Introduction

From its origin in Stanford University’s Mapping the Republic of Letters Project in 2008, I sought to involve both undergraduate and graduate students in the collaborative work of the Grand Tour Project. The project got its initial data boost when Stanford graduate students Sarah Murray and Molly Taylor-Poleskey worked on the first manual data retrieval from A Dictionary of British and Irish Travellers in Italy, 1701–1800, compiled from the Brinsley Ford Archive by John Ingamells;¹ they remained involved in the project even after they embarked on academic careers elsewhere, carrying digital humanities expertise with them. Only a year into Mapping the Republic of Letters, the students in Jeff Heer’s CS448b Data Visualization class at Stanford produced an award-winning interactive visualization tool based on the project’s data, one of the first steps in the visualization path that led to the development of Palladio.² In the summer of 2010, Mapping the Republic of Letters was working with Kofi Ohene-Adu, then an undergraduate student in computer science, to make sense of the Grand Tour data in particular. Such relationships were essential to our transdisciplinary and transgenerational collaborations, and the founding in 2012 of the Center for Spatial and Textual Analysis (CESTA) meant that student participation could be formalized through the Center’s undergraduate research program. Many undergraduates have since interned with me on the project—three worked as developers well beyond their time at Stanford—and still more graduates have been critical collaborators.

But I also worked to bring the Grand Tour Project into the classroom, teaching courses based in my home department of Classics and cross-listed with many others (including History, English, and Modern Languages and Cultures). Such is the transdisciplinary nature of Grand Tour studies. This teaching experience and related materials—much of it developed together with graduate students and assistants—is shared here in the hope that it will be of use to or inspire others in their own teaching. Courses in digital humanities are now established in many humanities curricula, while data science programs are emerging and expanding the reach of what statistics classes used to be. All these programs promote data literacy as a necessary skill for students today—with attendant concerns about which data sets to use in the classroom, whether or how much computer programming should be included, how to promote project- and collaboration-based teaching, how to use open-access resources, how to include ethics and the humanities in the fundamentals of data work, and how to teach visualization as part of the interpretation of data.³ In this evolving context, my graduate TAs and I experimented with teaching the Grand Tour, combining data and historical work in ways that remain fundamental to humanistic inquiry.

Before the Grand Tour Explorer

I first brought the Grand Tour Project into my teachng for a course called “Modern Journeys in Ancient Lands” in 2011. This was before the Explorer was built and before a usable dataset existed. At that time in the project, we were immersed in figuring out best practices and structures for manual data extraction while simultaneously harnessing visualization as a means of exploring and understanding early modern travel. Although there was no ready database to share with the students, the class was already centered on the idea of the print Dictionary itself as a data source that we could study in conjunction with primary and secondary materials on eighteenth-century travel to Italy. The first part of the class covered the history of the Grand Tour and introduced the Dictionary, contextualizing its representations of travel to Italy. The second part of the class was project-driven: each student selected an eighteenth-century travel narrative as a primary source and then engaged in depth with it. The Dictionary could help to identify possibilities, with ECCO (Eighteenth-Century Collections Online, available through our university library) offering a wide range of published eighteenth-century books from which to choose. Each student’s task was to identify a research question that they would explore through data visualization, with a final project that would entail both writing a short paper and devising explanatory and exploratory images.

These final projects required a great deal of dedication and specialized support. Indeed, the collaborative ethos at the core of digital humanities projects operated also in the classroom setting: the class TA, Sarah Murray (then also a graduate collaborator on the Grand Tour Project), and Nicole Coleman (the Academic Technology Specialist and Co-investigator for Mapping the Republic of Letters) consulted generously with students on their final projects. The results were so striking—mixing evidence produced by close readings, attentive data extraction from eighteenth-century publications, and original solutions for data visualizations—that the Department of Classics published them in its newsletter. Students focused on individual travelers, but they were able to place them in the wider context of the Grand Tour because of the class lectures and discussions, secondary readings, and close engagements with the many figures animating the Dictionary. They came to appreciate concretely the larger historical picture in relation to the traces—be these archival or published sources—left by individuals. They were especially attracted by the cases of women travelers such as the writers Lady Montagu (1689-1762, travel years 1718, 1739–41, and 1746–62), who spent most of her later life in Italy, and Mariana Starke (c.1761-1838, travel years 1792-98), who also had lengthy stays in Italy; and by travels to less-visited places such as Sicily, destination for both the German Baron and diplomat J.J. Riedesel, who traveled to the island in 1767, and the British Grand Tourist Thomas Watkins (1761-1829, travel years 1787-89). In all cases, students traced these individual journeys on maps, thanks to careful data extraction of temporal and spatial information from primary accounts. But they also added extra layers of meaning and interpretation to their visualizations—finding ways to insert depictions of timelines (representing the progression of travels but also the amount of time spent in various places), networks of social interactions (especially with Italians), and measures of appreciation expressed by the travelers for the various sites visited alongside what they focused on in each place.

