Module 1. What is a Data Model?
Course 3. Modeling Meaning: How We Structure Humanities Data
Estimated Time: 25–30 minutes
🧭 Module Objectives
- Define what a data model is and explain its purpose.
- Describe how data models act as bridges between reality and representation.
- Identify examples of modeling in everyday and humanities contexts.
- Recognize that every model is interpretive: a reflection of human perspective and intention.
From Data to Design
In Course 2 (What Is Data?), we learned that data are human choices: selected, recorded, and interpreted fragments of the world. But once those fragments exist, we must decide how to organize them so that they make sense together. That organizing act—deciding what exists, how it connects, and what matters most—is called modeling.
A data model is a structured description of how information fits together. It tells us what kinds of things exist (entities), what they're like (attributes), and how they relate (relationships).
Modeling as a Way of Seeing
Modeling isn t just a technical process. It's a way of seeing. When you draw a map, outline a family tree, or organize your music library by genre, you're already building a model. A model highlights some features while simplifying others. It is a translation from the infinite complexity of reality into a form that can be stored, shared, and understood.
| Real-World Example | Simplified Model | What's Gained | What's Lost |
|---|---|---|---|
| A bustling city | A street map | Navigation clarity | Human stories, sounds, smells |
| A novel or song | A database of themes, lyrics, and people |
Searchability, structure | Ambiguity, emotional tone |
Every model reveals and conceals: it tells us what its creator thinks matters most.
Why Humanities Scholars Model
At first glance, data modeling might seem more suited to engineers or scientists. But humanists model constantly. Just in different forms (and often without even being aware that this is what they're doing!).
- A historian models the past when constructing a timeline or cause-and-effect chain.
- A literary scholar models meaning when categorizing themes, motifs, or character networks.
- An archaeologist models material culture when classifying artifacts by type and context.
The digital humanities simply make these models explicit and computational, allowing them to be visualized, queried, and shared.
To model is to interpret. — Johanna Drucker
Modeling doesn't replace analysis; it is analysis—made visible.
Data Models as Maps of Knowledge
A helpful metaphor:
- Data are the terrain — rich, complex, and detailed.
- The data model is the map — a simplified, structured view that helps us navigate.
Different maps emphasize different features (roads, topography, population).
Similarly, different data models highlight different relationships: authorship, influence, chronology, sentiment, place, and more.
In the Wellespring Project, data models help map Jesse Welles' creative world: linking songs, lyrics, themes, recordings, events, people, and audience reactions into a coherent whole.
This structure allows us to explore questions like:
- How do lyrical themes evolve over time?
- Which collaborators appear across multiple projects?
- How do online reactions relate to particular songs or performances?
Without a model, these connections remain scattered. With a model, they become discoverable and meaningful.
Models as Theories
Every model carries a theory about the world. When you decide that 'Song' connects to 'Artist' through a relationship called PERFORMED_BY, you're asserting something about causality and meaning.
In the humanities, this interpretive layer is not a flaw: it's the point. We model to make our assumptions visible, so that others can question, extend, or revise them. In this way, modeling becomes a collaborative and reflexive act: part analysis, part dialogue.
Key Takeaways
- A data model defines how information is structured and related.
- Modeling translates messy reality into a form we can explore and compute.
- All models are interpretive: they express choices, priorities, and values.
- In the humanities, models preserve complexity through explicit design and reflection.
Knowledge Check & Reflection
Suggested Readings & Resources
Core Conceptual Foundations:
Bowker, Geoffrey C., and Susan Leigh Star. Sorting Things Out: Classification and Its Consequences. MIT Press, 2000. — A foundational text exploring how the models we create (classifications) have real-world social and political impacts.
Weinberger, David. Everything Is Miscellaneous: The Power of the New Digital Disorder. Times Books, 2007. — A highly accessible book arguing that all organizational systems are choices that reveal and conceal information.
Data Modeling in the Humanities
Ciula, Arianna, Øyvind Eide, Cristina Marras, and Patrick Sahle. Modelling Between Digital and Humanities: Thinking in Practice. Open Book Publishers, 2023.
Drucker, Johanna. "Humanities Approaches to Graphical Display." Digital Humanities Quarterly 5 (2011).
Drucker, Johanna. Graphesis: Visual Forms of Knowledge Production. Harvard University Press, 2014.
Drucker, Johanna. "Data Modeling and Use." In The Digital Humanities Coursebook: An Introduction to Digital Methods for Research and Scholarship. Routledge, 2021.
Lavin, Matthew. "Why Digital Humanists Should Emphasize Situated Data over Capta." Digital Humanities Quarterly 15 (2021).
Owens, Trevor. "Defining Data for Humanists: Text, Artifact, Information or Evidence?" Journal of Digital Humanities 1 (2011).