I began this week’s readings by starting with the Drucker piece entitled Humanities Approaches to Graphical Display. Drucker began by arguing “that even for realist models, those that presume an observer-independent reality available to description, the methods of presenting ambiguity and uncertainty in more nuanced terms would be useful” (Drucker 1). I appreciated this critique. In order to better understand the very complex reality of observer bias and decision-making in research, it is essential to critically consider the ways in which assumptions regarding “traditional data and graphical displays” are established.  Thus, by accounting for and establishing new methods of presenting nuances, we establish a necessary space for these observations that counter positivist “claims of certainty” in research.

Drucker then goes on to say that “data are capta, taken not given, constructed as an interpretation of the phenomenal world, not inherent in it” (Drucker 3). Assumptions regarding data and data collection as the one size fits all approach become problematic and evoke systemic power relations between those who are allowed or able to to “create” knowledge and thus who get to “receive” knowledge.

While I agree with Drucker’s critiques and the necessity for myriad methods of presentation that account for nuances (thus allowing for multiple viewpoints and the reality of observer bias), I couldn’t help leaving Drucker’s work with some questions.I found myself wondering:

• By using more ambiguity and uncertainty in the visualization of data are we making the data more accessible to a wider audience (a foundation that I believe is essential to the digital humanities field) or in fact, are we complicating it to the point of inaccessibility for those outside of the ivory tower? Can a person outside of academia understand these complicated images of data? Should they be able to?
• Is there such a thing as too much ambiguity or uncertainty?
• Further, are ambiguous and uncertain visualizations always the best way to represent data? Or are there sometimes cases when visualizations are unnecessary or more accurately represented in other digital methods?

I do not have any clear answers to these questions (would love to hear others insights on this). While I recognize the importance of ambiguity and uncertainty in data visualization, I wondered if by suggesting that we complicated these visualizations in order to account for nuances, we were also rendering them inaccessible to those who do not have access to the education or resources to understand them. Perhaps there is somehow a middle ground? Or perhaps certain visualizations are better explained in other ways utilizing other types of digital tools in order to create more accessible content? I feel strongly that, at the end of the day, accessibility should be a main focus in all digital humanities work, and thus data visualizations should not be only made for academic audiences or those who can understand their complexities. This does not mean that we should not address nuanced perspectives in our data visualizations, however we should keep in mind our own power and privilege as academics who have access to higher education. We must recognize and address our own positionally in our data visualization constructions. 

Moving on to Klein’s pieces, I really enjoyed her writing and the way she advocates for re-imagining silences in data visualization within The Image of Absence: Archival Silence, Data Visualization, and James Hemings. She states that “Illuminating this movement, through digital means, reframes the archive itself as a site of action rather than a record of fixity or loss.” (665) I liked the concept of a site of action or even–maybe more accurately put–these silences could be referred to as a call to action. The verb “call” designates a more demanding or forceful approach than a “site”.

When reading Klein’s piece I often related back to Choosing the Margin as a Space of Radical Openness by bell hooks. hooks states:

Understanding marginality as position and place of resistance is crucial for oppressed, exploited, colonized people. If we only view the margin as sign marking the despair, a deep nihilism penetrates in a destructive way the very ground of our being. It is there in that space of collective despair that one’s creativity, one’s imagination is at risk, there that one’s mind is fully colonized, there that the freedom one longs for as lost. (hooks, 207)

I want to say that these margins have been both sites of repression and sites of resistance. (208).

Reimagining these spaces that are known solely for their systemic injustices (as Klein has done through addressing silences and her data visualization of The Ghostly Story of James Hemings) offers the potential for us to gain a more accurate and thorough understanding of the past, present and future. These silences are places of repression, but also resistance for data visualization.