Humanities research does not have to be done manually! This page describes some computational methods that can be applied to humanities research questions, along with related tools available in the Scholarly Commons and online.
Digital Humanities Research Methods
Generating visualizations is a way to “see” your data. A common example of this is network analysis, which generally refers to visualizing relationships between multiple entities (more below). You can also visualize results of text analysis (like word frequencies or topic models) or image analysis (like concept mapping).
See the library guide on Data Visualization.
Geographic Information Systems (GIS)
Using GIS methods in digital humanities is sometimes called the “spatial humanities,” and utilizes interactive maps to assist in better understanding the concept of “space” in relation to humanistic research. Answers to questions such as “How does space influence events?” or “What has this space meant in history?” can potentially be represented with GIS software. For more on spatial data services at the Scholarly Commons, see the GIS Information and Data Discovery pages.
Text Mining and Analysis
Text mining refers to the process of computationally analyzing large bodies of texts. One commonly used method is called Topic Modeling, which groups words that frequently occur together into a “topic.” Computational text analysis allows a researcher to study larger amounts of text than would normally be possible with traditional textual research techniques.
Network analysis is a type of data analysis and visualization, which explores and displays the relationships between entities. This analysis can be used to track how they influence, relate to, or even predict one another.
Tools available in the Scholarly Commons: R, Python
Multimedia analysis is the process of extracting information from digital images, audio, or video utilizing digital automated processing techniques. For example, teaching a computer to tag videos on its own (using machine learning), or comparing image content across hundreds of images.
Tools in the Scholarly Commons: Digital Scanners