Author: Katherine I. Knowles

When I started working through this week’s readings and activities, the last thing I anticipated was gaining a neww appreciation for bar charts. I’ve always been frustrated with all of the options in trying to represent data, and when working with programs such as Excel I’ve always been under the impression that the more flashy the graph the better. However, this week I developed a better understanding of the importance in weighting the clarity of data over more aesthetic choices as well as how those aesthetic choices influence an audience’s interpretation of the data.

In Johanna Drucker’s essay “Humanities Approaches to Graphical Display,” she outlines the various ways in which digital humanists can make use of data to create visualizations while maintaining awareness of the problems or implicit biases built into these tools. Drucker calls attention to the idea of data as capta, a concept that appears repeatedly throughout this unit:

Capta is ‘ taken’ actiely while data is assumed to be a ‘given’ able to be recorded and observed. From this distinction, a world of differences arises. Humanistic inquiry acknowledges the situated, partial, and consitutive character of knowledge production, the recognition that knowledge is constructed, taken, and not simply given as a natural represntation of pre-existing fact.

Drucker walks readers through the way data is constructed and the various assumptions made when visualizing it. She uses examples of time and temporality to contrast how humanists might perceive these topics and need more freedom in visualiztions versus the way scientists and social scientists may view them. Drucker poses a potential model for humanitists creating visualizations, and invites them to embrace the ambiguity of humanities data rather than hiding it in existing representational models.

Steve Braun’s article “Critically Engaging with Data Visualization through an Information Literacy Framework” picks up this idea, and suggests that possibly the ACRL framework used by librarians. The framework consists of six different “frames,” but in the case of digital humanities visualization Braun argues that “authority is constructed and contextual” and “information creation as a process” are the most crucial in using data as humanists. He also breaks down a series of design dichotomies of visualization to better assess meaning. Braun describes a “Choose Your Own Adventure” book that he uses with students to encourage them to consider data visualizations as “forms of dialogue rather than statements of fact,” and I hope I might have the opportunity to incorporate this kind of activity into my own pedagogy at some point.

In “Racism in the Machine: Visualization Ethics in Digital Humanities Projects,” Katherine Hepworth and Christopher Church address the biases built into many digital tools. They give the example of TayTweets, and discuss how an algorithm was quickly able to learn racism and hatred from the internet before pointing to the fact that all data visualizations are algorithmic. By comparing two digital mapping projects that focus on lynchings in America, Hepworth and Church are able to point to the ways projects can communicate similar information in different ways–ranging from the selection of data used, the explanations provided for the choices made in creating the project, and the aesthetic elements of the visualization.

The section on visualizations in Exploring Big Historical Data: The Historian’s Macroscope provides introduction to different kinds of visualizations, the different kinds of data that can be visualized, how certain elements of a visualization can influence audience interpretation, and tips on how to make a visualization as impactful as it can be. The case studies on the “Six Degrees of Francis Bacon” networking project and Michelle DiMeo and A. R. Ruis’s work with epistemic network analysis (ENA) show the nuances of data visualization in practice. I’ve always wanted to try out a network visualization project ever since I did a Gephi workshop, so I was interested in examining the pros and cons and learning the differences in different kinds of networks .

I end with the bar graph I created with the same data from the maps I used last week in my attempt at making a choropleth map; however, I took the advice of several of the articles and narrowed down the scope of the data when representing it visually. I have a new appreciation for the simplicity and clarity of bar charts, and how they make data more digestible for a wider audience.

The following screen shots are from a map that I created with StoryMaps JS as part of my ENG 818 course in Spring 2020, and it was the last project I started before the COVID-19 pandemic shutdowns hit. In fact, I was in East Lansing’s Espresso Royale with Dr. Scott Michaelsen on March 11 discussing the various directions I could go with this project when the university sent out an email announcing a case had been discovered on campus and everyone would be going home noon that day. Funnily enough, this project was for a Climate Fiction course, which of course addressed the potentiality of world changing plague. This short proposal for a map was all that ever came of this project, so I was excited to explore mapping again this week.

