From Hermeneutics To Data To Networks: Data Extraction And Network Visualization Of Historical Sources

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

Network visualizations can help humanities scholars reveal hidden and complex patterns and structures in textual sources. This tutorial explains how to extract network data (people, institutions, places, etc) from historical sources through the use of non-technical methods developed in Qualitative Data Analysis (QDA) and Social Network Analysis (SNA), and how to visualize this data with the platform-independent and particularly easy-to-use Palladio.

This tutorial will focus on data extraction from unstructured text and shows one way to visualize it using Palladio. It is purposefully designed to be as simple and robust as possible. For the limited scope of this tutorial it will suffice to say that an actor refers to the persons, institutions, etc. which are the object of study and which are connected by relations. Within the context of a network visualization or computation (also called graph), we call them nodes and we call the connections ties. In all cases it is important to remember that nodes and ties are drastically simplified models used to represent the complexities of past events, and in themselves do not always suffice to generate insight. But it is likely that the graph will highlight interesting aspects, challenge your hypothesis and/or lead you to generate new ones. Network diagrams become meaningful when they are part of a dialogue with data and other sources of information.

Topics include:  

  • Introduction
  • About the case study
  • Developing a coding scheme
  • Visualize network data in Palladio
  • The added value of network visualizations
  • Other network visualization tools to consider

This tutorial is also available in Spanish at:

Authoring Person(s) Name: 
Marten Düring
Authoring Organization(s) Name: 
The Programming Historian
License - link to legal statement specifying the copyright status of the learning resource: 
Creative Commons Attribution 4.0 International - CC BY 4.0
Access Cost: 
No fee
Citation - format of the preferred citation for the learning resource: 
Marten Düring, "From Hermeneutics to Data to Networks: Data Extraction and Network Visualization of Historical Sources," The Programming Historian 4 (2015),
Primary language(s) in which the learning resource was originally published or made available: 
Also available in - other languages in which the learning resource has been translated or made available other than the primary: 
More info about
Keywords - short phrases describing what the learning resource is about: 
Community standards
Data analysis
Data coding
Data collection
Data visualization
Data visualization tools
Digital humanities
Historical data
Humanities data
Network analysis
Social anthropology data
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Arts and Humanities
Arts and Humanities: Digital Humanities
Arts and Humanities: History
Arts and Humanities: History of Art, Architecture, and Archaeology
Arts and Humanities: Other Languages, Societies, and Cultures
Published / Broadcast: 
Wednesday, February 18, 2015
ID - identifier that provides the means to locate the learning resource or its citation: 
Type - namespace prefix for the citable locator, if any: 
Publisher - organization credited with publishing or broadcasting the learning resource: 
The Programming Historian
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.
Target Audience - intended audience for which the learning resource was created: 
Citizen scientist
Data professional
Early-career research scientist
Graduate student
Mid-career research scientist
Research scientist
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)