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Module 4. Data Across Disciplines: Humanities, Science, and Society

Course 2: What is Data? Understanding the Building Blocks of Knowledge
Estimated Time: 25–30 minutes

🧭 Module Objectives

  • Compare how data are defined and used in the humanities, sciences, and social sciences.
  • Describe different approaches to evidence, measurement, and interpretation.
  • Recognize shared challenges across disciplines, such as bias, uncertainty, and ethics.
  • Explain why interdisciplinary perspectives on data strengthen digital humanities work.

One Word, Many Worlds

"Data" is a word that crosses almost every field, but it doesn't mean the same thing everywhere.

Field Common Definition of Data Typical Goal
Natural Sciences Measurable observations of
the physical world.		 To test hypotheses and
establish general laws.
Social Sciences Quantified or coded
observations of human
behavior.		 To model trends and
predict outcomes.
Humanities Recorded traces of human
expression and experience.		 To interpret meaning
and context.

Each approach asks different questions and uses data differently to answer them. Science seeks certainty. The humanities often embrace ambiguity. Yet both depend on careful, ethical, and transparent handling of data.

The Scientific Ideal vs. the Humanistic Reality

In the sciences:

  • Data are gathered through controlled observation and experiment.
  • Standardization ensures that results can be replicated.
  • Objectivity is prized; subjectivity is treated as noise.

In the humanities:

  • Data often emerge from unique, non-repeatable events: a performance, a letter, a poem.
  • Context and interpretation are part of the data itself.
  • Subjectivity is acknowledged as part of the human condition, not an error to eliminate.

The difference is not about rigor but about purpose. Where scientists may ask, "What causes this?", humanists ask, "What does this mean?"

The Social Sciences in the Middle

Social sciences—like anthropology, sociology, and economics—bridge the gap. They quantify human activity but also rely on interviews, ethnography, and narrative. A cultural anthropologist's field notes and an economist's data table might both describe a village, yet they frame the world through different lenses.

Digital humanities often borrow from this mixed-methods approach, combining qualitative and quantitative insight.

📘 Example: A Wellespring Project analysis might count how often themes like "war" or "love" appear in Jesse Welles' lyrics (quantitative) and interpret how those words are used and felt (qualitative).

Shared Challenges Across Fields

Despite differences, all disciplines face similar tensions:

Challenge Description Shared Concern
Bias Human or systemic
influences on what is
measured or recorded.		 Recognize and disclose
assumptions.
Uncertainty Incomplete or
ambiguous data.		 Quantify or
contextualize
uncertainty.
Representation Whose voices or
phenomena are included
or excluded.		 Address inequality
in data sources.
Ethics Consent, privacy,
and responsible use
of information.		 Protect human dignity.

These shared challenges are why interdisciplinary literacy is so valuable. Understanding how other fields handle data helps humanists refine their own critical practices.

Thinking Across Scales

Data operate on many scales: from micro-level details to global trends. Humanists often focus on close reading (the singular artifact), while scientists and social scientists may often work with distant reading (aggregates and patterns).

Approach Typical Scale Example
Close Reading/
Microdata		 Individual, contextual, qualitative Analyzing one Welles lyric line.
Distant Reading/
Macrodata		 Aggregated, quantitative Mapping word frequencies across 100 songs.

Digital humanities thrive when they move between these scales: not replacing one with the other, but linking them to tell fuller stories.

Why This Matters for the Wellespring Project

Our work in the Wellespring Project draws on all three traditions:

  • From the sciences, we borrow precision and structure: nodes, relationships, data models.
  • From the social sciences, we inherit curiosity about networks, systems, and collective behavior.
  • From the humanities, we retain empathy, ambiguity, and the search for meaning.

Understanding these traditions helps us build a knowledge graph that is both analytical and interpretive: one that treats songs, people, and emotions as data, yet never forgets their human origins.

Key Takeaways

  • Every discipline defines and uses data differently.
  • The humanities emphasize context and interpretation rather than prediction.
  • All fields face shared challenges: bias, uncertainty, representation, ethics.
  • Interdisciplinary thinking allows us to approach data both critically and creatively.

Knowledge Check & Reflection

Suggested Readings & Resources

A LOT has been written about data over the past few decades, exploring the term from a range of different perspectives. We cannot provide a comprehensive bibliography on the subject but here are some particularly relevant sources for further reading:

Updated on Nov 6, 2025