Brandon Walsh

Ripper Press Reports Dataset

[Crossposted on the WLULDH blog.]

Update: since posting this, Laura McGrath reached out about finding an error in the CSV version of the data. The version linked to here should be cleaned up now. In addition, you will want to follow steps at the end of this post if using the CSV file in Excel. And thanks to Mackenzie Brooks for her advice on working with CSV files in Excel.

This semester I have been co-teaching a course on “Scandal, Crime, and Spectacle in the Nineteenth Century” with Professor Sarah Horowitz in the history department at W&L. We’ve been experimenting with ways to make the work we did for the course available for others beyond our students this term, which led to an open coursebook on text analysis that we used to teach some basic digital humanities methods.

I’m happy to make available today another resource that has grown out of the course. For their final projects, our students conducted analyses of a variety of historical materials. One of our student groups was particularly interested in Casebook: Jack the Ripper, a site that gathers transcriptions of primary and secondary materials related to the Whitechapel murders. The student group used just a few of the materials on the site for their analysis, but they only had the time to copy and paste a few things from the archive for use in Voyant. I found myself wishing that we could offer a version of the site’s materials better formatted for text analysis.

So we made one! With the permission of the editors at the Casebook, we have scraped and repackaged one portion of their site, the collection of press reports related to the murders, in a variety of forms for digital researchers. More details about the dataset are below, and we’ve drawn from the descriptive template for datasets used by Michigan State University while putting it together. Just write to us if you’re interested in using the dataset - we’ll be happy to give you access to them under the terms described below. And also feel free to get in touch if you have thoughts about how to make datasets like this more usable for this kind of work. We’re planning on using this dataset and others like it in future courses here at W&L, so stay tuned for more resources in the future.


Jack the Ripper Press Reports Dataset


The dataset can be downloaded here. Write if you have any problems accessing the dataset. This work falls under a cc by-nc license. Anyone can use this data under these terms, but they must acknowledge, both in name and through hyperlink, Casebook: Jack the Ripper as the original source of the data.


This dataset features the full texts of 2677 newspaper articles between the years of 1844 and 1988 that reference the Whitechapel murders by Jack the Ripper. While the bulk of the texts are, in fact, contemporary to the murders, a handful of them skew closer to the present as press reports for contemporary crimes look back to the infamous case. The wide variety of sources available here gives a sense of how the coverage of the case differed by region, date, and publication.

Preferred Citation

Jack the Ripper Press Reports Dataset, Washington and Lee University Library.


The Jack the Ripper Press Reports Dataset was scraped from Casebook: Jack the Ripper and republished with the permission of their editorial team in November 2016. The Washington and Lee University Digital Humanities group repackaged the reports here so that the collected dataset may be more easily used by interested researchers for text analysis.


The same dataset exists here organized in three formats: two folders, ‘by_journal’ and ‘index’, and a CSV file.

  • by_journal: organizes all the press reports by journal title.
  • index: all files in a single folder.
  • casebook.csv: a CSV file containing all the texts and metadata.

Each folder has related but slightly different file naming conventions:

  • by_journal:
    • journal_title/YearMonthDayPublished.txt
    • eg. augusta_chronicle/18890731.txt
  • index:
    • journal_title_YearMonthDayPublished.txt
    • eg. augusta_chronicle_18890731.txt

The CSV file is organized according to the following column conventions:

  • id of text, full filename from within the index folder, journal title, publication date, text of article
  • eg. 1, index/august_chronicle_18890731.txt, augusta_chronicle, 1889-07-31, “lorem ipsum…”


The zip file contains two smaller folders and a CSV file. Each of these contains the same dataset organized in slightly different ways.

  • by_journal - 24.9 MB
  • index of all articles- 24.8 MB
  • casebook.csv - 18.4 MB
  • Total: 68.1 MB uncompressed

Data Quality

The text quality here is high, as the Casebook contributors transcribed them by hand.


Data collected and prepared by Brandon Walsh. Original dataset scraped from Casebook: Jack the Ripper and republished with their permission.

