Here are some links we’ll be using in exercises in the workshop. The workshop will be held in the Library IL Suite on the second floor of the Library on the Open University campus. The workshop slides are downloadable at Data Visualisation for CHASE Digital Scholars.
While getting started…
- Sign up for Many Eyes http://www-958.ibm.com/software/analytics/manyeyes/register
- Check out http://www.visualcomplexity.com/vc/, http://selection.datavisualization.ch and http://www-958.ibm.com/software/analytics/manyeyes/page/Visualization_Options.html for inspiration
- Network visualisations: Les Misérables character interaction presented as a force directed graph. or 20th century composers: making the connections
- N-grams: Google Ngram viewer and bookworm Open Library
- Trying entity recognition with Stanford tools or Voyeur Tools
- ‘Re-visions of Minard‘
- Visualising rich media: Mitchell Whitelaw’s Generous Interfaces
Scholarly visualisations to explore and critique
- Visualizing Emancipation
- Mapping the Republic of Letters
- Locating London’s Past
- GapVis Ancient Places
- Pelagios example ‘Meroe‘
- Lincoln Logarithms: Finding Meaning in Sermons uses Voyant for text mining, MALLET for topic analysis, Paper Machines and Viewshare to visualise the same dataset and is a useful way of exploring the strengths and weaknesses of different tools
- Some background on ‘distant reading': What Is Distant Reading?
Cleaning and preparing data for visualisations
- Formatting tips for ManyEyes http://www-958.ibm.com/software/data/cognos/manyeyes/page/Data_Format.html
- Wrangler: http://vis.stanford.edu/wrangler/
- OpenRefine (was Google Refine)
- Tool: ManyEyes
- Tool: Google Fusion Tables
- Tool: Tableau Public (Windows download)
- Specialist timeline tools include: Timeliner
- Specialist mapping tools include: CartoDB
- Specialist network visualisation tools include: Gephi
- Specialist tools for visualising collections of historical items include: Omeka and Neatline, Viewshare
- Specialist tools for topic modelling include MALLET, try Getting Started with Topic Modeling and MALLET
- Specialist tools for visualising statistical data include R
- Choosing visualisation types: Chart and image gallery: 30+ free tools for data visualization and analysis
- Finding visualisation tools for scholarly use: Project Bamboo
- Designing good visualisations: Stephen Few’s Effective Chart Design (PDF)
Exercise: visualising data
- Choose one of your datasets (or a subset)
- (If you don’t have a dataset, try http://www-958.ibm.com/software/data/cognos/manyeyes/datasets/cloud/tags or work with someone who has data)
- Decide: exploratory or explanatory? Static or dynamic? Small- or large-scale? Why?
- Choose a type of visualisation (map, timeline, chart, etc)
- Is your dataset in a suitable format for your visualisation type? How can you clean it? While you’re learning, you might want to start with just two fields – try combining dates or locations with another value.
- Optionally, sketch out your visualisation on paper to test it
- Choose one of IBM Many Eyes, Google, Tableau or other tools as appropriate for your chosen visualisation
- Try a visualisation and evaluate the results
- Is more cleaning or transformation needed? You may need to iterate with different versions of your data
Inspiration for future work and further reading
- Data Visualization Talks Online, 2010 post from Alark Joshi listing various videos
- Data and visualization blogs worth following (early 2012 post by Nathan Yau)
- If you want to learn more about technical topics, try The Programming Historian or Computational methods in the humanities
Books on information visualisation
- Few, Stephen. 2009. Now I See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
- Lima, Manuel. 2011. Visual complexity: mapping patterns of information. New York: Princeton Architectural Press.
- Moretti, Franco. 2005. Graphs, maps, trees: abstract models for a literary history. London: Verso.
- Stanton, Jeffrey. 2013. Introduction to Data Science. (ebook with code examples for learning R)
- Tufte, Edward R. 1983. The Visual Display of Quantitative Information. Graphics Press.
- Tufte, Edward R. 2007. Beautiful evidence. Cheshire, Conn: Graphics press.
- Ware, Colin. 2008. Visual Thinking for Design. Morgan Kaufmann.
- Yau, Nathan. 2011. Visualize this: the FlowingData guide to design, visualization, and statistics. Indianapolis, Ind: Wiley Pub.