Key Terms

In order to help you focus your reading and to serve as a mnemonic device, I have provided these key terms for each week of class. You will need to know these terms for the final exam. Please note that the definition I will request is not the dictionary definition of the term, but an elucidation of the term as we have used it in the context of the class: in our discussions, in our readings, and in our project work. You will be expected to cite relevant authors (though not exact quotes or page numbers) as well as class discussions.

Week One

digital humanities

The intersection of digital tools (videos, images, archives, maps) with the humanities, using technology to answer humanities questions. In the best cases, the two fields bring there differences together to create a bigger field than the two usually separated entities. We can expect there to be a tension between “the digital” and “the humanities,” but ideally, it’s a productive tension.


Deals with the study of what makes us human, which encompasses, art, culture, communication, morals, and philosophy. The study of human pursuits. The study of how people process and document human experience. Deals with observations that usually can’t be quantified, because they could be a feeling or a personal response.


Raw materials, including files, images, texts, and sounds, used in the course of research.


Getting your source into a computer and making it analyable (computationally tractable).


How do you present all the stuff that you’ve done to your sources? The design of a site or other digitized work, the “face” that the viewer (or listener) encounters.

Week Two


The story used to convey a set of information. Many stories are possible in the telling of history. Narrative forms the connective tissue between facts and documents.


Within the discipline of history, the belief that the truth of a matter is something that can be objectively and tangibly quantified. Michel-Rolph Trouillot objects to an entirely positivist approach because telling history involves a process of selection, and that process of selection involves power.


Within history, the notion that truth is constructed by a community over time and via a diversity of methods. The meaning we attribute to what happens. Michel-Rolph Trouillot critiques constructivism because sometimes the people telling the story won’t understand their own power biases, and it’s important to him as a moral issue that we remember that certain events took place.


A collection of records (representations of human activity that travel across time and space).

community archive

An archive that is put together outside of traditional institutional authorities. Institutional authorities might be motivated by goals that aren’t consistent with the community being documented. A way for communities to retain their own control over records while also preserving them.

Week Three

humanities research question

Question that’s bound in time, that makes meaning from events and activities that occur in the world around us. Question common assumptions about meaning or human cultural production. Finding new ways to understand culture and human interactions.


Data about data. Structured method of describing entities. Organizing data into subcategories so that people can find and analyze it more easily

controlled vocabulary

A list of allowed terms to describe something, meant to eliminate ambiguity, and make it easier to aggregate like terms.

Week Four


A method of organizing data that reflects someone’s (or some institution’s) point of view or perspective, which can create a lot of biases. An ontology is a data science concept but also a concept from philosophy, because ontology is understood to express something essential about a domain.


A group of people who are native to a certain region or area, who should be given the right to own and collect their own data. Because indigenous groups have often been colonized, their own ontologies have often been overwritten by colonial categories. Duarte and Belarde-Lewis argue that we should find a way to restore indigenous ontologies.

data visualization

A mechanical pictorial representation of data that makes it more digestible. Shows a pattern that might not be detected in the data’s “raw” form. Easily misleading.

Week Five


What’s in front of us, what the world is, material reality.


What you can record and take from the real world.

Week Seven (Mapping)

Mercator Projection

A world map that’s flat enough to represent the globe. Distorted to represent the United States and Europe as much bigger than they really are in proportion to other land masses.

Cartesian coordinates

A system of coordinates that represent exactly where a certain point is. An item’s position relative to the and y coordinates will tell you where it is. Latitude and longitude are examples of Cartesian coordinates. A standard way of describing location. In mapping it’s important to have a standard way of locating points. An example of how a Western European understanding of space has come to describe the entire globe.

Week Eight (Network Analysis)


Often represented as a point in network graphs. An individual entity with the possibility/capability of being connected to another entity. A visual representation of individual point in a dataset.


The connection between nodes, indicating a relationship. In a network diagram, often represented as a line.

one-mode network

A graph that contains entities, all of which are the same kind of thing.

two-mode network

A network graph that includes two distinct kinds of entities.


The importance of a particular node. There are many different ways of defining centrality, depending on what kind of network you’re looking at.

Week Nine (Machine Learning)

machine learning

An approach that uses algorithms to devise general rules from lots of data. We often say that with machine learning, computers “learn,” but in truth, computers are refining algorithms based on data. Machine learning models often reproduce and even magnify instances of bias already present in the data.