Hi there! My name is Claire Ringer and I’m the undergraduate Digital Humanities intern at Oklahoma State University. This post was originally published on my blog, https://dh-intern.library.okstate.edu
Hello! It’s me again, your trusty Digital Humanities Intern, Claire. Today, I come to you with a more theoretical blog post, which I hope I’ve made easy to understand. Too often, I’ve read articles about digital humanities (DH) that go WAY over my head about all sorts of theoretical stuff (and, let’s face it, some not so theoretical stuff). Today I’m going to be discussion information literacy and how it applies to DH.
Before this semester, I had only heard the term information literacy in passing had never stopped to explore the meaning of the term. So, what is information literacy? After a quick Google search, I decided to define it this way: the ability to use the tools you have available to yield a result that you want. How about an example of information literacy in action? A student is asked to write a class research paper. The tools that they have access to are Google, their school library, their school’s online databases, and their teacher. Information literacy is the ability to use these sources, filter out the good and bad (or irrelevant) information, and write their paper. Let’s say the student is writing about clothing in Colonial Latin America and needs ten sources. She does a quick Google search and one item comes up. She goes to the library and picks out five books. She searches the databases, but nothing is forthcoming. Realizing that she needs more sources she talks to the teacher and is able to find four more sources. Now, the student has enough books but she still must determine what is relevant and what is not. For instance, if the student is writing about the colonial period, she shouldn’t include information about the post-colonial period. (Does this sound like an obscure topic? I agree, it is. Yet somehow, I find myself writing an eight-page paper on the subject.)
In DH, information literacy works in essentially the same way. DH researchers have a wealth of tools at their fingertips (if they know how to use them; learning those tools takes a lot of time and brain power). However, we must know how to use those tools to get what we want or solve a problem. And sometimes, that’s harder than you may think.
Here’s a real life example. This semester, I’ve been working on a project about the Women Accepted for Voluntary Emergency Service program (WAVES) in the 1940s. OSU was a training center for this program and the school has kept the 10,800 registration cards that the WAVES filled out when they arrived. These cards are chock full of information about the women’s hometowns, education, and age. We weren’t terribly sure what we wanted to do with this information, but my supervisors saw it as a great way for me to learn Tableau Prep and Open Refine (tools that they use a lot and which I talked about in a previous blog post) and continue honing my skills with Tableau. After spending many hours cleaning the data, I put it through Tableau to see what would show up. I created a variety of visualizations with the data but after a while, I was stuck and what the data showed were things I had learned from other sources. My primary supervisor was absolutely sure that she could figure out a way to show the date these women arrived in Stillwater and when they left. After spending quite a bit of time on it, she hit a dead end as well. So, my supervisors and I had a small conversation about information literacy and asked the question, “Can this tool actually help me?”
To solve this issue, I decided to start asking questions: questions that would help me not run into brick walls. Knowing the answers to these questions allows us to use our time wisely. In no particular order, here are my questions:
- What do we want to do with this data?
- How can we visualize this data?
- Can this data be visualized the way I want?
- What is the end goal? Do we want to show something or just count it?
- What do I think this data is saying? Can we find a way to show the data that proves or disproves my hypothesis?
- Do we need a visualization to prove the hypothesis?
- Can this tool help me answer the questions about my data?
- What tool will give me the visualizations I need?
- What are the limitations of this tool? Perhaps I’m not the intended audience for this tool- will that influence me ability to use this tool effectively?
- Are we trying to make the data look like I want it to look like, or are we allowing the program to do its own thing? Are we using the right tool?
Data Questions: in a project, we are given our data, and it is up to the researcher to turn that data into a visualization that proves or disproves the hypothesis. In my WAVES project, we weren’t sure what we wanted and the main goal was to teach me how to use those tools because they are useful tool for many projects. Looking at the data, I quickly saw a few visualizations that I could do, but overall, I didn’t feel like I needed a visualization to prove or disprove my hypothesis. However, maps and visualizations are incredibly useful for teaching others about your project.
Tool Questions: As I just discussed, I didn’t have many questions because I didn’t need to look at the visualizations to get an answer to my hypothesis. One pitfall we did have was trying to make the data do what we wanted. As I mentioned earlier, my primary supervisor wanted to see when people arrived in Stillwater and when they left. However, we quickly had to abandon the project as we were ‘trying to fit a square peg into a round hole.’ The data and the tool weren’t compatible for what we wanted to do. Because of the data, I’m not sure that there is a way to do that visualization, even with a different tool. Additionally, Tableau is meant for business people, not historians, so perhaps the tool wasn’t 100% perfect for us.
There are some scholars who say that visualizations allow researchers to see things that aren’t overtly obvious just by looking at the data up close. This is called distant reading. Personally, I don’t think that this is a watertight theory, especially in light of this project. While these visualizations did show me that the second largest section of WAVES came from the North East, I already knew from other sources that the majority of WAVES came from California and New York. Regardless, these visualizations are extremely valuable for teaching others about this program and helping others learn, which is what DH is all about. So, all in all, I’d say this project was a roaring success. Keep an eye out for some blog posts talking about these visualizations in more detail in the next few months!