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a visual representation of connections between people, words, or other common nodes to show patterns.

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Geographically represented data showing correlations and deepening systemic understandings of history, society, environmental patterns, etc..

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"Story Maps are a great way to combine your maps with narrative text, images, and multimedia content to create compelling, user-friendly web apps."

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A way to analyze data represented as a graph, picture, or report.

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online collection of primary resources, citable, digital museum

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"A carefully edited digital archive of the writings of an author, along with extensive scholarly essays, analysis, and multimedia galleries." ~Miriam Posner

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Platform for Network Visualization and Data storytelling.

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"Textual Data and Digital Texts in the Undergraduate Classroom." I provide a framework + sample assignments.

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Low-Tech Approaches to Digital Research, 26 February 2019

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Topic: immigration history of surrounding Augsburg University neighborhood
Tool: Curatescape
Type: historic tour

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This site is powered by Omeka + Curatescape, a humanities-centered web and mobile framework available for both Android and iOS devices.

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A primer of types of projects.

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Edit: Dot Porter made a Zotero collection for this post! Thanks, Dot!

Many students tell me that in order to get started with digital humanities, they'd like to have some idea of what they might do and what technical skills they might need in order to do it. Here's a set of digital humanities projects that might help you to get a handle on the kinds of tools and technologies available for you to use.

I've tried to include a few different types of projects, but it's hard to provide a really representative list. If you'd like to see more digital humanities projects, you can find directories at art-humanities.net and DHCommons.

Here, I discuss:

A Gallery of Primary Sources: Making the History of 1989

What it is

A collection of primary sources related to the fall of Communism in Eastern Europe, accompanied by teaching materials and interpretive essays.

What you'd need to know

Omeka.org (which forms the basis of the site), or you could use Omeka.net if you aren't so picky about the way the site looks and acts

HTML and CSS (optional; to customize the way the site looks)

PHP (optional; to customize certain site functions, like the way items display)

Get started

A Digital Scholarly Edition: The Willa Cather Archive

What it is

A carefully edited digital archive of the writings of Willa Cather, along with extensive scholarly essays, analysis, and multimedia galleries.

What you'd need to know

Get started

A Mapping Project: The Negro Travelers' Green Book

What it is

A searchable map of the addresses contained in the 1956 Negro Travelers' Green Book, which the user can filter by state or establishment type.

What you'd need to know

Alternatives

There are almost too many mapping technologies to list, but some popular tools include Neatline (which you'd use together with Omeka), GeoCommons (a relatively simple mapping application), and ArcGIS (used for complex or large-scale mapping projects). If you're serious about learning to build dynamic web-based maps, you'd be wise to learn the client-side language JavaScript (to control the way things show up on browsers), a server-side language like PHP (to interact with data), and the database language SQL (to manage your geospatial data). More mapping tools.

Get started

Learn how to clean and map data using Google Maps and Google Fusion Tables

A Network Visualization: A Co-Citation Network for Philosophy

What it is

A visualization of the authors referenced together in a corpus of philosophy journals.

What you'd need to know

Alternatives

You can make network visualizations without (necessarily) coding by using the web-based ManyEyes or the free Gephi or Cytoscape. Other tools for data visualization.

Get started

Computer-Aided Text Analysis: Topic Modeling Martha Ballard's Diary

What it is

An analysis of a historical document that uses a statistical method called topic modeling to group together the "topics" found in a large set of texts.

What you'd need to know

MALLET, a Java-based software package for text analysis (including topic modeling)

R, a programming language for statistical analysis and graphics

Alternatives

The Topic Modeling Tool provides a simpler-to-use graphical user interface for topic modeling, as does Paper Machines (which produces topic models from your Zotero library). But topic modeling is just one kind of textual analysis. Find a rundown of different kinds of text analysis here. Often, people who are new to text analysis enjoy starting with the web-based Voyant Tools. I also like the Lexos suite. More tools for text analysis.

