丁香园AV

data visualisation, faculty collaboration, community-based research, data production studies

New Insights into Research: Visualizing Faculty Collaboration

September 12, 2023

The data visualization project, led by Associate Dean of Research Dr. Nathalie Sinclair, allows us to select and create data in the form of a dynamic network. Co-designed and implemented by Quincy Wang, this innovative way of viewing research enables us to understand, process, and generate relationships and stories about the scholarly work we undertake in our non-departmentalized, interdisciplinary faculty.

Traditionally, we communicate faculty research through individual profiles that reify research as siloed and independent from other scholarly practices. In fact, most outstanding new knowledge results from clusters of researchers doing collaborative work (Gunawardena et al., 2019). DV, which is 鈥渧itally embedded in broader transformations of science, society, and culture鈥� (Cairo, 2020, p.17), offers not only a new way to increase external visibility and recognition of scholarly work and faculty service to the community, but a means to discover and explore cross-faculty areas of research collaboration. DV, then, becomes a method to understand and highlight relations among individuals across and within their various forms of practice, such as research, teaching, and service.

With that in mind, the offers three options for viewing scholarship in the Faculty of Education: research interests, graduate program involvement, and roles in supervisory committees. By using visualizations to shape individuals鈥� worldviews or their own experience (N忙rland, 2020), DV can provoke thought and spark new insights into collaborative efforts and the potential for faculty development. Our DV project is also driven by D鈥檌gnazio and Klein (2020)鈥檚 data feminism approach, which foregrounds two data principles: 鈥渆mbrace pluralism鈥� and 鈥渕ake labor visible鈥� (p.18). In this way, DV can foster an inclusive educational research culture as well as effectively present and value greater degrees of interdisciplinary collaboration.

Another powerful DV feature is , which showcases collaborative research projects in BC, Canada, the US, and internationally. Visiting the map and clicking on a number will show the title, funding agency, and names and institutions of investigators for each community-based research project. DV鈥檚 communication method also allows us to track past relations, see existing relations better, and unfold research potential, fostering a culture of inclusion and demonstrating the strength of knowledge co-creation in academia. Not least, the DV project enables views of faculty research that we might not otherwise track. For example, through visualization, we can gain insights into faculty members鈥� sense of belonging in the faculty, particularly in relation to opportunities for collaborative research.

By sharing data visualization as a means to explore and promote cross-faculty work, we contribute toward achieving three goals for knowledge co-creation: 1) develop a methodology and computational method to present faculty members鈥� collaborative work efficiently and meaningfully; (2) identify types and sources of open data to explore various data visualization tools that designers and researchers can easily build to inform department level policy; (3) continue to expand 鈥渄ata production studies鈥� research (D鈥檌gnazio and Klein, 2020, p. 183).

As Dr. Sinclair notes, the research network grows and changes over time as faculty members become very engaged in contributing to correctly situating themselves in this DV. 鈥淭hese DVs,鈥� she says, 鈥渕ay have affected faculty members鈥� sense of the faculty, particularly in relation to opportunities for collaboration. In terms of what we have learned,鈥� she continues, 鈥渢here are two main points. The first is that we realized how much data was quite easily available within the faculty, but not being used to guide any decision-making. The second point relates to the need to create data that is available but not necessarily suitably organized. The DV development process helped us re-think how we gather, organize, and store information with stakeholders鈥� needs in mind.鈥�

References:

Cairo, A. (2020). Foreword: The dawn of a philosophy of visualization. In M. Engebretsen & H. Kennedy (Eds.), Data Visualization in Society (pp. 17鈥�18). Amsterdam University Press. 

D'ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

Gunawardena, C., Frechette, C., & Layne, L. (2019). Culturally inclusive instructional design: A framework and guide to building online wisdom communities. Routledge.

N忙rland, T. U. (2020). 4. The political significance of data visualization: Four key perspectives. Data visualization in society, 63鈥�73.