What has drawn me to wanting to participate in mapping projects in the digital humanities is the dynamism of visuals along with the bending of spatial and temporal logic to portray a more humanistic version of these kinds of narratives. As Todd Presner and David Shepard wrote in “Mapping the Geospatial Turn,” “Maps and models are never static representations or accurate reflections of a past reality; instead, they function as arguments or propositions that betray a state of knowledge, Each of these projects is a snapshot of a state of knowledge, a propositioned argumnet in the form of dynamic geo-visualizations” (207). The humanistic reconceptualizations of space and time are incredibly interesting to me, and I’m hoping to eventually include some sort of mapping element in my dissertation project.

I was probably most interested in the case studies we read for this week, although I enjoyed the various examples shared by Tim Hitchcock in his blog post “Place and the Politics of the Past,” particularly his discussion of how he worked with recreating London maps to more clearly represent his project data as this is something I’m hoping to do with early modern theatres in England. Fletcher & Helmrecih’s project, which was discussed in their essay “Local/Global: Mapping Nineteenth-Century London’s Art Market,” provided an example of how maps can be effectively combined with other forms of visualizations (in this case, networks) to better represent the scope of the layers of people and places that were key to their research. Cameron Blevins’s “Space, Nation, and the Triump of Region: A View of the World from Houston” (and web companion) detailed the ways space can be produced and combined textual analysis of newspapers with mapping visualizations to represent how a specific publication represented the country to its readers. I’ve currently been engaging with Henri Lefebvre’s The Production of Space as part of my research, so seeing it appear in multiple readings this week in terms of how it can apply to a digital humanities project was incredibly useful.

Since I had previous experience with both StoryMaps JS and the Geolocation module in Omeka, I dedicated most of my time this work to experimenting with map creation in Flourish. Below, you can see my attempts at creating a data map with pins and a choropleth map. Flourish is pretty user friendly and after watching Albert Cairo’s tutorials I felt fairly confident in my ability to create the map with pins using the data from the Alabama Slave Narratives CSV. I struggle more with the creating the choropleth map, as for some reason I had a difficult time getting the matching of columns between the JSON and CSV files to run. However, I was eventually able to get it to run, and it resulted in the second of the two maps below. I ended up focusing on the portion of the workbook dedicated to religious organizations by denomination.

Overall, I think this week was very influential in helping me think through how I might use mapping visualizations in my dissertation project. It also made me rethink what I might want to do for the grant writing project for this course. I’m looking forward to next week’s unit on visualizations to see how I might make connections between these different elements!

This week’s approach to text analysis felt more complex than last week’s. I was familiar with Voyant and Tableau, although the dive into topic modeling was a bit deeper than what I’d encountered before. My only experience using Python was at an SSDA Intro to Python workshop last year, and it was very (as would be expected) social sciences based so I struggled using it in ways I never would for my own research; therefore, I was very excited to dig into this week’s focus on Text Analysis with Python.

Nguyen et al.’s article “How We Do things With Words: Analyzing Text as Social and Cultural Data” provided a comprehensive overview of the steps someone might need to take in creating a text analysis research project. The specificity of this article was helpful in imagining how I might formulate my own text analysis project by working through developing research questions, conceptualization, data, operationalization, and analysis. The example of using Reddit to examine hate speech was very compelling, particularly with everything that has happened in the last year. The section I found most compelling was probably the one on operationalization–the sub-section on modeling considerations alone demonstrated how much I was leaving out of what it takes to develop this kind of project.