Teaching with the Grand Tour Explorer

Once the building of the current database got under way, along with the designing of the Grand Tour Explorer as a way to interact with, browse, and download the data carefully retrieved and curated from the Dictionary, the possibilities for teaching with it expanded and evolved. The class “Virtual Italy: Methods for Historical Data Science,” taught in various iterations, paralleled the last three years of my work on the Grand Tour Explorer. This was, to say the least, experimental teaching, especially insofar as the class itself contributed to my thinking as the Explorer took its final shape. Much of what happened in the classroom, and in conversation with students and TAs alike, informed my approach as I thought about how best to turn the Dictionary’s data, as well as the complex historical reality that stands behind it, into an effective and functional research apparatus. Students in the course helped to test the Explorer, experimenting with and improving its various features. Given how integral this pedagogical experience was for completing the Explorer, I thought it essential to document and share some of that work, and I also asked the graduate student TAs to reflect on teaching such a unique class. With A World Made by Travel now published, any future iteration or adaptation of the class will benefit from the Explorer’s full array of finalized features, as well as this book’s additional material—from the scholars’ essays to the explanation of the making of both the Dictionary and our data extraction, to this section on teaching.

I would like now to describe this class and its evolution. At its core, “Virtual Italy: Methods for Historical Data Science” focused on eighteenth-century travel accounts, with an emphasis on reading original sources and reflecting on the layering in archives—from original sources to reference works, and from analog to digital forms. But with the availability of the Explorer data, it was simultaneously a class in data science and digital humanities, encompassing the full arc of the work as students progressed from data transformation and analysis to interpretation and visualization. In turn, collaboration became an even more essential component of the class. Indeed, for the class’s first iteration—in the winter quarter of 2019—I received grants from the university’s Vice Provost for Teaching and Learning and from the Collaborative Teaching Program, allowing me to support Rachel Midura’s time and efforts in developing the course’s practicums. The course’s major challenge (and its ultimate payoff) was—and continues to be—figuring out how to merge the introduction of historical context, the meaning of a historical research question, the use of primary sources reflected in the Explorer database (published and manuscript travel accounts and letters, as well as archives), and analysis and interpretation of historical data. The bipartite structure of the first half of the course—with the first of two weekly classes dedicated to lectures and discussion on eighteenth-century contexts and sources, and the second focused on practicums introducing students to increasingly more complex approaches to data—embodied this balancing act.

To generate unity within each week of the course, as well as from week to week, the syllabus progressed through readings and related practicums addressing a series of questions about the Grand Tour: “What is the Grand Tour?,” “Who were the Grand Tourists?,” “Where did travelers go?,” “For how long did they travel?,” “Why did they travel?” Students learned data analysis skills by approaching these questions; they also took stock of the general shape of the data, including its biases, limits, and possibilities. Data interpretation was thereby introduced early on in the class. Our class activities were designed to underline the importance of writing and reading as technologies of knowledge and to highlight the ways that data—both qualitative and quantitative—is found within and outside of the digital realm. For example, I made a point of reading aloud together Leopold Berchtold’s “On Committing Observations to Paper,” from his 1787 book An Essay to Direct and Extend the Inquiries of Patriotic Travelers. These few pages, dense with instructions on how to collect information during travels, are detailed to amusing delight; they urge, among other things, that travelers use various types of writing implements and paper notebooks, produce multiple copies of the notes, date and reference all information sources, and revisit notes soon afterward so as to organize them by topic or to include additional information. Information technology, it turns out, has a long history, one in which traveling has played its own role, and students in the class enjoyed learning to interrogate this role. In another activity, students were instructed to sketch from memory a place they know on campus within five minutes from the classroom and then to visit that place in person to correct their drawing on the spot—all to emphasize the fact that travel in the eighteenth century was also about data collection, both written and visual, and how complex the process remains. The mixing of a traditional humanistic approach with data science and computational work was also reinforced by the composition of the class’s enrollees, with students being a mix of STEM and humanities majors who learned skills from one another during their in-class group work.