If working with the CSV data in Excel, you have a few extra steps to import the data. Excel has character limits on cells and other configurations that will make things go sideways unless you take precautions. Here are the steps to import the CSV file:

  1. Open Excel.
  2. Make a blank spreadsheet.
  3. Go to the Data menu.
  4. Click “Get External Data”.
  5. Select “Import Text File”.
  6. Navigate to your CSV file and select it.
  7. Select “Delimited” and hit next.
  8. In the next section, uncheck “Tab” and check “Comma”, click next.
  9. In the next section, click on the fifth column (the column one to the right of the date column).
  10. At the top of the window, select “Text” as the column data format.
  11. It will take a little bit to process.
  12. Click ‘OK’ for any popups that come up.
  13. It will still take a bit to process.

Your spreadsheet should now be populated with the Press Reports data.

Introduction to Text Analysis: A Coursebook

[Crossposted on the WLUDH blog]

I am happy to share publicly the initial release of a project that I have been shopping around in various talks and presentations for a while now. This semester, I co-taught a course on “Scandal, Crime, and Spectacle in the 19th Century” with Professor Sarah Horowitz in the history department here at Washington and Lee University. The course counted as digital humanities credit for our students, who were given a quick and dirty introduction to text analysis over the course of the term. In preparing for the class, I knew that I wanted my teaching materials on text analysis to be publicly available for others to use and learn from. One option might be to blog aggressively during the semester, but I worried that I would let the project slide, particularly once teaching got underway. Early conversations with Professor Horowitz suggested, instead, that we take advantage of time that we both had over the summer and experiment. By assembling our lesson plans far in advance, we could collaboratively author them and share them in a format that would be legible for publication both to our students, colleagues, and a wider audience. I would learn from her, she from me, and the product would be a set of resources useful to others.

At a later date I will write more on the collaboration, particularly on how the co-writing process was a way for both of us to build our digital skill sets. For now, though, I want to share the results of our work - Introduction to Text Analysis: A Coursebook. The materials here served as the backbone to roughly a one-credit introduction in text analysis, but we aimed to make them as modular as possible so that they could be reworked into other contexts. By compartmentalizing text analysis concepts, tool discussions, and exercises that integrate both, we hopefully made it a little easier for an interested instructor to pull out pieces for their own needs. All our materials are on GitHub, so use them to your heart’s content. If you are a really ambitious instructor, you can take a look at our section on Adapting this Book for information on how to clone and spin up your own copy of the text materials. While the current platform complicates this process, as I’ll mention in a moment, I’m working to mitigate those issues. Most importantly to me, the book focuses on concepts and tools without actually introducing a programming language or (hopefully) getting too technical. While there were costs to these decisions, they were meant to make any part of the book accessible for complete newcomers, even if they haven’t read the preceding chapters. The book is really written with a student audience in mind, and we have the cute animal photos to prove it. Check out the Preface and Introduction to the book for more information about the thinking that went into it.

The work is, by necessity, schematic and incomplete. Rather than suggesting that this be the definitive book on the subject (how could anything ever be?), we want to suggest that we always benefit from iteration. More teaching materials always help. Any resource can be a good one - bad examples can be productive failures. So we encourage you to build upon these materials in your courses, workshops, or otherwise. We also welcome feedback on these resources. If you see something that you want to discuss, question, or contest, please drop us a line on our GitHub issues page. This work has already benefited from the kind feedback of others, either explicit or implicit, and we are happy to receive any suggestions that can improve the materials for others.

One last thing - this project was an experiment in open and collaborative publishing. In the process of writing the book, it became clear that the platform we used for producing it - GitBook - was becoming a problem. The platform was fantastic for spinning up a quick collaboration, and it really paid dividends in its ease of use for writers new to Markdown and version control. But the service was new and under heavy development. Ultimately, the code was out of our control, and I wanted something more stable and more fully in my hands for long-term sustainability. I am in the process of transferring the materials to a Jekyll installation that would run off GitHub pages. Rather than wait for this final, archive version of the site to be complete, it seemed better to release this current working version out into the world. I will update all the links here once I migrate things over. If the current hosting site is down, you can download a PDF copy of the most recent version of the book here.