Get started

A Historical 3D Model: Digital Magnesia

What it is

A painstakingly researched re-creation of the Hellenistic city of Magnesia.

What you'd need to know

This particular model is created using the procedural modeler CityEngine.

Alternatives

The easiest way to create 3D models is probably with SketchUp. Serious 3D modelers often use Maya or Rhino.

Get started

A Longform, Media-Rich Narrative: The Nicest Kids in Town

What it is

An essay, accompanied by photographs, video, and sound, that can be reconfigured by the viewer to be read in multiple ways.

What you'd have to know

Scalar, a multimedia authoring platform

CSS (optional, to add custom styling to Scalar)

Get started
August 30: Post edited to reflect that you don't need Python to download Web of Science data. Thanks for the correction, Scott Weingart!

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Edit: Dot Porter made a Zotero collection for this post! Thanks, Dot! Many students tell me that in order to get started with digital humanities, they'd like to have some idea of what they might do and what technical skills they might need in order to do it.

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This interactive visualization shows the spread of racially-restrictive deeds across Hennepin County during the first half of the twentieth century.

Click on a racially covenanted lot to learn more about the property. Use the search button at the bottom of the map to look for a specific address.

** If you are having issues with any of the features on our map, try clearing your browser's cache and reloading the window **

Racial covenants were tools used by real estate developers to prevent people of color from buying or occupying property. Often just a few lines of text, these covenants were inserted into warranty deeds across the country. These real estate contracts were powerful tools for segregationists. Real estate developers and public officials used private property transactions to build a hidden system of American apartheid during the twentieth century.

Inspired by the idea that we cannot address the inequities of the present without an understanding of the past, Mapping Prejudice was created to expose the racist practices that reshaped the landscape of Minneapolis. People of color see how discriminatory practices cascade through their lives, erecting barriers that limit access to housing, credit, education, and wealth. But the resulting physical, emotional, and financial baggage has been harder to recognize for people who have not personally experienced racism. Mapping Prejudice seeks to make these burdens visible by locating them on a digital, interactive map. The project team was inspired by a desire to create the first-ever comprehensive map of racial covenants for an American community.

The project started in Minneapolis, where research revealed that the city was not always segregated. Covenants helped remake the racial landscape of the city. As racially-restrictive deeds spread, African Americans were pushed into small and increasingly circumscribed neighborhoods. Even as the number of black residents continued to climb, ever-larger swaths of the city became entirely white.

The project is based at the John Borchert Map Library at the University of Minnesota. From this base, our interdisciplinary team of community activists, students and scholars has mobilized community members to identify discriminatory deeds. Thanks to the enthusiastic support of the community, the Hennepin County map was finished early in 2020.

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This interactive visualization shows the spread of racially-restrictive deeds across Hennepin County during the first half of the twentieth century. Click on a racially covenanted lot to learn more about the property. Use the search button at the bottom of the map to look for a specific address.

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Augsburg Historic Recordings Collection - YouTube

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A collection of 35mm and 16mm films, as well as some audio recordings, housed in the Augsburg University Archives that date from the late 1930s to the early ...

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The bars below show emotion intensity for each sentence. Click on a character in the graph to see where they appear. Hover over each bar to read the original sentence.

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Are Shakespeare's tragedies all structured in the same way? Are the characters rather isolated, grouped, all connected?
Narration, even fictional, contains a network of interacting characters. Constituting a well defined corpus, the eleven Shakespearean tragedies can easily be compared: We propose here a network visualization in which each character is represented by a node connected with the characters that appear in the same scenes. The result speaks for itself: the longest tragedy (Hamlet) is not the most structurally complex and is less dense than King Lear, Titus Andronicus or Othello. Some plays reveal clearly the groups that shape the drama: Montague and Capulets in Romeo and Juliet, Trojans and Greeks in Troilus and Cressida, the triumvirs parties and Egyptians in Antony and Cleopatra, the Volscians and the Romans in Coriolanus or the conspirators in Julius Caesar.

