for their dedication to enhancing models in statistical and data science consulting.
Co-Editors: Emily H. Griffith and Mara Blake