Update: I got around to doing that! You can find the new, improved, and more stable version of the site here.

Text Analysis Workshop: Four Ways to Read a Text

[Crossposted on the WLUDH blog.]

On Monday I visited Mackenzie Brooks’s course on “Data in the Humanities” to introduce digital text analysis to her students. I faced a few challenges when planning for the visit:

  • Scope - I had two hours for the workshop and a lot of material to cover. I was meant to introduce anything and everything, as much as I wanted in a general overview of text analysis.
  • Background - This course is an introductory digital humanities course that counts as a science credit at W&L, so I assumed no prior knowledge of programming. Mackenzie will be covering some things with them later in the course, but at this stage I needed to avoid anything really technical.
  • Length - Two hours was both a lot of time and no time at all. It was certainly not enough time to teach anyone to program for the first time. As an aside, I often find it hard to gauge how much material is appropriate for anything longer than 75 minutes.
  • Content - Since this was meant to be a general overview of the field, I did not want to lean too heavily on analysis by tools. I worried that if I did so the takeaway for the students would be how to use the tools, not the underlying concepts that the tools aided them in exploring.

I wound up developing a workshop I called “Introduction to Text Analysis: Four Ways to Read a Text.” Focusing on four ways meant that I felt comfortable cutting a section if things started to go long. It also meant that I was developing a workshop model that could easily fit varying lengths in the future. For example, I’ll be using portions of this workshop throughout my introduction to text analysis lectures in my own course this fall. The approach would necessarily be pretty distant - I couldn’t go into much detail for any one method in this time. Finally, I wanted the students to think about text analysis concepts first and then come to tools that would help them to do so, so I tried to displace the tools and projects from the conversation slightly. The hope was that, by enacting or intuiting the methods by hand first, the concepts would stick more easily than they might otherwise.

The basic structure of the workshop was this:

  1. I introduce a basic methodology for reading.
  2. Students are presented with a handout asking them to read in a particular way with a prompt from me. They complete the exercise.
  3. We talk about the process. We clarify the concept a little more together, and the students infer some of the basic difficulties and affordances of the approach.
  4. Then I show a couple tools and projects that use that method for real results.

The four ways of reading I covered were close reading, bags of words, topic modeling, and sentiment analysis. So, to use the topic modeling portion as an example, any one of those units looked something like this:

  1. I note how, until now, we have been discussing how counting words gives us a sense of the overall topic or scope of the text. Over time and in close proximity, individual words combine to give us a sense of what a text is about.
  2. I give the students three paragraphs with the words scrambled and out of order (done pretty quickly in Python). I ask the students to get in groups and tell me what the underlying topics or themes are for each excerpt. They had to produce three single-word topics for each paragraph, and paragraphs could share topics.
  3. We talk about how were able to determine the topics of the texts even with the paragraphs virtually unreadable. Even out of order, certain words in proximity together suggest the underlying theme of a text. We can think of texts as made up of a series of topics like these, clusters of words that occur in noticeable patterns near one another. We have human limits as to how much we can comprehend, but computers can help us run similar, mathematical versions of the same process to find out what words occur near each other in statistically significant patterns. The results can be thought of as the underlying topics or discourses that make up a series of documents. A lot of hand waving, I know, but I am assuming here that students will examine topic modeling in more detail at a later date. Better, I think, to introduce the broad strokes than lose students in the details.
  4. I then share Mining the Dispatch as an example of topic modeling in action to show the students the kinds of research questions that can be explored using this method.

So, in essence, what I tried to do is create a hands-on approach to teaching text analysis concepts that is flexible enough to fit a variety of needs and contexts. My handouts and slides are all up on a github repository. Feel free to share, reuse, and remix them in any way you would like.

Reading Speech: Virginia Woolf, Machine Learning, and the Quotation Mark

[Crossposted on the Scholars’ Lab blog as well as the WLUDH blog. What follows is a slightly more fleshed out version of what I presented this past week at HASTAC 2016 (complete with my memory-inflected transcript of the Q&A). I gave a bit more context for the project at the event than I do here, so it might be helpful to read my past two posts on the project here and here before going forward. This talk continues that conversation.]