SHAKESPEAREAN TRAGEDY

(The 11 tragedies in chronological order)

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Are Shakespeare's tragedies all structured in the same way? Are the characters rather isolated, grouped, all connected? Narration, even fictional, contains a network of interacting characters. Constituting a well defined corpus, the eleven Shakespearean tragedies can easily be compared: We propose here a network visualization in which each character is represented by a node connected with the characters that appear in the same scenes.

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Topic modeling is used to discover the topics that occur in a document's body or a text corpus. Latent dirichlet allocation (LDA) is an approach used in topic modeling based on probabilistic vectors of words, which indicate their relevance to the text corpus.

In this tutorial we present a method for topic modeling using text network analysis (TNA) and visualization. The approach we propose is based on identifying topical clusters in text based on co-occurrence of words. We will demonstrate how this approach can be used for topic modeling, how it compares to Latent Dirichlet Allocation (LDA), and how they can be used together to provide more relevant results.

You will see how LDA may be less efficient for some texts as it tends to only focus on the terms' frequency and disregard the structure of a text ("bag of words" or "vector space" model, also n-gram model where n=1). Text network analysis, on the other side, takes into account both the text's structure and the words' sequence, providing more precise results in some cases.

We will start from a general overview of the two approaches and will then run a test on real data to show the differences between the two approaches and how they could be used together.

Latent Dirichlet Allocation (LDA) - How It Works

To make the long story short and to avoid complicated maths we will go through how LDA works glossing over some details, just to give a general picture.

LDA, at its core, is an iterative algorithm that identifies a set of topics related to a set of documents ( Blei 2003). In order for it to work, LDA needs to know how many topics it's searching for beforehand. For example, if we have one document we can specify that we're looking for four different topics. LDA will then go through each word that appears in the text, randomly ascribe it to one of the four topics, and calculate a special score for this word based on the probability that this word will be found in this particular topic in the set of documents (in our case, only one document). LDA then ascribes this same word to another topic and calculates the same score. After many iterations we get a list of words in each topics with probabilities. For each topic we can select the top 5 words with the highest probability of belonging to that particular topic and we will get a pretty good description of what the topic is about through the combination of words with the highest probability. Naturally these words tend to co-occur together in the same context. Words with high frequency will take more prominent position in each topic ( Blei 2012). A distinguishing characteristic of latent Dirichlet allocation is that all the documents in a collection share the same set of topics, but each document exhibits those topics with different proportion.

LDA is perhaps the most popular and well-tested method for topic modeling today. Since it was introduced a few years ago, it's gone through some upgrades. For example, lda2vec is a model that combines LDA with word2vec, providing better insights about the words and topics. LDA itself is a special case of pLSA - Probabilistic Latent Semantic Analysis, which is a modification of LSA (Latent Semantic Analysis).

One of the main drawbacks of LDA is that it's quite complex to understand and that it requires a precise number of topics in order to proceed. Moreover, the fact that it's a bag-of-words model (i.e. the sequence of words and the structure of narrative is lost) may lead to some losses, especially for texts with complex narrative structure (e.g. several topics that re-occur in different parts of the text). We also tend to lose the relations between the topics. Moreover, if certain words re-occur throughout all text they might be present in all the topics identified making the results less precise.

This is why there's been numerous efforts to find other, more efficient approaches, and text network analysis is one of them.

Text Network Analysis and Visual Topic Modeling

In text network analysis a text is represented as a graph. The words are the nodes and co-occurrences of the words are the connections between them (see Paranyushkin 2001). In order to make the graph more precise and representative of the way we read, we can implement the Landscape reading model ( Broek et al 1999) scanning the connections between the words in 4-word windows. Such approach yields a graph that traces the activation of each concept in relation to others, so that it's possible to see how the different words are connected in a specific narrative.

Once the graph is constructed, we can use community detection algorithms to identify the groups of nodes that are more densely connected to one another than to the rest of the network as well as the most influential nodes (words) inside the graph using the measure of betweenness centrality or degree. This data can be obtained both in qualitative and quantitive way, making text network analysis appropriate both for machine learning tasks and human interpretation.

