Why is this labeled as a November update when it’s being posted in December? Simple: I drafted this post back in late November and it’s just taken me this long to get around to finishing it. Hence, technically an update from November.
Alright, I think I’ve pinpointed why my research for the Digital Scholarship Fellowship initially felt so strange and undefined. It has to do with the starting point(s) for research projects.
Project Starting Points:
Most research → starts with a research question and/or hypothesis. Example: How do government shutdowns affect government employment? (This was the central question for my project in Econometrics.)
Most of my research → starts by asking what data already exists. This is NOT a great approach for innovative research, but instead born from the limitations of the undergraduate experience i.e. I wanted to do research over Elizabeth Woodville, but all of the physical sources had been checked out from the library and never returned, so I switched my research to Billy the Kid since OU has the Western History Collections, where you can’t check out primary sources. Basically I 1.) Find a large dataset that 2.) I have immediate access to and 3.) I cannot lose access to the sources. This approach also saves time on things like econometrics research because you can focus on developing the model instead of figuring out how to get the data in the first place. I still don’t think this is a great way to do research, since it’s reactionary.
This research → starts by asking how do we get the data? What data is even out there? See, this is tricky. There isn’t necessarily a clear direction for the project as a whole, because any further inquiries will be dependent on how much and what types of data you have access to.
Why This Feels Weird:
In terms of long-term project planning, this approach of not knowing what we were looking for was a little frustrating because, well, it’s very difficult to develop a long-term plan for the project. We discussed possible results of our data collection—analysis, visualizations, pedagogical tools, websites—but we were mostly just stabbing blindly, still in darkness. I couldn’t rely on my usual “script” for projects. Because of this, I mainly focused on short term goals such as finding and learning how to use individual programming tools to collect/process data this semester. While I don’t think that was unproductive, it did feel less meaningful since I didn’t always know whether it would be relevant/useful for our end goal—there wasn’t yet an end goal to look ahead to and focus our experimentation around.
In spite of my initial issues with not immediately having an end goal, I ended up enjoying this approach to research. More importantly, I think this approach to research will become more and more necessary, particularly when it comes to research on big data and digital-born content. That’s why there are hundreds of tutorials on different methods for web scraping. That’s one of my favorite things about this fellowship: it allows me to try new approaches and techniques for research that I would avoid in other research settings due to lack of time, resources, and support.