This year in the Scholar’s Lab I have been working with Eric on a machine learning project that studies speech in Virginia Woolf’s fiction. I have written elsewhere about the background for the project and initial thoughts towards its implications. For the purposes of this blog post, I will just present a single example to provide context. Consider the famous first line of Mrs. Dalloway:

Mrs Dalloway said, “I will buy the flowers myself.”

Nothing to remark on here, except for the fact that this is not how the sentence actually comes down to us. I have modified it from the original:

Mrs Dalloway said she would buy the flowers herself.

My project concerns moments like these, where Woolf implies the presence of speech without marking it as such with punctuation. I have been working with Eric to lift such moments to the surface using computational methods so that I can study them more closely.

I came to the project by first tagging such moments myself as I read through the text, but I quickly found myself approaching upwards of a hundred instances in a single novel-far too many for me to keep track of in any systematic way. What’s more, the practice made me aware of just how subjective my interpretation could be. Some moments, like this one, parse fairly well as speech. Others complicate distinctions between speech, narrative, and thought and are more difficult to identify. I became interested in the features of such moments. What is it about speech in a text that helps us to recognize it as such, if not for the quotation marks themselves? What could we learn about sound in a text from the ways in which it structures such sound moments?

These interests led me towards a particular kind of machine learning, supervised classification, as an alternate means of discovering similar moments. For those unfamiliar with the concept, an analogy might be helpful. As I am writing this post on a flight to HASTAC and just finished watching a romantic comedy, these are the tools that I will work with. Think about the genre of the romantic comedy. I only know what this genre is by virtue of having seen my fair share of them over the course of my life. Over time I picked up a sense of the features associated with these films: a serendipitous meeting leads to infatuation, things often seem resolved before they really are, and the films often focus on romantic entanglements more than any other details. You might have other features in mind, and not all romantic comedies will conform to this list. That’s fine: no one’s assumptions about genre hold all of the time. But we can reasonably say that, the more romantic comedies I watch, the better my sense of what a romantic comedy is. My chances of being able to watch a movie and successfully identify it as conforming to this genre will improve with further viewing. Over time, I might also be able to develop a sense of how little or how much a film departs from these conventions.

Supervised classification works on a similar principle. By using the proper tools, we can feed a computer program examples of something in order to have it later identify similar objects. For this project, this process means training the computer to recognize and read for speech by giving it examples to work from. By providing examples of speech occurring within quotation marks, we can teach the program when quotation marks are likely to occur. By giving it examples of what I am calling ‘implied speech,’ it can learn how to identify those as well.

For this project, I analyzed Woolf texts downloaded from Project Gutenberg. Eric and I put together scripts in Python 3 that used a package known as the Natural Language Toolkit for classifying. All of this work can be found at the project’s GitHub repository.

The project is still ongoing, and we are still working out some difficulties in our Python scripts. But I find the complications of the process to be compelling in their own right. For one, when working in this way we have to tell the computer what features we want it to pay attention to: a computer does not intuitively know how to make sense of the examples that we want to train it on. In the example of romantic comedies, I might say something along the lines of “while watching these films, watch out for the scenes and dialogue that use the word ‘love.’” We break down the larger genre into concrete features that can be pulled out so that the program knows what to watch out for.

To return to Woolf, punctuation marks are an obvious feature of interest: the author suggests that we have shifted into the realm of speech by inserting these grammatical markings. Find a quotation mark-you are likely to be looking at speech. But I am interested in just those moments where we lose those marks, so it helps to develop a sense of how they might work. We can then begin to extrapolate those same features to places where the punctuation marks might be missing. We have developed two models for understanding speech in this way: an external and an internal model. To illustrate, I have taken a single sentence and bolded what the model takes to be meaningful features according to each model. Each represents a different way of thinking about how we recognize something as speech.

External Model for Speech:

“I love walking in London,” said Mrs. Dalloway. “Really it’s better than walking in the country.”