The class has continued to evolve since the first time I ran it. The fact that more scholarly work has appeared has allowed us to expand the course’s scope and to refine our focus on certain topics. For instance, as we’ve developed work on “hidden figures”—most of whom were women and servants, the latter all but invisible when surveying the print Dictionary—the students have read pages from Kathryn Walchester’s Travelling Servants: Mobility and Employment in British Travel Writing 1750–1850 (2020) and from the 2017 first edition of the Memoirs on the Life and Travels of Thomas Hammond, 1748–1775, a rare manuscript travel account by a servant. Sarah Goldsmith’s Masculinity and Danger on the Eighteenth-Century Grand Tour and Catherine D’Ignazio’s and Lauren Klein’s Data Feminism have both informed the project and the conversation in the classroom in real time since they appeared in 2020. As new studies with their own data have become available—such as Benjamin Reilly’s 2021 article “Seasons in Italy: Northern European Travelers, Rome, and Malaria,” in the Journal of Tourism and Cultural Change—it has been interesting to test them against our dataset. And now that the Explorer database is itself available in open access, I expect there to be dialogues and exchanges with scholars and students at other institutions, who are using it to conduct their own inquiries. I also anticipate more data sets becoming available and more sources being digitized. As the technology continues to evolve, the class will evolve with it.

The five practicums shared here—all created by Rachel Midura—work through some data science fundamentals using data drawn from the Grand Tour Explorer and relying exclusively on open-access sources and no dependencies. Using Google Sheets and Palladio, the practicums introduce DH projects in general and the Grand Tour Explorer in particular, then teach how to analyze data by sorting and filtering in Google Sheets, for example, to find certain categories of travelers or specific slices of time. They move on to more complex operations with pivot tables, allowing students to calculate, for instance, travelers’ ages at time of travel and the lengths of tours. Then, using Palladio, they introduce networks and network metrics, as well as spatial relationships and mapping. These practicums are examples that can be developed or adapted further. We imagine the possibility of adding other tools and exploring other topics and exercises—for example, networks of family relations among the travelers. This first set of practicums was intentionally designed to allow all data analysis and visualization to be conducted without programming skills, using only Google Sheets, Palladio, and some other platforms. But already in the past couple of years, the experience and interests of new TAs, along with students’ existing skills (or desires to acquire new skills), have led to the introduction of more computational methods. Some students, for instance, have wished to experiment with Python for data analysis; others have wanted to learn basic text-mining methods. The sample of students’ work in this section includes examples of sentiment analysis, topic modeling, and word vectors, to name a few approaches. The point is to equip students well enough that they can embrace data analysis and interpretation while identifying and working through a research question for their final projects.

An important step in the class, reiterated again and again (and especially as students embarked on their final projects), is to introduce students to the important “absence and presence” problem in our data and to the role of visualizations in making arguments and revealing these gaps. Together with the students, we spend time thinking about what might be missing in the archives, the silences, and what might be further silenced or brought to light by data processing. My framing of these questions in A World Made by Travel has been enriched by these teaching moments, and many of this book’s sections in turn reinforce this pedagogical purpose. Moreover, the book’s pedagogical contribution should extend beyond the case of eighteenth-century Grand Tour of Italy. Subjects such as data curation and data editing, the necessary attention to the gaps and silences in existing data and archives, the transparent and conscious categorization of thick and dense small-scale data sets (as opposed to the illusions and perils of big data) have for years been central to thinking in the digital humanities. Lauren Klein’s codework as carework, Jo Guldi’s critical search, Katherine Bode’s data-rich analysis, and the alternative quantifying of Claire Lemercier and Claire Zalc are all finding renewed interest, while data scientists become more and more concerned with issues of bias and interested in data ethics, humanistic complexity, and source criticism.⁴ One persisting obstacle to the bridging of this gap between data scientists and domain experts has been the scarcity of historical data sets. The Explorer is one such digital archive. Incomplete and imperfect, yet also rich in historical layers, explicit about its own making, and posing hard questions about the categorization of data and its relationship with sources, the Explorer adds to the field’s ongoing and essential conversation about data and makes it easier to bring that conversation into the classroom.

In the sections that follow, readers will find a sample syllabus for this course, the practicums, graduate teaching assistants’ accounts of their experiences teaching the course, and a sampling of final projects created by students during the course’s various iterations, all making use of the Grand Tour Explorer data. The fundamental principle of the class has always been to show how data might become part of humanistic and historical inquiry, and how data science can attend to context. However messy, surprising, rich, unsettling, fascinating, revealing, uncertain and incomplete those contexts may be, they are the starting point from which any exploration of the past necessarily begins.

Notes