Introduction

In recent years, as the growth of Data Science programs has proliferated, academic libraries have converged on reproducible research practices as a framework for extending and shaping data services for researchers and scholars at all levels. In addition to many service offerings, such as consulting on data management and data sharing plans for grant applications and data curation, library data service units are increasingly supporting, contributing and collaborating on services such as: open source programming languages; software and data documentation; robust project management and versioning; computational infrastructure and analytic environments; data/software repositories and archives; and critical instruction to promote information literacy in the classroom.

This chapter shares examples of how University of California, Berkeley library staff are collaborating with the Division of Data Sciences, Research Information Technology and other campus partners to support data science initiatives around the theme of reproducible research. The chapter provides some ideas for how reproducibility, as a professional orientation and practice, can pave the way for future services and collaboration between libraries and data science practitioners.

Research is inherently messy. Making research reproducible can be an added burden to a researcher's already complex workflow. The more libraries can help to minimize the labor of reproducible research practices, the greater impact they can have in the academy. At a high level, libraries can mean different things to different people, but at their core, libraries are community hubs for access and creation of knowledge. As researchers naturally gravitate to new forms of knowledge creation, libraries are tasked with a unique responsibility to facilitate these changes.

Traditionally, knowledge is captured in print and its digital counterparts. On the link between scholarship and scholarly articles, Claerbout and Karrenbach (1992) put it succinctly: ‘An article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result.’ This well-traveled quote raises an important point about research papers: that tables, figures and prose alone are simply not sufficient for auditable, verifiable and falsifiable science. While the reproducible science call to action must be met by those doing science, it does take a village to support those actions. Sayre and Riegelman (2018) and Stodden et al. (2013) both wrote about libraries being a catalyst for culture change ‘toward reproducible research’.