The external model was our initial attempt to model speech. In it, we take an interest in the narrative context around quotation marks. In any text, we can say that there exist a certain range of keywords that signal a shift into speech: said, recalled, exclaimed, shouted, whispered, etc. Words like these help the narrative attribute speech to a character and are good indicators that speech is taking place. Given a list of words like this, we could reasonably build a sense of the locations around which speech is likely to be happening. So when training the program on this model, we had the classifier first identify locations of quotation marks. Around each quotation mark, the program took note of the diction and parts of speech that occurred within a given distance from the marking. We build up a sense of the context around speech.

Internal Model for Speech:

I love walking in London,” said Mrs. Dalloway. “Really it’s better than walking in the country.”

The second model we have been working with works in an inverse direction: instead of taking an interest in the surrounding context of speech, an internal model assumes that there are meaningful characteristics within the quotation itself. In this example, we might notice that the shift to the first-person ‘I’ is a notable feature in a text that is otherwise largely written in the third person. This word suggests a shift in register. Each time this model encounters a quotation mark it continues until it finds a second quotation mark. The model then records the diction and parts of speech inside the pair of markings.

Each model suggests a distinct but related understanding for how sound works in the text. When I set out on this project, I had aimed to use the scripts to give me quantifiable evidence for moments of implied speech in Woolf’s work. The final step in this process, after all, is to actually use these models to identify speech: looking at texts they haven’t seen before, the scripts insert a caret marker every time they believe that a quotation mark should occur. But it quickly became apparent that the construction of the algorithms to describe such moments would be at least as interesting as any results that the project could produce. In the course of constructing them, I have had to think about the relationships among sound, text, and narrative in new ways.

The algorithms are each interpretative in the sense that they reflect my own assumptions about my object of study. The models also reflect assumptions about the process of reading, how it takes place, and about how a reader converts graphic markers into representations of sound. In this sense, the process of preparing for and executing text analysis reflects a certain phenomenology of reading as much as it does a methodology of digital study. The scripting itself is an object of inquiry in its own right and reflects my own interpretation of what speech can be. These assumptions are worked and reworked as I craft algorithms and python scripts, all of which are as shot through with humanistic inquiry and interpretive assumptions as any close readings.

For me, such revelations are the real reasons for pursuing digital study: attempting to describe complex humanities concepts computationally helps me to rethink basic assumptions about them that I had taken for granted. In the end, the pursuit of an algorithm to describe textual speech is nothing more or less than the pursuit of deeper and enriched theories of text and speech themselves.


I managed to take note of the questions I got when I presented this work at HASTAC, so what follows are paraphrases of my memory of them as well as some brief remarks that roughly reflect what I said in the moment. There may have been one other that I cannot quite recall, but alas such is the fallibility of the human condition.

Q: You distinguish between speech and implied speech, but do you account at all for the other types of speech in Woolf’s novels? What about speech that is remembered speech that happened in earlier timelines not reflected in the present tense of the narrative’s events?

A: I definitely encountered this during my first pass at tagging speech and implied speech in the text by hand. Instead of binaries like quoted speech/implied speech, I found myself wanting to mark for a range of speech types: present, actual; remembered, might not have happened; remembered incorrectly; remembered, implied; etc. I decided that a binary was more feasible for the machine learning problems that I was interested in, but the whole process just reinforced how subjective any reading process is: another reader might mark things differently. If these processes shape the construction of the theories that inform the project, then they necessarily also affect the algorithms themselves as well as the results they can produce. And it quickly becomes apparent that these decisions reflect a kind of phenomenology of reading as much as anything: they illlustrate my understanding of how a complicated set of markers and linguistic phenomenon contribute to our understanding that a passage is speech or not.

Q: Did you encounter any variations in the particular markings that Woolf was using to punctuate speech? Single quotes, etc., and how did you account for them?

