Data Visualizations

This week I attended a Plotly data visualization workshop by PhD Candidate Matthew D. Lincoln from the Department of Art History at the University of Maryland. Plotly is a free web-based graphing tool for making data visualizations from small-to-moderate user-provided datasets. Groups can collaborate on projects directly through their Plotly accounts without having to send data back and forth through email. Datasets charted using Excel, MATLAB, Python, Tableau, and R can be easily graphed in Plotly and exported to several image formats, including pdf, png, eps, and jpg.

During the demo, users “forked and edited” Matt’s data table–data mined from the National Gallery of Art website’s HTML–to create their own visualizations. This histogram represents the number of works acquired in each genre of Dutch Baroque paintings by different NGA curators since the 1930s.
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View of the Plotly workspace with Matt’s data table. The user chooses which variables correspond with which axis based on the values and type of plot one has chosen to visualize. The settings here were used to plot the bubble chart below.
Screen Shot 2014-10-30 at 10.57.40 AM
A bubble chart visualizing the relative size of paintings acquired by different curators across the 20th century, plotted according to their creation date. This chart was plotted using the optional text column corresponding to artwork titles. When the user hovers over each bubble in the chart, the title of that painting appears. Plotly offers several theme options, seen in the left-hand column.

Visualizations of Humanities data allow us to quickly grasp a lot of bits of information that in the past might have taken a scholar years of toiling in archives and a whole article or book to document. In the bubble chart above, not only can we see when and for how long curators were acquiring works for the National Gallery of Art, but we also obtain an immediate impression of the relative size of each work, the range of dates each curator was interested in acquiring, as well as the rigidity or fluidity of their collecting preferences or opportunities. For example, the current curator Arthur K. Wheelock clearly has the most outliers in terms of size and range of creation dates represented among his acquisitions. This information then opens up many more questions for further research–questions the student or scholar might not have otherwise thought to ask–such as, what precisely accounts for these outliers in Wheelock’s collecting history? Changes in the art market? Personal preference? A desire to push boundaries? Shifting parameters in the field of Dutch Baroque art history?


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