The Natural Language Processing with Python book was probably my most frustrating experience in this class so far, as I tried to work through the exercises and the provided tutorials as I went along. After installing Anaconda, I had a lot of trouble getting Jupyter notebook to run the command to download the suggested corpora (in total it took 3 hours–about 2.5 hours more than I’d care to admit). This hands-on approach to working with text analysis was enlightening, and I felt more comfortable with these exercises than the ones I participated in at the SSDA workshop I mentioned above. I was able to run commands to compare texts, determine word frequencies, and even reorder or combine sentences among many other things. I also must share that I was very entertained by running commands that compared Monty Python and the Holy Grail to the Book of Genesis. The later chapters, as acknowledged by the Preface, are much more in-depth and rely on more specialized Python and linguistic knowledge, but I think if I have time over the summer I might want to try and work through the entire book rather than just read it.

Sandeep Soni et al.’s article, “Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers,” was very helpful in that it provided an example of a specific project using this kind of methodology. The data and results sections included several ways of visualizing the numerical outputs produced by the tests. For instance, figures on pages 29 and 31 represent leader-follower pairs, while Figure 6 on page 32 visualizes page-rank scores. Ultimately, the research team was able to trace shifts in the meanings of words and how these changes were diffused across newspapers.

Honestly, I feel that I could spend many more weeks working on this unit. Much of the mathematical representation used across all of these works was over my head, and I know that two days of working through Python tutorials is nowhere near enough for me to even begin to understand all of the ins and outs that make it useful in this kinds of project. However, I definitely think I’ve found my summer project…

As an Early Modernist, text analysis has come up quite frequently in my studies. Ted Underwood discusses in “A Geneaology of Distant Reading” that distant reading is not some recent creation fueled by computers. Scholars have been counting rhyme endings, specific images, and manually modelling topics for decades before Franco Moretti (who also made use of Shakespeare’s works in his study of distant reading). I think what is most interesting about Underwood’s essay is the contention he highlights between the digital humanities and distant reading–they’re not the same thing, but they are for some reason often conflated. Additionally, Underwood points to the issue of distant reading often being aligned with a more social sciences approach to text, whereas the digital humanties are constantly pushing back against the assumption the the increase of technology in scholarly work does not inherently mean that threads of humanistic inquiry are moving toward the sciences.

In this essay, Underwood acknowledges that authorship analysis is perhaps the main area in which computres have deeply impacted the landscape of distant reading. Probably the most obvious and significant example of the developments of textual analysis in Shakespeare studies is the New Oxford Shakespeare, which participates in a new and updated computational analysis of authorship in the canon. In addition to modern spelling and critical reference editions of the complete works, the editors included an Authorship Companion. As Underwood states in the other essay of his we read this week, “Seven ways humanists are using computers to understand text,” “Part of the reason statsitcal models are becoming more useful in the humanities is that new methods make it possible to use hundreds of thousands of variables,” which seems to be what the editors of the New Oxford Shakespeare are attempting to do in finding every possible instance of a new hand being introduced into each play.

The 2016 New Oxford Shakespeare seems to realize the fears of many early modern scholars, as the plays and poems have been run through a computer and become a series of graphical models with accompanying essays on methods rather than focusing on critical readings of the language of the text. Admittedly, it goes much further beyond the example Froehlich mentioned in regarding the misunderstandings of textual analysis–“Shakespeare’s plays are about kings and queens”–but there is the risk of seeing all of the tables and graphs and fearing that the humanistic element has been removed from the plays. The Oxford University Press describes the collective edition as “an entirely new consideration of all of Shakespeare’s works, edited from first principles from the base-texts themselves, and drawing on the latest textual and theatrical scholarship.” This “entirely new consideration” might begin to explain why 18 works in the table of contents of the Modern Critical Edition include additional authors—twelve in total, not counting various credits to Anonymous—whose names are sometimes accompanied by a question mark or merely credited as an adapter of or contributor to the text.

