Data Visualisation: The rising star in the Digital Humanities

(Parkins, 2017)

The term digital humanities (DH) refers to a wide range of ‘new’ media technologies that converge at the intersection of technology and humanities scholarship (Parry, 2012). Thus, in the era of digitisation and innovation, digital technologies fuel academic advancement. While it is indisputable that technology plays a critical role in humanities and scholarships, yet one wonders, how it is perceived and used by humanities scholars.

As a research student myself, I rely on various DH tools, including visualisation data to interpret and communicate data findings of my study clearly to the general audiences. In my experience, when exploring data visualisation tools, my cognitive horizons often reach a state of ‘AH-HA’ and very quickly, escalate to all kinds of euphorias: ‘Ooohhhs..’, ‘Ahhhhsss..’, and ‘Hmmm..??’. The recurring ‘tension’ when interpreting data visualisation naturally brings me to a state of disequilibrium, but in a good way. It provides a positive force to open up rich opportunities, critical analysis, and information I had otherwise missed!

Early data visualisations focused on snippets of information gathered to broaden humanity's understanding of the geographical world, for example, maps. Early data visualisations were also used to draw answers on issues related to public health. Nonetheless, its primary purpose is to discern valuable information or to draw a conclusion of a research study.

Research study is often associated with big data. And with such a big amount of data, how does one decide on which and how much data to best tell the story without losing its rigour, reliability, and validity? What are the socio-cultural effects that result from easy access to data? What does all this mean? It means, it is crucial for us, creator and user alike, to be data literate. But how can we learn to ‘read’ data effectively and ethically, and ‘communicate’ the data's inherent story clearly? The answers to these questions are never straightforward.

When thinking about the surrounding aspect of data visualisation related to ‘subjectivity’, Alberto Cairo, an information designer and professor in Visual Journalism at the School of Communication of the University of Miami, argues:

Charts and graphs are visual arguments that convey ideas. In order to understand any argument, students need to be aware of their own biases. So, one key technique […] is to learn to become more mindful. Ethical thinking is about not only intentions, but consequences (Bisoux, 2019).

It is, therefore, critical for researchers to adopt consequentialist ethics when it comes to creating data visualisations so that it does not mislead the general public, either on purpose or unintentionally. According to Cairo (2019), practice and discernment on the visualisation tools build one’s foundation to read and interpret data accurately, thus, leading one into becoming data literate.

To this end, let us examine an example of data visualisation by Nakano (2015) to emphasise the importance of a well-designed and accurately interpreted graphic can be like for an environmental occurrence. Takeo Nakano’s (2015) work:

‘extracts only proper nouns from news articles related to the Great East Japan Earthquake reported online between March 11th, when the Great East Japan Earthquake struck, and March 11, 2015, four years later. This is a visualization of the transition of words using the above parameters’.

(Nakano, 2015)

(Nakano, 2015)

Nakano, 2015)

The data visualisation from Nakano’s (2015) work demonstrates the important role of data visualization for humanities. It is intriguing, persuasive, attractive, and powerful. You may find out more about Nakano’s work here.

As mentioned earlier, laying the groundwork for a successful data presentation is critical. For this reason, I am leaving you with this e-book that I had found useful for an introduction to preparing and visualising information: Data + Design.

(Chiasson & Gregory, n.d.)

References

Bisoux, T. (2019). The ethics of data visualisation. https://bized.aacsb.edu/articles/2020/january/the-ethics-of-data-visualization

Cairo, A. (2019). How charts lie: Getting smarter about visual information (1st edition.). W. W. Norton & Company, Inc.

Chiasson, T., & Gregory, D. (Eds.), (n.d.). Data + Design: A simple introduction to preparing and visualizing information. http://trinachi.github.io/data-design-builds/titlepage01.html

Parkins, D. (2017). The world’s most valuable resource is no longer oil, but data. https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data

IBM. (2020). Text mining. https://www.ibm.com/cloud/learn/text-mining

Nakano, T. (2015). Transitional topics: 2011.3.11 – 2015.3.11. http://nakano-design.com/?p=8409

Parry, D. (2012). The digital humanities or a digital humanism. In M. Gold (Ed.), Debate in the Digital Humanities (pp. 429-437). JSTOR. https://www.jstor.org/stable/10.5749/j.ctttv8hq.27