As an example, here's a graph of the same text fragment as was shown in the LDA example above made using text network analysis tool InfraNodus:

The words that tend to co-occur next to each other in this text are organized in communities (indicated with distinct colors), while the most influential words (the nodes with the highest betweenness centrality) are shown bigger on the graph.

It can be seen easily that the main topics in this short text are:

which sums up this short text pretty well (it is about the number of genes an organism needs to survive).

A very important feature of text network analysis is that it doesn't only detect the main topical clusters but also shows the relation between them - something that is missing from LDA analysis.

Text Network Analysis vs LDA

We will now compare how the two approaches perform on medium-sized documents with a ground truth value: that is, the Wikipedia articles that have been manually categorized and ranged according to the topics that editors deemed appropriate for the texts. Later we will be publishing a more extensive research on comparing the two approaches using a large and widely accepted corpus of documents.

In order to evaluate the quality of text network analysis and LDA we will be using InfraNodus open-source Javascript / Node.Js based tool for text network analysis and LDA.js open-source Javascript package. We set both algorithms to identify 4 topics in a document and will compare their results to the topics that have been set manually for each article by Wikipedia editors.

Case 1: Wikipedia Article on Text Mining

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Main topics according to Wikipedia editors:
1: Text mining
2: Artificial intelligence applications
3: Applied data mining
4: Computational linguistics
5: Natural language processing
6: Statistical natural language processing
7: Text

We can see that both methods performed quite well on this article, however, the topics identified by text network analysis contain a cluster on "business intelligence research" making it clearer what text mining is actually used for. LDA gave slightly more importance to the terms "copyright" and "biomedical", which are also contained in the first topic identified by TNA (applications of text mining), but not at the first positions. You can check this using the embedded TNA graph below:

Case 2: Wikipedia Article on Deconstruction (philosophical concept)

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Main topic categories according to Wikipedia editors:
Deconstruction
Literary criticism
Philosophical movements
Philosophy of language
Postmodern theory

In this case in LDA results we have the word "derrida" and "derrida's" appearing in several topics, making it harder to distinguish topics clearly from one another. In the TNA method we propose a word can only belong to one topic, so the topical structure it produces is clearer. Some of the topics that LDA and TNA produced are similar, such as "searle / austin / speech", related to the Philosophy of Language topic in Wikipedia.

Conclusions

We have demonstrated the difference between LDA and text network analysis (TNA) methods for topic modeling. We have shown that while LDA is a more popular method it does not always produce very precise results and it lacks information about the relation of topics to one another. These deficiencies are resolved by the text network analysis method we proposed, which maintains the structure of the narrative and shows the relations between different topics, both on quantitive and qualitative levels.

We will perform a more thorough analysis of the both methods and compare their performance using a larger text corpus. For now we invite you to try text network analysis on your documents using InfraNodus tool and, please, feel free to contact us if you have any comments, suggestions or feedback about this approach.

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Tutorial: Text Mining Using LDA and Network Analysis

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In this tutorial we present a method for topic modeling using text network analysis (TNA) and visualization. The approach we propose is based on identifying topical clusters in text based on co-occurrence of words. We will demonstrate how this approach can be used for topic modeling, how it compares to Latent Dirichlet Allocation (LDA), and how they can be used together to provide more relevant results.

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Advanced text mining tutorial w Natural Language Processing

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Advanced text mining tutorial w Natural Language Processing

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NLP Based Analysis of Literary Texts (M 5.2.3)

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The following tutorial describes how to analyze texts, by first generating linguistic annotations with a simple, single java program that bundles the abilities of several state-of-the-art NLP (Natural Language Processing) tools, and then accessing the annotations provided in astandardized output format for complexempirical analyses of text style and content in a scripting language.