The Authorship Companion attempts to expand on an extensive history of authorship studies in Shakespeare scholarship, but claims that this edition can be set apart from the rest due to the advancements in technology allowing a more thorough examination of writing patterns. In the introduction, editor Gabriel Egan describes the intrigue of attempting to edit works with questionable authority: “Shakespeare’s case raises particular difficulties: there are no manuscripts of any of his undisputed works in his own undisputed handwriting, and no completely reliable early definition of his canon” (v). The editors have divided this book into two sections: in the first, they share essays describing their methodology of their process for determining authorship while the second includes a variety of case studies detailing their justification for crediting certain authors to specific works. Additionally, a section of datasets accompanies the traditional works cited and index at the end, as the editors hope that it will “enable and inspire future research” (Egan vi).

The New Oxford Shakespeare and its Authorship Companion is one of the more recent (and prominent) examples of a massive text analysis process that made an important impact on interrogating the way we consider authorship and canon in early modern literature. I’m curious if, in my exploration of text analysis for this class, I will be able to use their datasets to participate in some of the “future research” imagined by the editors.

Selecting a content management system to fit your DH project is crucial; and, as pointed out in the “Choosing a platform” essay for this weeks reading is determined by several things: functionality, familiarity, community, support, and cost. Personally, I have the most experience with WordPress (which I’m using for this blog) and Omeka (which I’m using for a digital collections grant I’m working on)–although I do have some experience with Scalar, which I feel works nicely for more free form projects and is easy to use with students who are new to developing digital projects.

I had not heard of Mukurtu before, and I was surprised that was the case. With Mukurtu’s ethical considerations regarding how to treat collections containing sensitive material taken from indigenous communities, it seems like a good choice for collections that include material from communities that are not part of the university or library structure. Kimberly Christen et al. developed Mukurtu when, following their exploration of other content management systems, “discovered a set of unmet needs, including: cultural protocol driven metadata fields, differential user access based on cultural and social relationships, and functionality to include layered narratives at the item level.” As such, this CMS has attempted to addressing these issues by providing space for knowledge from the community to be included alongside more “traditional” metadata and distinctions as to who can access certain material based on their position in the community (or as an outsider).

Additionally, Christen discusses the creation of Traditional Knowledge licenses and labels in “Tribal Archives, Traditional Knowledge, and Local Contexts.” Since, in many instances, copyright law works against Indigenous communities or falls on the side of public domain, these licenses help contributors and users be more mindful in the way they produce and consume products of digital archives using this material. While they do not provide legal protection, I feel they do important work–especially considering the level of detail included in helping users determine what they need when creating them.

Lauren G. Kilroy-Ewbank’s essay “Doing Digital Art History in a Pre-Columbian Art Survey Class” was extraordinarily helpful in envisioning how to incorporate the use of content management systems into the undergraduate classroom in a way that scaffolds toward a final project while accepting the various limitations of this kind of project. In the past, I have seen undergraduate literature classes try and use both Omeka and Scalar for final projects, but without proper context students and professors alike felt frustrated at the lack of progress. While I am not a historian–or art historian for that matter–I’m hoping that I can use some of Kilroy-Ewbank’s strategies in the summer online class I will be teaching that requires students to curate a kind of digital anthology that includes a variety of multimedia materials.

On October 4, 2018 at about 10 am, I joined my colleagues in an event space at the University of Kansas to participate in the Digital Frontiers conference. That morning, Lauren Klein was giving the keynote lecture “Data Feminism: Community, Allyship, and Action in the Digital Humanities,” and she started the talk with a look at Periscope’s data visualization of U.S. Gun Deaths in 2018. I remember the emotional tension in the room as we watched each point fall too soon along the x-axis, and this sensation returned this week as I read Chapter 3, “On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints,” of Klein and Catherine D’Ignazio’s book Data Feminism. Last semester, I participated in an independent study supervised by Dr. Fitzpatrick focusing on Cultural Heritage, Digital Humanities, and Affect, which naturally had some overlap with feminist digital humanities work; and I’ve been particularly intrigued by the question of how emotion fits into digital humanities work.

