For the last few months, I’ve been waist-deep in the Library of Congress’ MARC records — the digital cataloguing files that represent the…
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@scholarlyvision
For the last few months, I’ve been waist-deep in the Library of Congress’ MARC records — the digital cataloguing files that represent the…
Fantastic overview of ‘generous interfaces’ for cultural heritage collections, with lots of infovis examples along the way
A visualisation of the length of Terms and Conditions for different social networks. It’s enough to put you off Instagram for life! HT https://twitter.com/hailmika/status/992391607302451200
Topicgraph is a ‘new research tool to help you explore scholarly books’ from JSTOR
On March 31 1939, when John and Ruby Lomax left their vacation home on Port Aransas, Texas, they already had some idea of what they would encounter on their three-month, 6,502 mile journey through the southern United States collecting folk songs. This is a visualization of their journey and the folk artists they recorded by Aditya Jain.
Different ways of visualising Perceptions of Probability and Numbers, using ggplot2
The source data is responses to the question ‘What [probability/number] would you assign to the phrase "[phrase]"?‘ for phrases like ‘probably’ or ‘we believe’.
Outpacing pandemics has lots of interesting features for interactive visualisations.
Hat tip https://www.kaushik.net/avinash/create-high-impact-effective-data-visualizations/
there seems to be at times a fundamental incompatibility between digital historians' emphasis on the importance of process and the methodological opaqueness of most visualizations and databases. Any representation of data, no matter how well designed, is woefully incomplete. We need the data, encoding assumptions, data correction strategies, algorithmic disclosure, and methodological transparency. Historians would naturally scoff at any kind of analysis without adequate citation of evidence. The handling of data and its representations must be subject to the same critique.
New Forms of History: Critiquing Data and Its Representations, Frederick W. Gibbs
A chronology of various attempts through the last four centuries to visually organise and make sense of colour: from simple wheels to multi-layered pyramids, from scientific systems to those based on the hues of human emotion.
For fans of infovis history! Also a nice reminder of colour wheels you might have made in primary school...
Data is an abstraction, and it’s impossible to encapsulate everything it represents in real life. So there is uncertainty. Here are ways to visualize the uncertainty.
Understanding data visualisations is an essential skill in today’s data-driven society. But beyond technical considerations like accuracy and consistency, what makes a good visualisation and …
Some excellent examples of what happens when fashion images meet data mining at Robots Reading Vogue.
Text analysis of Trump's tweets confirms he writes only the (angrier) Android half - lots of detail on using R to analyse social media texts for sentiments expressed:
My analysis, shown below, concludes that the Android and iPhone tweets are clearly from different people, posting during different times of day and using hashtags, links, and retweets in distinct ways. What’s more, we can see that the Android tweets are angrier and more negative, while the iPhone tweets tend to be benign announcements and pictures.
Lisa Charlotte Rost: I just found this beauty at a U-Bahn station in Berlin: A subway map that shows the amount of passengers with line widths, from 1927. – at U-Bhf. Uhlandstraße
Search results in the Digital Austin Papers can be graphed by positive, neutral or negative sentiment, location and word counts.
Great post discussing some of the compromises and decisions to be made when visualising collections data on timelines, including arranging densely-packed images, dealing with fuzzy dates and date ranges and unevenly applied tags.