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December 2018. The full text of Cather Studies Volume 11 is now available on the Archive! Published by the University of Nebraska Press in 2017, Willa Cather at the Modernist Crux was edited by Ann Moseley, John J. Murphy, and Robert Thacker. This volume is dedicated "To the Memory of David Porter (1935-2016): Cather scholar, musician, teacher, and wise friend."

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December 2018. The full text of Cather Studies Volume 11 is now available on the Archive! Published by the University of Nebraska Press in 2017, Willa Cather at the Modernist Crux was edited by Ann Moseley, John J. Murphy, and Robert Thacker.

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Created with Tableau software.

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Extend Tableau to fit your organization's needs.

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Topics: Multiple disciplines
Tool: ArcGIS Online w StoryMap app
Type: presentation (upgraded digital version of a research paper)

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Classic Story Maps Gallery - Get Inspired

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Discover stories from around the world created by the storytelling community with Esri Story Maps. Get inspiration and ideas for your own stories.

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Celebrating Venice: On Land and Sea In the fall of 2012, the Bell Library mounted a gallery exhibition with the same title. This online exhibit was drawn from the exhibit guide for the physical exhibition, exploring Venice from its origins as a sanctuary for refugees fleeing waves of Germanic invasions and Huns to a thriving a city state that functions as a cultural and commercial hinge between east and west.

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Torn Apart is a curation and visualization of publicly available data concerning ICE, CBP facilities, and usages. Also lists of allied and pro-immigrant facilities.

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What follows is the text of my talk at the 2017 MLA Annual Convention, slightly modified for the web. I spoke on a panel that showcased new forms of nineteenth-century digital scholarship. (Also featured were Mark Algee-Hewitt and Annie Swafford). An essay-length version of the talk is in the works, but since I'm heads-down on my book manuscript at the moment, I'm posting my remarks here. NB: If you'd like to read some of the more design-oriented work I've done on the subject, please see my paper from the 2016 IEEE VIS conference, co-authored with Catherine D'Ignazio, that outlines our principles of feminist data visualization and specifies some design questions for engaging them.
This talk departs from a seemingly simple question: "What is the story we tell about the origins of modern data visualization?" And as a set of follow-ups, "What alternate histories might emerge, what new visual forms might we imagine, and what new arguments might we make, if we told that story differently?"

To begin to answer these questions, I'll focus on the work of one visualization designer from the nineteenth century, Elizabeth Palmer Peabody, whose images are rarely considered in the standard story we tell about the emergence of modern visualization techniques. When they are mentioned at all, they are typically described as strange-and sometimes even as failures. You can see one of them just below.

To us today, accustomed to the charts and graphs of Microsoft Excel, or the interactive graphics that we find on (dot com) on any given day, we perceive schemas like this as opaque and illegible. They do none of the things that we think that visualization should do: be clear and intuitive, yield immediate insight, or facilitate making sense of the underlying data. But further questions remain: why have we become conditioned to think that visualization should do these things, and only these things? How has this perspective come to be embedded in our visual culture? And most importantly for us here today, what would it mean if we could view images like these, from the archive of data visualization, instead as pathways to alternate futures? What additional visual schemas could we envision, and what additional stories could we tell, if we did?

So I'm going to inhabit my method, and frame my talk today in terms of an alternate history. First, I'll walk you through the visual schema that you see above-left, proposed by Peabody in 1856. Then I'll talk about some of the more speculative work I've done in attempting to reimagine her schema in both digital and physical form. And then I'll try to explain what I'm after by describing this work, as you saw in the title of this talk, as feminist-

More specifically, I'll show how Peabody's visual method replaces the hierarchical mode of knowledge transmission that standard visualization techniques rest upon with a more horizontal mode, one that locates the source of knowledge in the interplay between viewer, image, and text. I'll demonstrate how this horizontal mode of knowledge transmission encourages interpretations that are multiple, rather than singular, and how it places affective and embodied ways of knowing on an equal plane with more seemingly "objective" measures. And finally, I'll suggest that this method, when reimagined for the present, raises the stakes for a series of enduring questions-about the issue of labor (and its relation to knowledge work), the nature of embodiment (and how it might be better attached to digital methods), and the role of interpretation (and how is not only bound to perception, but also design).

