09.10.2020 | Digitale Akademie

Werkzeuge zur digitalen Verarbeitung geistes- und kulturwissenschaftlicher Information

Visualisation

Methods and tools for critical reflections on data

Slides: https://studiengang-digitale-methodik.pages.gitlab.rlp.net/modul-5/5c/slides/visualisierung/2020/

Sarah Pittroff, Aline Deicke | @digicademy | digicademy | CC-BY 4.0

Content

1. Theory
2. Tools
3. Hands-on
4. Wrap up

01

Theory

Visualization – what is it good for

• Tool for handling amounts of data or information that are either so large or so complex that the human mind cannot oversee them without external tools
• Generate knowledge through explorative analysis from large (unstructured) data corpora
• Finding errors in the data set

Visualization in the research process

1. Explorative Analysis
2. Confirmatory Analysis
3. Explanatory Visualisation

What can I display and how?

• Temporal Reference: Time Lines
• Spatial Reference: Data Maps
• Abstract reference: various types of diagrams
• Relational Reference: Graphs and Trees

Dot and Line Diagram 1

Number of job advertisements placed in the daily newspapers Passauer Zeitung und Kurier für Niederbayern in the period 1914–1918

Dot and Line Diagram 2

Length of "Liveticker" text about the Bundesliga provided on weltfussball.de in the period 2003-2019 Source: Liveticker – Evolution einer Textsorte aus quantitativer Sicht by Simon Meier-Vieracker

Data Map1

Data Map2

Time Series / Data Map

Charles Minard: Carte figurative des pertes succecives en hommes de l'Armée Française dans la campagne des Russie 1812–1813

Abstract reference 1

Box diagram showing a foldout chart from "Chronological history of the major floods of river Elbe since a thousand and more years" (in the period 1501-1784), by Christian Gottlieb Poetzsch 1784, Courtesy of Bayrische Staatsbibliothek

Abstract reference 2

Box diagram showing results of federal elections in germany 2017

Graph

Letter Network of the project „Fruchtbringenden Gesellschaft"
Data: correspsearch.net

Visualization of information

is complex interwoven in the research process

• 1. data sampling / data processing
• 2. algorithm based analysis
• 3. data visualization
• 4. hermeneutic interpretation of the visualization

Note

These four phases are interdependent.
Important for visualization is the consistency and comparability of the data.
Therefore, there is a risk of misinterpretations due to poorly designed visualization.

• Annotations make information in sources explicit
• Explicit information becomes machine-readable
• ... and visualizable by machine
• Next steps: Interpretation and Contextualization of the Visualized

→ Visualization is always a reduced model of the abstracted

02

Demo and Hands-on

Introducing data set

• Medieval Stained Glasses

Introducing tools for data visualization

• RAWGraphs
• Palladio

Data set

• CVMA csv
• CVMA tsv

Breakout rooms

A. Kuczera, Th. Kollatz, T. Schrade, DHd 2016 Leipzig: Methods and Tools for visualising Digital Humanities data sets

03

Wrap Up

• Do not believe in visualization, always critically question the data basis
• Data Criticism: Data are always an extract from an even more extensive mass of possible data.
• Modelling (simplification, shortening) of the "reality" → Data do not represent reality, they point to it

Literature & Software

Literature

• Roxana Kath, Gary S. Schaal und Sebastian Dumm, „New Visual Hermeneutics“, in: Zeitschrift für germanistische Linguistik 43/1 (01.01.2015). Online: Crossref, DOI: 10.1515/zgl-2015-0002.
• Malte Rehbein, Informationsvisualisierung. in: Digital Humanities: eine Einführung, herausgegeben von Fotis Jannidis, Hubertus Kohle und Malte Rehbein, S. 328-342, Stuttgart 2017.

Download

• Slides
• License: CC-BY 4.0, Marjam Trautmann, Sarah Pittroff