When I started working through this week’s readings and activities, the last thing I anticipated was gaining a neww appreciation for bar charts. I’ve always been frustrated with all of the options in trying to represent data, and when working with programs such as Excel I’ve always been under the impression that the more flashy the graph the better. However, this week I developed a better understanding of the importance in weighting the clarity of data over more aesthetic choices as well as how those aesthetic choices influence an audience’s interpretation of the data.

In Johanna Drucker’s essay “Humanities Approaches to Graphical Display,” she outlines the various ways in which digital humanists can make use of data to create visualizations while maintaining awareness of the problems or implicit biases built into these tools. Drucker calls attention to the idea of data as capta, a concept that appears repeatedly throughout this unit:

Capta is ‘ taken’ actiely while data is assumed to be a ‘given’ able to be recorded and observed. From this distinction, a world of differences arises. Humanistic inquiry acknowledges the situated, partial, and consitutive character of knowledge production, the recognition that knowledge is constructed, taken, and not simply given as a natural represntation of pre-existing fact.

Drucker walks readers through the way data is constructed and the various assumptions made when visualizing it. She uses examples of time and temporality to contrast how humanists might perceive these topics and need more freedom in visualiztions versus the way scientists and social scientists may view them. Drucker poses a potential model for humanitists creating visualizations, and invites them to embrace the ambiguity of humanities data rather than hiding it in existing representational models.

Steve Braun’s article “Critically Engaging with Data Visualization through an Information Literacy Framework” picks up this idea, and suggests that possibly the ACRL framework used by librarians. The framework consists of six different “frames,” but in the case of digital humanities visualization Braun argues that “authority is constructed and contextual” and “information creation as a process” are the most crucial in using data as humanists. He also breaks down a series of design dichotomies of visualization to better assess meaning. Braun describes a “Choose Your Own Adventure” book that he uses with students to encourage them to consider data visualizations as “forms of dialogue rather than statements of fact,” and I hope I might have the opportunity to incorporate this kind of activity into my own pedagogy at some point.

In “Racism in the Machine: Visualization Ethics in Digital Humanities Projects,” Katherine Hepworth and Christopher Church address the biases built into many digital tools. They give the example of TayTweets, and discuss how an algorithm was quickly able to learn racism and hatred from the internet before pointing to the fact that all data visualizations are algorithmic. By comparing two digital mapping projects that focus on lynchings in America, Hepworth and Church are able to point to the ways projects can communicate similar information in different ways–ranging from the selection of data used, the explanations provided for the choices made in creating the project, and the aesthetic elements of the visualization.

The section on visualizations in Exploring Big Historical Data: The Historian’s Macroscope provides introduction to different kinds of visualizations, the different kinds of data that can be visualized, how certain elements of a visualization can influence audience interpretation, and tips on how to make a visualization as impactful as it can be. The case studies on the “Six Degrees of Francis Bacon” networking project and Michelle DiMeo and A. R. Ruis’s work with epistemic network analysis (ENA) show the nuances of data visualization in practice. I’ve always wanted to try out a network visualization project ever since I did a Gephi workshop, so I was interested in examining the pros and cons and learning the differences in different kinds of networks .

I end with the bar graph I created with the same data from the maps I used last week in my attempt at making a choropleth map; however, I took the advice of several of the articles and narrowed down the scope of the data when representing it visually. I have a new appreciation for the simplicity and clarity of bar charts, and how they make data more digestible for a wider audience.

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