Data Scopes

• Coherent methods for using digital data in humanities research
• Data scope: you want to analyse a certain aspect of your materials,
  • but the “raw” data is not suitable for direct analysis.
• You have to do something with the data. Questions:
  • What do I have to do to make data suitable?
  • How do I do that?
  • What should I document of this process to I can share it with others?
  • Which parts of this process are specific to my analysis and which are generically applicable?

Example: project Legal Structures

• Analyse networks of internal references in national constitutions
• Investigate relations between network structure and legal, social and political variables
  • visualized networks look great and show very suggestive patterns,
  • but how do you know what you’re looking at?
• Questions:
  • Which constitutions? Only current or only first or all versions? What counts as a version?
  • When are two constitutions representing the same country?
  • What do you do with countries changing names? What do you do with countries channging borders?
  • What elements are we identifying in each constitution? References, logical structure of constitutions (individual articles, sections, chapters, …)
  • Only internal references or also external? What counts as a reference?
  • Do we treat references to individual articles the same as references to entire chapters? What about references to ranges of articles?

Focus on data interactions

• It’s not about specific tools
  • Many tutorials online for that, e.g. the excellent Programming Historian
• It’s about what you’re doing, how and why
• Translating research questions, assumption and interpretations to data interactions
• Consequences of interactions for questions, assumptions and interpretations

Frameworks

• Data Scope: select, model, normalise, link, classify
• Other models focusing on process:
  • Scholarly Primitives (John Unsworth): discover, annotate, compare, refer, sample, illustrate, represent
  • Data Visualization (Ben Fry): acquire, parse, filter, mine, represent, refine, interact

Data Scope Schema

Modelling

• “Heuristic process of constructing and manipulating models” (McCarty, 2004)
• Model:
  • “a representation of something for purposes of study,”
  • “or a design for realizing something new”
• Model determines what aspects of data to focus on
  • Structures data in sources around research focus
  • Transforms data, affects interpretation!

Data axes

• Defining data axes:
  • Persons, organisations, locations, dates, topics,
  • Themes, life courses, events, actions, decisions
• Defining categories or classes along those axes:
  • Roles of people and organisations
  • Periods, regions
  • Research stages:
• Model is updated as research progresses
  • This updating reflects growing insights

Selecting

• Which materials do I include? Which data elements do I focus on?
  • Data axes
• Algorithmic selection:
  • Everything matching a (set of) keyword(s)
  • Documents by type, creator, title, size, …
• What are consequences of these selections?
  • What am I excluding?

Normalizing

• Bringing surface forms expressed in data back to an underlying standard form
• Map variation onto a single representation:
  • E.g. linguistic variation, geographical and spatial variation
  • What is irrelevant variation?
  • Essential for next step: linking

Linking

• Establishing explicit connections between objects in the data sources that are not there from the beginning
  • Connecting representations of a single element across different documents or data sets
  • Connection different elements through relations
• Linking within a corpus:
  • Relations between people, between people and events, between topics and dates, …
• Linking across different corpora:
  • E.g. mentions of same person, location, date, …
  • Can bring together disparate data about single entity from different sources
• Allows querying across different types of source materials

Classifying

• Reduction of complexity by grouping (data) objects into predefined categories, or classes
  • Bringing together objects with similar properties
  • Separating objects with unlike
• Adds new layers of structure and interpretation to data
  • Especially useful for low-frequency items
  • Many data axes have “long tails” which are hard to structure
• Facets
  • multiple, indepdent classifications that can be combined
  • allows very flexible selection and querying

Domain and subject expertise

• All these activities require both:
  • Technical know-how of how digital tools handle data
  • Intimate knowledge of the domain and subject of source materials
• But also:
  • Reflection on how choices are informed by prior knowledge and experience
  • Reflection on how choices put emphasis on some aspects, while pushing back others
• Often requires collaboration…
  • How to organise that
• … and lots of discussion
  • Choices that one collaborator makes should be visible to the rest

#

Back to main program