Twinsets & toolbelts - developing library data skills aka library carpentry at Macquarie University Library

BACKGROUND: In 2016 Macquarie University delivered a Data Science and eResearch Platform Strategy to “provide ...a foundation of enabling data science and eResearch expertise, systems, policies, technologies and support”. In response to this development, in 2017, the Library embarked on a series of initiatives, including developing workshops, to improve the data skills of all our library sta ; to grow confidence when engaging in data science practice, eResearch conversations and the support of researchers in the new paradigm.

METHODS: In 2016 a small group of staff began a community of practice approach to the development of data skills. These early adopters were introduced to Library Carpentry lessons: Introduction to Data, OpenRefine, Unix Shell, and GitHub. These lessons were used as the basis for our 2017/2018 workshops. Learning outcomes included the development of a set of core competencies in engaging with, manipulating, analysing and managing data. The Data Skills workshops, along with an online community of practice hub and regular hacky hours, form the foundation for a strategic approach to managing both the support the Library offers researchers, and improving internal data processes and work analysis. FINDINGS: Workshop attendees from across the Library demonstrated competence with the essential elements of data management including data description, le organisation and inputting and extracting information from systems. One benefit of the workshops was the repositioning of known processes as data skills, and the encouragement of staff to build on these skills and understanding. Issues remain in ensuring the further development and maintenance of data skills through practical application.

DISCUSSION: Future readiness in this new reality of data science requires agility and a willingness to build an understanding and momentum simultaneously, in a field that is rapidly evolving. Strategically, this requires managers to “envision the diverse contexts, opportunities, and bene ts in applying data science methods” Burton et al (2018). This includes encouraging library staff to ‘be in the space’, championing those who are, and supporting exploration of data skills and tools.