Introduction

Data science, in its stubborn refusal to be defined or constrained around a coherent conceptual node, can be seen as a collection of skills, approaches and methods that have become relevant across a variety of research domains. While the departmental home for data science within the academic context continues to be debated (Donoho, 2017), the need for data science-related skills within the broader scientific research community has grown across domains and those researchers are actively searching for help (Osborne et al., 2014).

Data science is also a quickly growing academic degree and certificate area. Undergraduate degree programs and academic units hosting these programs are being strongly urged to develop faculty specializing in data science research and education (National Academies of Sciences, Engineering, and Medicine, 2018).

As much as the field of statistics may lament not becoming data science's de facto home, the reality is that this new domain has grown beyond the boundaries of any single department. This ownership debate will likely continue unabated as many academic disciplines recognize missed strategic opportunities and attempt to assert political control on their campuses. Looking beyond organizational chart intrigue, these new students and scholars will exist no matter how contentious their position is. Waiting for the debates to settle before acting to provide them with library and information services puts them at strong risk of being unserved or underserved for technical, data and other information services.

This entirely new subject domain and service population represent an exciting engagement opportunity for librarianship and information services. Not only are there patrons working directly in this new subject area, like undergraduates majoring in data science, but there are also indirect members sitting as affiliated faculty and other researchers seeking out data sciencealigned training. They have unique research needs around data discovery, technical services, research data management and scientific reproducibility. Libraries resistant to engaging with these new scholars and students face a similar missed strategic opportunity.

Given the complicated and bespoke nature of where these data science research, educational and service units live within academic campuses, hosting campus-level data science training opportunities and consultations within a university's library or a research unit within a library can be one of the most efficient methods of distributing that service.