Palladio is an analytical visualization platform developed by researchers at Stanford University to map correspondence, travel, and circulation of information by and among writers during the Enlightenment, situated in Western and Central Europe and European colonies in the Americas. But is an open-access platform, and invites general users to make use of its computational tools.
Palladio enables users to map and graph data of various corpus sizes. The data typically present information about historical events – people’s names, locations, gender, dates, and topics of interest or personal experience. Palladio uses an algorithm that plots data as nodes or identifying spaces on a map or a graph. Relationships between different kinds of nodes – people’s connections to other people; people and places of origin; women and the topics of their correspondence or transcribed conversations, etc. – may be plotted along their “edges,” or types of connections. Through their visualizations, Palladio’s maps and graphs facilitate understanding of the relational or associational patterns that connect otherwise discrete elements in data sets. Users may see answers, or suggestions of answers, to “big picture” questions about potentially very large sets of digitized information.
I practiced with Palladio using a data set of digitized records, previously prepared by Stephen Robinson, associated with Alabama slave narratives, created in the 1930s as oral interviews of former American slaves by the Works Progress Administration. The exercise consisted generally of creating visual maps of the intersections or correlations between a primary data set, which contained all metadata attached to the slave narratives, and secondary data sets that highlighted specific topics or attributes within the primary set.
Palladio allowed various maps and graphs of the data. The first was the relationship between places of enslavement and interview locations. The second was the relationship between particular interviewers and how many former slaves each interviewed. The third was a correlation between interviewers and the gender of interview subjects. The fourth, fifth, sixth, seventh, and eighth visualizations were correlations of the number of interview subjects, the gender of interview subjects, the age of interview subjects, he places where interview subjects were enslaved, and the identity of the interviewers, and the kinds of topics that former slaves talked about in their interviews.
It was somewhat perplexing to try to reposition nodes in the various visualizations to detect the meaning of the “edges,” or connections, between different variables, and the meaning of the relative sizes of the nodes, which shows the strength of the relationship between two different kinds of data. The most important relationship I noticed was the strong relationship between certain interviewers and numbers of interview subjects, which should alert a researcher to pay attention to interviewing or editing techniques that that interviewer may have employed uniquely.
Palladio as a tool to study patterns or networks of data really beckons researchers to ask different questions about sources than asked, or possible to ask, by previous generations. These new sorts of questions focus on meta- or macro-patterns in large data sets. But as the Palladio creators warned in a research report, (“ “Historical Research in a Digital Age: Reflections from the Mapping the Republic of Letters Project,” American History Review (2017), focus on questions about organization data networks comes, potentially, at the cost of interest in or appreciation of the intended content of the data, and/or the possible, or probable, exceptionality of a few elements in the data set. Cliometric and statistical analysis of historical data was popular in the 1960s and 1970s, before giving way to the “cultural turn” in historiography by the late 1980s and 1990s, with its focus on the meaning of important ideas. Can it be, merely, that how scholars think about what is important in the past, at the level of either the micro-history, focusing on the meaning of a single concept, like “republicanism” or “citizenship,” or macro-history, focusing on the contours of a community that may be only incidentally related through utterances of snippets of a common discourse, swings from generation to generation, like the proverbial women’s hem line?