Paper Digital Humanities Australasia 2018

Data Fluency: Interdisciplinary connections, skill development and building researcher capability (34)

David Groenewegen 1 , Linda Kalejs 1 , Sonika Tyagi 1
  1. Monash University, Frankston, VICTORIA, Australia

With a greater focus on cross-disciplinary research collaborations providing innovative solutions to complex world problems (Madden 2013), Universities are seeking to provide meaningful opportunities for graduate researchers, researchers and professional staff to develop skills and network across disciplines. Data Fluency at Monash University, is one such initiative, led by the Library and Bioinformatics Platform, providing like-minded and passionate researchers and graduate Researchers from all STEM and HASS disciplines to learn, share and help each other use digital research tools and interact in the digital enquiry space. The community of practice consists of a mix of Software and Data Carpentry, and Monash developed workshop curricula in using tools such as R, Python, Machine Learning, Data Mining and Visualisation; weekly drop-in sessions, tech talks and hackathons; as well as networking events and seminars. All elements have a focus on making connections across the university, building partnerships beyond traditional silos and skill development to enhance research.

Although Data Fluency is in its infancy, engagement and support from Faculties, eResearch, eSolutions is growing; the mailing list is rapidly increasing in size; and workshops are in demand, booked out within hours of release, with waiting lists. Proposals are underway to include cross disciplinary workshops as part of the Monash Doctoral Program, to enable both workshop participants and instructors to gain credit towards compulsory research training, alongside valuable teaching experience. Programs are in place to recruit and train instructors and facilitators, enabling further outreach across the research community. Various pedagogical approaches to teaching are being considered, with environmental scans from similar initiatives and programs across the tertiary sector informing best practice teaching and learning.

Connections within the University community are immediately evident, with the initiative forging alliances between disciplinary groups, Faculties and professional staff, creating community and an environment of sharing and learning across the humanities and STEM areas. Providing opportunities within the University for researchers to interact and learn across disciplines creates new approaches to problem solving, and increases levels of trust and interdependence between disciplinary research groups (Cummings & Kiesler 2005). Capturing and measuring the full impact of Data Fluency upon University research capability is yet to be observed, however, early workshop feedback demonstrates direct application of skills to research and teaching: “I will start using R in my research visualisations”; “I will provide insights into machine learning to my students”; “I now have confidence in employing tensorflow to make predictions on existing test datasets”; “ I now have an understanding how Python can be used for data analysis”.

  1. Madden, ME, Baxter, M, Beauchamp, H, Bouchard, K, Habermas, D, Huff, M, Ladd, B, Pearon, J and Plague, G 2013, ‘Rethinking STEM education: an interdisciplinary STEAM curriculum’, Procedia Computer Science, vol. 20, pp. 541-546.
  2. Cummins, JN and Kiesler, S 2005, ‘Collaborative research across disciplinary and organizational boundaries’, Social Studies of Science, vol. 35, no. 5, pp. 703-722.