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Developing Data Skills at Macquarie University Library

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Developing Data Skills at Macquarie University Library


Grai Calvey

Fiona Jones

Heather Cooper

Posted on 19 September 2019

Estimated read time: 4 min
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Developing Data Skills at Macquarie University Library

Posted by g.law on 19 September 2019 - 7:57am Man on computer in libraryPhoto by Harry Cunningham on Unsplash

By Grai CalveyFiona Jones and Heather Cooper

This blog post was originally published on the Library Carpentry blog


Drawing on Library Carpentry lessons, pedagogy and community.

In 2016, Macquarie University delivered a Data Science and eResearch Platform Strategy. In response, the Library embarked on a series of initiatives including:

  • Developing workshops to improve the data skills of all our library staff
  • To grow confidence when engaging in data science practice, eResearch conversations and the support of researchers

Our three goals were to:

  • Upskill library staff to be ready to meet the emerging challenges and demands of the University’s increased focus on data science and eResearch
  • Improve our understanding of the data science/eResearch environment as a whole, including language, tools, workflows, issues and needs, in order to engage in support for researchers at all levels
  • Improve our own work as library staff via better data management, transparent workflows and applying computational approaches to our work where appropriate

We drew on The Carpentries by:

  • Experiencing the four core Library Carpentry lessons as learners
  • Adapting material from the Library Carpentry lessons and pedagogy for our workshops
  • Tapping in to The Carpentries community on campus

We developed our community of practice via:

  • Short intensive face-to-face workshops
  • An online hub for sharing workshop and learning material, links to resources, and discussion
  • Regular hacky hour meetings for demonstration of tools and techniques, troubleshooting data difficulties, feeding back learning from webinars etc, and asking questions to inform future meetings

For our face-to-face workshops we created two bespoke Data Skills modules based on the Library Carpentry lessons Introduction to Data and OpenRefine. We began by writing competencies and learning outcomes for our own context, then assessed The Carpentries lessons to choose suitable content and activities. We used a flipped classroom model, using pre- and post- workshop activities for the introduction of concepts and practice via self-directed learning, with face-to-face workshop time used for “live-coding” demonstration, activities, and learner-centred discussion.

We have delivered Library Data Skills modules to three groups of library staff who attended two two-hour sessions each. A total of 52 staff members attended the training between December 2017 and February 2019. Each time we repeat them, there is an opportunity for members of our community of practice to ‘step up’, with our goal being to take people on a journey from learners > helpers > instructors.

We have kept evaluation of the lessons simple, using the traditional Carpentries one up and one down. This ensured that staff would do the evaluation – it’s an easy way to get valuable feedback. This method of evaluation is appropriate for Library carpentry which is a community of practice for sharing knowledge, and is not the same as formal training. As instructors, we constantly assess the sessions and readjust accordingly. With 2/3 instructors/helpers in the room during each workshop, we are able to debrief afterwards and use our observations, paired with learner feedback, to improve future sessions. At all times we are mindful that it is a community of practice and there are no experts – we are all learning.

We believe staff are now more aware that processes that they have been doing for years, like data manipulation/using spreadsheets, are identified as data skills. Staff have developed new data skills as well. Moving forward, our aim is to avail ourselves of opportunities to work collaboratively with researchers using data and to use these skills to improve our own daily work.

This work is licensed under a Creative Commons Attribution 4.0 International License.

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