But I think that's enough of a preamble. So, to begin.

Elizabeth Palmer Peabody was born in Massachusetts in 1804. Today, she is most famous for her proximity to more well-known writers of the American Renaissance, such as Emerson and Hawthorne. (Hawthorne was actually married to her sister, Sophia). But Peabody had impact in her own right: the bookstore that she ran out of her home, in Boston, functioned as the de facto salon for the transcendentalist movement. She edited and published the first version of Thoreau's essay on civil disobedience, which appeared in her Aesthetic Papers, in 1849. And interestingly, she's also is credited with starting the first kindergarten in the United States.

But in the 1850s, Peabody set off to ride the rails. She traveled as far north as Rochester, NY; as far west as Louisville, KY; and as far south as Richmond, VA, in order to promote the US history textbook she'd recently published: A Chronological History of the United States. Along with boxes of books, Peabody traveled with a fabric roll the size of a living room rug, which contained floor-sized versions of charts like the one in the image above, which I'll tell you only now is a visualization of the significant events of the seventeenth century, as they relate to the United States. (This image that you see is a plate from the textbook, measuring, at most, 3 inches square).

In her version of a sales pitch, Peabody would visit classrooms of potential textbook adopters, unroll one of her "mural charts" (as she called them) on the floor, and invite the students to sit around it to contemplate the colors and patterns they perceived.
Peabody's design was derived from a system developed in Poland in the 1820s, which employed a grid, overlaid with shapes and colors, to visually represent events in time. At left you see, on the bottom left of the page, a numbered grid, with each year in a century marked out in its own box. On the top right you see how each box is subdivided. So, top left corner for wars, battles, and sieges; top middle for conquests and unions; top right for losses and divisions, and so on. And shapes that take up the entire box indicate an event of such magnitude or complexity that the other events in that year didn't matter.

The basic exercise was to read the narrative account in Peabody's textbook, and then convert the list of events that followed, like the one you see below-left, into graphical form. And I should note, at this point, that the events are color-coded, indicating the various countries involved in a particular event.

So now I'll return to the original chart (at left), and you can see now, hopefully, that England is red, the Americas are orange, and the Dutch are teal-those are the colors that dominate the image. The French get in on the act, too, in blue. And if you cross-reference the chart to the table of events, you can see, for instance, the founding of Jamestown in 1607; and the settlement of Plymouth in 1620-that's the red on the right-and, interestingly, the little teal box stands for the first enslaved Africans arriving in Virginia at that same time.

But I think it's safe to say that no one in this room could have known this without me explaining how to interpret the chart. And for researchers and designers today, who champion the clarifying capacity of visualization; or for those who believe that datavis is best deployed to amplify existing thought processes-for such people, Peabody's design would be a complete and utter failure. For Peabody, though, this near-total abstraction was precisely the point. Her charts were intended to appeal to the senses directly, to provide what she called "outlines to the eye." Her hope was that, by providing only the mental outline of history, and by insisting that each student interpret the outline of history for herself, she would conjure her own historical narrative, and in that way, produce historical knowledge for herself.

So this is where the feminist aspects of Peabody's approach to data visualization begin to emerge. Anticipating some of the foundational claims of feminist theory, Peabody's schema it insists upon a multiplicity of meanings, and locates knowledge in the interplay between viewer, image, and text. Hers is a belief in visualization, not as clarifying or illuminating in its own right, not as evidence or proof of results, but as a tool in the process of knowledge production.

At this point, it also bears mention that for Peabody, the creation of knowledge took place through a second mode: through the act of creating the images themselves. Peabody also printed workbooks, with sheets like the one you can see at left, and she envisioned the exercise, ideally, as one not merely of cross-referencing events to their visual representation, but of constructing the images they would then study. So you can see one student's attempt below-left. And below on the right is another, by someone who appears to have given up all together. (These images come from a blog post by the Beinecke, but I've traveled to several archives across the northeast U.S. and seen the same thing). I used to show these images to get a laugh, but I know now, because at this point I've tried it a number of times, that this method is a very hard thing to actually do.

