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.
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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