With the affective turn, scholars have taken a lot of the work feminists scholars have done on emotion and, in some ways, made it more “acceptable.” Historically, emotion has been considered to have no place in the academy, but feminist scholars and then affect theorists have begun to make the case for emotion as a kind of knowledge. In digitial humanities specifically, there is already a tension between humanities work and the perceived clean, cold products of technology, and so what happens when we invite emotion into these projects and spaces? To specifically quote Klein and D’Ignazio’s rephrasing of Alberto Cairo, for example, “Should a visualization be designed to evoke emotion” (4)?

In some ways, I feel like this question could engage more specifically with the work of postcolonial digital humanities to become more fully intersectional. As D’Ignazio and Klein discuss, visualizations are traditionally structured in ways that subscribe to hierarchies of power. As such, they often (albeit unintentionally) contribute to the oppression of certain groups of people–and no matter what, “when visualizing data, the only certifiable fact is that it’s impossible to avoid interpretation” (7). When considering this in the context of the digital humanities where the interpretation is often more important than the data itself (as pointed out by the majority of the articles and essays we read this week), it seems reductive to claim that emotion is getting in the way based on certain design choices.

Going forward, I want to think more about the possibility of representing uncertainty. I remember the conflict surrounding the representation of 2016 election data via the visualization of “meters” that had the moving hand, and am curious how that argument might look after the 2020 election. Although Biden did scrape through with the win, there was a lot of discussion about how this was another data failure due to the fact that Biden was predicted to win by a landslide rather than by the slim margin that actually happened. The whole process was uncertain for weeks, and people were uncomfortable with the fact that the data and visualizations could not be considered complete for that time. The need to represent uncertainty is crucial–we must “leverage emotion and affect so that people experience uncertainty perceptually” (19), or, viceralize the data.

Last fall (when the world was still open), I attended Scout Calvert’s workshop “Crash Course in Research Data Management,” and I couldn’t believe how much I learned. I’d previously helped create rudimentary data management plans during my time as a project manager, but never something as in detail as was described in this workshop. At the time, I didn’t have my own project in mind, so I’m very excited to hear Calvert’s presentation today now that I will be able to apply it directly to my research.

I met with Kristen Mapes last week to discuss my tentative idea for a digital research project, and through this conversation I realized how far I had to go in terms of data curation/management before I could even begin to start thinking in terms of creating data visualizations–I felt like one of the humanists in Miriam Posner’s transcript of her talk “Humanities Data: A Necessary Contradiction“, and it didn’t occur to me that of course the data had to come before my manipulation of it. Therefore, I’m now scaling back my project idea for this class but I think in a very useful way; my goal is now to create a database on information regarding people involved in specific theatre spaces in Early Modern London (starting with Henslowe’s Diary) that can then be used to potentially build digital projects such as social networks.

Julia Flanders and Trevor Muñoz’s essay “An Introduction to Data Curation” was extremely helpful in refining my ideas as to how data would play a role in my humanities research. While I was familiar with the actual process of creating a sustainable data management plan, I hadn’t really thought through how humanists use data differently and how that might influence data curation. In particular, I am interested in further thinking through in which the interpretation of the data may be just as valuable as the data itself. Additionally, the idea that the conversations that occur and records of responsibility based on this interpretive layering and supplements the data in other ways. As my project seems to be shaping up as a kind of database, it is important that I consider these humanistic approaches to data in order to keep my project rooted in the literary historical framework I’m working within.

In his short essay “Defining Data for Humanists: Text, Artifact, Information or Evidence?,” Trevor Owens further explores the relevance of data to humanists by identifiying the varying ways data might operate and discussing how it might be of value. Not only does Owens identify data itself as potentially being texts, artifacts, or information, but he also examines the ways in which the use of the data can create new cultural objects. The emphasis of the questions one is asking as being as important as the data itself helps carry on the theme established in the Flanders and Muñoz essay, and I’m hoping to explore the ways in which I can represent the data I use humanistically to capture these other elements alongside the data itself.