But that seems to be both a liability of the form, and also the point. Peabody devised her method at a moment of great national crisis-the decade leading up to the Civil War-and she recognized that the nation's problems would be difficult to solve. Her goal was to prompt an array of possible solutions-one coming from the creator of each chart. And her hope was that, by designing new narratives of the past, her students would also imagine alternate futures.

In fact it was this aspect of Peabody's system-the idea that, by insisting that each student participate in the creation of each chart, they would also each create their own interpretations of them, that seeded my desire to reimagine these charts for the web.

I'd already recognized, in Peabody's method of first scanning a list of events, then identifying the type of each event and the nations involved, and then plotting it on the chart, a method that was strikingly procedural. And the process of cross-referencing between text and image seemed to me, even more, like the form of interaction you see all over the web. But it was her insistence on the need to create the charts in order to create knowledge that confirmed my decision to pursue the project. What would we discover, about the past, or about the present, if we were to make Peabody's scheme accessible to students and scholars today?

This was the point that I enlisted a graduate student in Human-Computer Interaction (HCI), Caroline Foster, who helped design the really beautiful site that you'll see in a minute. And I was also fortunate to be able to work with two very talented computer science undergrads, Adam Hayward and Shivani Negi, who helped with the technical implementation. And together, we set about creating this site. (NB: Erica Pramer, who graduated last year, helped with an earlier version of the project).

Below you can see some screen-shots from the four interactive modes: "Explore" (top left), which shows you how the events and their visual representations align; "Learn" (top right), which guides you through Peabody's original lesson; "Play" (bottom left), which gives you a blank grid to color in; and "Compare" (bottom right), which generates a timeline on the basis of the current dataset, and displays it alongside the data as it's stored in CSV form, as it's listed in the text, and as it's displayed on Peabody's grid. (There are also some narrative components of the site, and while we're still fixing bugs and tweaking the text, we encourage you to explore the site and send us your feedback).

There's a lot more to be said about the eerily object-oriented way in which Peabody structures her data, and I touch on that in one of the interactive elements of the site, but in terms of my current argument, the salient point is how the clarity and legibility of the timeline, by contrast to Peabody's grid, shows just how conditioned we've become to certain ideas about what visualization should and should not do. To us today, accustomed to a standard typology of visual forms, we can form a really quick rank order of these images. The timeline is better we say, because it yields immediate insight. Who the heck knows what is going on with that grid?!

But what if we understood the purpose of visualization differently? What if we were supposed to stop and think hard about what we were seeing, and what it meant?

For my part, I've been thinking about Peabody's charts in their most striking instantiation-those mural charts on the floor-and how they demand a different mode of sensory engagement altogether. They replace the decorative and utilitarian function of a rug with an experience designed to generate knowledge, and in so doing, they require viewers to reconsider the actual position of their bodies in relation to their objects of knowledge.

This feature has prompted me to undertake a second project to implement a floor-size version of Peabody's charts, which you can see at lest in some very early phases. (You can read about our current progress on the DH Lab blog). So in the top image, you see a matrix composed of 900 individually addressable LEDs, and in the bottom, you see the touch interface that we're developing, which makes use of conductive tape and neoprene in an almost matrix-like keyboard interaction, so that you'll be able to toggle each square off and on. And as my students and I carefully measure each strip of tape, and solder each part of each circuit together, I think back to Peabody's own process of fabrication.

When I mentioned that she made floor-sized versions of the charts as a sort of marketing ploy, what I didn't mention was that, as an additional incentive, she promised a handmade chart to any classroom that purchased the book. Writing to a friend in 1850, Peabody revealed that she was "aching from the fatigue" of making the charts for each school. She described how she would stencil shapes and colors onto a large piece of fabric, and how a single one took her 15 hours. As you can see from the text of the letter, she yearned for her book to become profitable so that she could hire someone to "do this drudgery for [her]."

