Course content

Course 3. Modeling Meaning: How we structure humanities data

x Lessons

Estimated Time: ≈ 3 hours (full course, including all 5 modules)

Course Overview

How do we take something as complex and fluid as meaning and represent it in a structured form that computers—and humans—can understand? That is the central question of this course.

Modeling Meaning: How We Structure Humanities Data introduces the principles, practices, and philosophies behind data modeling in humanistic research. You will learn how scholars and practitioners design frameworks that express relationships between people, places, events, texts, and ideas: not as cold abstractions, but as living representations of culture and context.

Where the previous course, Understanding Data in the Humanities, explored what data is, this one examines how data takes shape and how those shapes reflect our interpretive choices, ethical responsibilities, and creative insights.

This course provides the conceptual foundation for your next step: Building Graphs in Neo4j, where you'll translate these ideas into a functioning, queryable knowledge graph.

What You'll Learn

By completing this course, you will be able to:

Explain how modeling structures data in the humanities (including key components: entities, relationships, and attributes).
Identify and compare major data representation paradigms (hierarchical, relational, graph).
Understand the role of interpretation, context, and ethics in modeling humanistic information.
Apply modeling principles to design your own small conceptual data model.
Prepare to implement your model in a graph database such as Neo4j.

Course Structure

#	Module Title	Summary
1	What Is a Data Model?	Introduces the concept of modeling as the bridge between raw data and meaning, showing how models act as interpretive frameworks that organize the world.
2	Entities, Relationships, and Attributes	Breaks down the grammar of modeling: the building blocks that describe "things," "connections," and "characteristics."
3	From Tables to Graphs: Ways of Representing Knowledge	Compares hierarchical, relational, and graph-based data models and shows why graph thinking is especially powerful for the humanities.
4	Modeling in the Humanities: Meaning, Context, and Ambiguity	Explores interpretive modeling, context, uncertainty, and ethics, highlighting frameworks such as CIDOC-CRM and TEI.
5	Designing Your Own Humanities Data Model	Guides you step-by-step in creating a conceptual model for your own humanities topic, preparing you for the next course on building graphs in Neo4j.

Why Modeling Matters

Modeling is how we give structure to complexity. It's what allows a library to catalogue books, a museum to document collections, or a digital humanist to connect songs, artists, and reactions within a larger web of meaning.

In the humanities, modeling is not simply a technical task: it is an interpretive act. When we decide what counts as a "song," what makes something a "theme," or how to represent audience "reaction," we are making scholarly judgments about meaning and relationships.

Understanding this process empowers us to design data that is:

Meaningful — grounded in human experience and interpretation.
Structured — organized for discovery, comparison, and analysis.
Transparent — open about assumptions, gaps, and uncertainties.

Course Philosophy

This course is built on a simple principle: Data is never neutral. It always reflects the questions we ask and the ways we see the world.

Modeling, therefore, is both a technical and a humanistic practice. It requires precision but also empathy, humility, and imagination.

Each module invites you to think like both a designer and a scholar: to see how structures of data can become structures of thought, shaping how we understand art, history, and human experience itself.

How This Course Connects

This course sits at the core of the Wellespring Project's initial learning sequence, connecting the conceptual groundwork of Understanding Data in the Humanities to the hands-on implementation of Building Graphs in Neo4j.

Series Progression	Focus
Course 1: Understanding Jesse Welles and the Wellespring Project	Context — exploring a human story through data and meaning.
Course 2: Understanding the Building Blocks of Knowledge	Foundation — defining and differentiating "data."
→ Course 3: Modeling Meaning: How We Structure Humanities Data	Framework — learning to represent complexity.
Course 4: Building Graphs in Neo4j	Implementation — constructing and querying actual data.
Course 5 (Future): Analyzing Networks of Meaning	Exploration — interpreting what the data reveals.