I’d encountered the Library of Congress’s Recommended Format Specifications before, but after considering in relation to the rest of this week’s readings I’m seeing it in a new light. I thought it was interesting comparing the specifications for physical versions of an artifact to a similar format but in a digital version–there seems to be more flexibility in preserving the digital form of a work, particularly with the inclusion of both a “preferred” format option and an “acceptable” format option. Currently, I’m working on a digital archiving project with SIUE’s Lovejoy Library, and while we’ve been creating scans of archival materials to these standards it has been challenging due to the fact that they don’t physically exist in the preferred format. I’m interested in how that might be reflected in our data for this project. I wish I had known about Tropy earlier, as I’m processing large numbers of archival scans and trying to stick to a particular file naming and metadata scheme.

Overall, I really appreciated this week’s cursory look at data management, and I’m looking forward to exploring it further in Scout Calvert’s workshop during class and through next week’s reading assignments.

This week, I explored the various ways DH manifests and how its history has shaped perceptions of the field.

In her essay for the first Debates in the Digital Humanities, Kathleen Fitzpatrick attempts to clarify what exactly is/are the Digital Humanities. She addresses the field’s evolution from the days of humanities computing and explores the tension between scholars who work in DH who view it as being about making things versus those who utilize DH to interpret things. While as a project manager for a digital humanities center, I tended to fall into the making field—I was constantly assisting with building websites and developing content; however, since I’ve started my career as a PhD student at MSU I’ve moved more into the realm of interpreting by exploring the ways in which DH methods might be used to improve affective atmospheres in online learning. Regardless of the ways scholars engage with DH, I agree that “The particular contribution of the digital humanities, however, lies in its exploration of the difference that the digital can make to the kinds of work that we do as well as to the ways that we communicate with one another” (Fitzpatrick 15).

In the same edition, I read Rafael C. Alvarado’s blog post “The Digital Humanities Situation.” I was particularly intrigued by his argument that DH is “is a social category, not an ontological one” and therefore is nearly impossible to be define—and perhaps the way we define all disciplines in academia is inherently flawed. It seems that in many ways digital humanities is more about the tools and methods that are applied to disciplines rather than being its own. Rather, digital humanities is important as an avenue for allowing play and constantly finding ways to rework representations of things in a variety of ways to reveal new meaning.

Hockey’s essay “The History of Humanities Computing” provided interesting context for how DH has become what it is today by tracing the development of key projects, publications, tools, and conferences since 1949. In this article, Hockey argues that TEI is the single most important advances in the history of DH. Although I study literature and TEI is undoubtedly a major development, I agree with Sharon Leon’s argument in “Complicating a ‘Great Man’ Narrative of Digital History in the United States” that Hockey’s approach to documenting the history of humanities computing is preoccupied with textual focused projects and tools.

Although I am not a historian and cannot speak to the role of women in digital history/public history projects, I feel that Leon’s description of how the work of women is overlooked in digital history is perhaps representative of wider issues in DH. In particular, the discussion of who gets grants and university buy-in was reflective of my experience as a project manager for a digital humanities center. Like Leon mentions, at this university staff members could not receive PI credit and in order for the administration to buy into some of our (female) core faculty’s grant projects we had to first write and complete a grant project for a male colleague. I feel that the mentioned “Collaborator’s Bill of Rights” is a particularly intriguing document, and it makes me curious about how to make universities subscribe to the outlined guidelines. When digital humanists are already constantly trying to justify and define their field for tenure/promotion/funding reasons, how do they convince administrators to fundamentally change their ways of thinking in regard to producing scholarship and sharing credit?

