While they sound vastly different from each other,\u00a0Yale librarians,\u00a0Peter Leonard<\/a>\u00a0and\u00a0Lindsay King<\/a>, brought these terms together by creating Robots Reading\u00a0Vogue.\u00a0<\/em><\/a>From grant receiving n<\/em>-gram Search project to student ran projects, Robots Reading\u00a0Vogue<\/em> explores the century-old magazine’s history, contents, and trends that account for its longevity.<\/p>\n But does fashion even contain quantified data that can be analyzed? More so, how are robots reading\u00a0Vogue?<\/em> These are some of the questions I would like to answer while reverse engineering some of these projects.<\/p>\n First the sources. Leonard and King were only able to create such projects because\u00a0Vogue<\/em> was digitized. The\u00a0Vogue Archive<\/a>, launched by ProQuest and Cond\u00e9 Nast in 2011, contributes\u00a0over 2700 covers, 400,000 pages, 6 TB of data. That’s right. Fashion can be quantified, and each page and each pixel are single data point out of the vast data set. Besides the Vogue Archive, Leonard and King used\u00a0Memos: The Vogue Years, 1962-1971,\u00a0<\/a><\/em>which contains over 200 memos of\u00a0Diana Vreeland, who was editor-in-chief of American\u00a0Vogue.\u00a0<\/em>These memos were used to created the Diana Vreeland Memo Generator<\/i><\/a>\u00a0– a random memo generator based on Vreeland’s writing style.<\/p>\n Second processing. These robots were actually reading Vogue <\/em>(the data set) and analyzing differently for each projects. For example, Markov Chain model<\/a> was used to generate Vreeland-esque memos. By analyzing the occurrences of words, the generator suggests new strings of words, which copies the sentence structure, length, and punctuation of Vreeland. In\u00a0Averaging Covers in\u00a0Vogue<\/a>, <\/em>a decade worth of\u00a0covers are overlaid and a mean RGB value for each pixel is generated. This project explores the general trend of the covers, in which can also contribute to customer behaviorism studies. Further projects used outside tools such as Bookworm which is the open-source version of the Google N-Gram Viewer,\u00a0learning.py<\/a>,\u00a0ffmpeg<\/a>,\u00a0Imagemagick,\u00a0<\/a>and more.These analysis led to the less intimidating and manageable data that we can view on Robots Reading\u00a0Vogue\u00a0<\/em>today.<\/p>\n Third and last, presentation. Robot Reading\u00a0Vogue<\/em> focuses on organized visual presentation. For\u00a0Slice Histogram<\/a>\u00a0<\/em>project, the graphs are compiled into a video. The video presentation allows for easy visualization of the evolution of color trends in\u00a0Vogue.<\/em>\u00a0<\/em> Other visualizations include interactive graphs where users can explore different statistics of the magazine, which also indicates the era of each editor-in-chief.\u00a0Vogue<\/em>\u00a0Covers in Colormetric Space<\/a> provides a zoomable graph, and a demonstration of an interactive graph that can compare different values of color.<\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n Robots Reading\u00a0Vogue<\/em> depicts what digital humanities represent – the cross section of humanities and technology. It provides fun and easy-to-approach analysis that cannot be performed without the help of digital technologies. But the main point of this project? Robots actually like to read\u00a0Vogue.\u00a0<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Data mine – the action of extracting information and compiling for further analysis. 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