It speaks both to poor book sales, and to the perceived lack of value of the charts, that none have been preserved. But what we do have, in letters like these, is evidence of the actual physical labor, as well as of the knowledge work, involved in producing these charts. And more specifically, with her references to the fabric and to the drudgery, it's labor in feminized form.

Let me take some time unpack this, because it's this observation that I want to end with, as I return to the question of alternate histories, and their impact on how we produce knowledge.

A lot of people-or, well, the handful who have ever thought to comment on Peabody's work-observe that Peabody's charts look like Mondrian paintings. And it's true that, in their abstraction, they evoke the modernist grid. But thinking about the feminized labor of making the charts brings to mind a second point of reference, which is quilting.

What you see here (above) are two quilts from the area of Alabama known as Gee's Bend. These quilts, created by a close-knit community of African American women in years that span the twentieth century, have in fact recently been posited as offering an alternate genealogy of modernism. This genealogy derives from folk art and vernacular culture, and centers this community of women who would otherwise be placed far to the side in the story of modernist art.

You might already be starting to guess where I am going with this line of thought-Peabody in relation to standard accounts of data visualization; the women of Gee's Bend in relation to Mondrian. And it's true-women's work of all kinds, be it education or quilting, has long been excised from the dominant accounts of their fields.

But there's another aspect of this comparison that I want to draw out-which is how both the quilts of Gee's Bend, and charts of Elizabeth Peabody, offer alternative systems of knowledge-making. Both employ shape and color to visually represent events in the world. And both, also, rely upon sense perception-and more specifically, the tactile experiences of the body-in order to assimilate those shapes and colors into knowledge. In her textbook, Peabody even talks about things like pleasure, and she emphatically rejected the idea of a single interpretation of history in favor of an exchange between the subject and object of knowledge. For Peabody, the abstraction of the grid was preferable to a more mimetic form because it "left scope for a little narration." In other words, she believed that if her visualizations provided the contours of history, the viewer could then-both literally and figuratively-color them in.

And therein lies her principal lesson: about what information constitutes knowledge, about how that knowledge is perceived, and about who is authorized to produce it. That, to me, is why this project-the historical part and the technical one-is a feminist one. Because it brings renewed attention to the role of interpretation, and to the modes of knowing outside of what we'd typically consider to be visualizable, such as intuition, or affect, or embodiment.

As humanists, we've been trained to recognize the value of these alternate forms of knowledge, just as we've been trained to register the people, like Peabody, who stand on the periphery of the archive. These are often people whose stories we would otherwise lack sufficient evidence to be able to bring to light, whether it's evidence in the form of data, or just the archival record.

And this is where I see a convergence in the historical and theoretical work surrounding the archive, and the more technical, but equally theoretical work relating to data and its visual display. It's where I think humanists have real lessons to teach those who design visualizations-and as I begin to speak more with designers and researchers outside of the humanities, I'm increasingly convinced of this fact. But it's also a space where I think we, as digital humanists, could make an intervention in our own scholarly fields. It's not only by taking digital methods and applying them to humanistic questions; or even what I've demonstrated here today: how humanistic theories allow us to better understand certain digital techniques. Rather, what I'd like to see as we "renew the networks" of nineteenth-century digital studies, to borrow a phrase that Alison employed to introduce this session, is to insist on a richer intersection of the digital with the humanities, as both critical and creative, theoretical and applied, both the contours and the coloring in. That's what I envision as the shape of things to come.

Thank you.

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Feminist Data Visualization; Or, the Shape of History

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What follows is the text of my talk at the 2017 MLA Annual Convention, slightly modified for the web. I spoke on a panel that showcased new forms of nineteenth-century digital scholarship. (Also featured were Mark Algee-Hewitt and Annie Swafford).