If anything, this week made me consider the ways in which DH—even though it has come so far—needs to continue to evolve in order to create a more equitable field. Following this week, I’m hoping to explore readings that more specifically speak to the history of and experience of BIPOC scholars in DH (off the top of my head, I’m thinking of revisiting the rest of Bodies of Information and and other Debates in the Digital Humanities essays such as Tara McPherson’s “Why Are the Digital Humanities so White?”). I feel the theme of this week merits a call to further action, so I end with a quote from Dr. Leon’s conclusion:

“All practitioners must work purposefully to recognize the contributions of the underrepresented—those whose work is masked by inequity. Then, all members of field must consciously revise our origin stories to be inclusive of these individuals and their influence” (358)

This week, I attempted to draft my research idea for this course by working through the chart modeled in the figure “An Interactive Model of Research Design” from Joseph A. Maxwell’s book Qualitative Research Design as feature in Trevor Owens’s essay “Where to Start? On Research Questions in the Digital Humanities.” As Owens highlights, I definitely will need to adapt my research question throughout the semester as I learn more about what I’m studying, and with my background as a project manager and grant writer, I will definitely need to learn to leave “fancy writing” behind as I attempt to develop this project.

When looking at this diagram, my impulse was to try and develop my research question since it is at the center. However, I worked down the list and began with the goals. These were easy enough to come up with, as I just adapted the guidelines I am currently using to steer the development of my reading list for my comprehensive exams. As such, my conceptual framework also drew from the body of texts I’ve been working from as part of this process. I will admit that I struggled a bit with these, as I am ultimately approaching this project from a literary studies background and I was trying to align more with the guidelines for historians that we read about in the other essays this week.

I feel that by explicitly stating my goals first, I was able to refine my research question more than I originally thought I would be able to at this stage. In the initial question I had in mind before I worked through this exercise, I didn’t have the terms “social networks” or “map”–those definitely came to me after I determined my goal was to attempt to visualize the elements of the affective atmospheres in Early Modern London’s theatre district. I know I will need to fine-tune this question even further as the semester goes, but as it currently exists it is at least seems to be a legitimate form of inquiry that could possibly result in a digital humanities project.

The section I struggled with most was methods. I can list the texts I will be consulting, but I need to explore my options of methodology further. On my second reading of the essays from this week, I attempted to apply the suggestions for historians (particularly from the Digital HIstory and Argument white paper) to my approach as a student of literature. I identified some theoretical frameworks and primary texts to consult, but it doesn’t yet seem like I’m thinking about the finer details of method. In Owens’s summary of Maxwell’s approach, he points out that “the way you will sample/explore [your sources], and the actual techinques you will use to analyze and interpret them” is just as important as the objects of study themselves. This is something I will need to more clearly define moving forward.

In contrast, the validy section was easiest to tackle–can I actually attempt to create some kind of visualization that captures something intangible like an affective atmosphere? In my mind, this looks like a kind of overlay of a network visualization of the relationships within the tight-knit theatre community of Early Modern London on top of a map of the district, but will this truly reveal anything? I believe it might work in terms of showing how the connections between certain places and people lead to the development affective atmospheres in modern cultural heritage theatre sites, and I’m hoping that by revising my methodology as I research will help solidify how this will work in practice.

Hello everyone! My name is Katie Knowles, and I am a PhD student in the English Department here at Michigan State University. I received a BA from Hanover College in English and Music. My master’s degree is from the University of Birmingham’s Shakespeare Institute in Stratford-upon-Avon, and my dissertation was entitled “Symphonic Shakespeare: Representations of the Plays in Romantic Music.” From January 2017 to August 2019, I was the project manager for the IRIS Digital Humanities Center at Southern Illinois University Edwardsville. My research interests include Early Modern drama, digital humanities, theatre as cultural heritage, and affective atmospheres. Right now, I’m examining the ways in which theatres act as cultural heritage sites that affectively influence performance. This semester, I’m hoping DH865 will help me refine my research question for developing a digital humanities project that may eventually evolve into a digital component of my dissertation project.