The digital tools Voyant, Kepler.gl, and Palladio are handy tools for historians and researchers to use when analyzing digital material. Researchers use these tools to look at relationships within, find patterns and trends, and convey meaning and understanding from the research material. Researchers can do this because of the visualizations these three tools create making finding specific patterns easier. While the goals for why researchers use these tools are similar, the tools themselves are very different in their executions.
Voyant is a text-mining tool researchers use to convey meaning from a data set. It includes five tools researchers can use together to derive meaning from selected texts. The Cirrus tool is a word cloud that shows words that appear most frequently. Like Cirrus, the Trends tool is a graph visualization that shows the frequency of a specific word across the document. The Reader tool shows the document itself where users can select a specific word to find in the document. The Summary tool shows an overview of the specific word in terms of the document. Finally, the Contexts tool shows each time the document uses the word. These tools, used together, can find meaning in how words can tell a lot about a period, place, people, and culture.
Kepler.gl is a digital mapping tool researchers can use to discover patterns and trends across space. Even a simple point map can derive meaning from the data they are researching by showing the different relationships between those points. However, that is only part of what Kepler.gl can do with a map and data. Researchers can change the map to look at the data in different ways. Instead of points, one can change it to a cluster map to see the quantity of the data and compare them. A heat map can do the same thing, showing the density of the data. Other items can also be added, including a timeline showing how that data changed over time. All of this can give researchers more information than just traditional research.
Similar to Kepler.gl, Palladio can also make use of maps in a digital medium. However, the tool’s main component is using network graphs to show the relationships between different data. These graphs, overlaid on a map, can show the connections across space. Nonetheless, some of the most helpful information comes from looking at the network graphs themselves and limiting which data is present. Limiting the data can show different connections that otherwise go unseen in traditional research or are harder to see. For example, when I worked with Palladio, I limited the information to only look at topics discussed between male and female interviewees. While both groups shared similar topics, some were limited to a particular group. These topics tell me a lot about the subjects themselves and the culture at the time of their interviews.
While these three tools have different modes of executions, researchers should look at them in a variety of ways. Researchers can use these tools together to find meaning, patterns, and trends with the material and data they choose to research. While Palladio can use maps with the network graphs, it only shows a certain amount of information. A researcher should also include using Kepler.gl to get the information one can get from using a map that otherwise Palladio lacks. At the same time, Palladio also relies heavily on using words especially in the network graphs. Voyant makes the perfect complement to that program to delve deeper into using those words in the data.