A: Yes - the version of Orlando that I am working with used single quotes to notate speech. So I was forced to account for such edge cases. But the question points at two larger issues: one authorial and one bibliographical. As I worked on Woolf I was drawn to the idea of being able to run such a script against a wider corpus. Since the project seemed to impinging on how we also understand psychologized speech, it would be fascinating to be able to search for implied speech in other authors. But, if you are familiar with, say, Joyce, you might remember that he hated quotation marks and used dashes to denote speech. The question is how much can you account for such edge cases, and, if not, the study becomes only one of a single author’s idiosyncrasies (which still has value). But from there the question spirals outwards. At least one of my models (the internal one) relies on quotation marks themselves as boundary markers. The model assumes that quotation marks will come in pairs, and this is not always the case. Sometimes authors, intentionally or accidentally, omit a closing quotation mark. I had to massage the data in at least half a dozen places where there was no quotation mark in the text and where its lack was causing my program to fail entirely. As textual criticism has taught us, punctuation marks are the single most likely things to be modified over time during the process of textual transmission by scribes, typesetters, editors, and authors. So in that sense, I am not doing a study of Woolf’s punctuation so much as a study of Woolf’s punctuation in these particular versions of the texts. One can imagine an exhaustive study that works on all versions of all Woolf’s texts as a study that might approach some semblance of a correct and thorough reading. For this project, however, I elected to take the lesser of two evils that would still allow me to work through the material. I worked with the texts that I had. I take all of this as proof that you have to know your corpus and your own shortcomings in order to responsibly work on the materials - such knowledge helps you to validate your responses, question your results, and reframe your approaches.

Q: You talked a lot about text approaching sound, but what about the other way around - how do things like implied speech get reflected in audiobooks, for example? Is there anything in recordings of Woolf that imply a kind of punctuation that you can hear?

A: I wrote about this extensively in my dissertation, but for here I will just say that I think the textual phenomenon the questioner is referencing occurs on a continuum. Some graphic markings, like pictures, shapes, punctuation marks, do not clearly translate to sound. And the reverse is true: the sounded quality of a recording can only ever be remediated by a print text. There are no perfect analogues between different media forms. Audiobook performers might attempt to convey things like punctuation or implied speech (in the audiobook of Ulysses, for example, Jim Norton throws his voice and lowers his volume to suggest free indirect discourse). In the end, I think such moments are playing with an idea of what my dissertation calls audiotextuality, the idea that all texts recordings of texts, to varying degrees, contain both sound and print elements. The two spheres may work in harmony or against each other as a kind of productive friction. The idea is a slippery one, but I think it speaks to moments like the implied punctuation mark that come through in a particularly powerful audiobook recording.

Apps, Maps, & Models: A New View

[Crossposted on the Washington and Lee University Digital Humanities Blog]

Last Monday several of us here at WLUDH traveled down to Duke University for their symposium on Apps, Maps & Models: Digital Pedagogy in Art History, Archaeology & Visual Studies. I found the trip to be enlightening and invigorating. If you are interested in the event, you can find videos of the talks here and here as well as a storify of the Twitter action here. That the event was so well documented is a testimony to how well organized it was by the Wired! Lab.

Many speakers at the event considered how the tools they were using might relate to more “traditional” modes for carrying out their research. They considered and responded to tough questions with and about their work. Are digital methods for tracing the topography of a surface, for example, fundamentally different in kind from analog means of doing so? If so, are they meant to displace those old tools? Why should we spend the time to learn the new technologies? A related question that comes up at almost every digital humanities presentation (though not at any of these): can digital humanities methods show us anything that we do not already know?

Such questions can be particularly troubling when we are investing such time and energy on the work they directly critique, but we nonetheless need to have answers for them that demonstrate the value of digital humanities work, in and out of the classroom. Numerous well-known scholars have offered justifications of digital work in a variety of venues, and, to my mind, the symposium offered many answers of its own, in part by showcasing amazing work that spanned a variety of fields related to preservation, public humanities, and academic scholarship. Presenters were using digital technology to rebuild the past, using digital modeling to piece together the fragments of a ruined church that have since been incorporated into other structures. They were using these tools to engage the present, to draw the attention of museum patrons to overlooked artifacts. The work on display at the symposium struck me, at its core, as engaging with questions and values that cut across disciplines, digital or otherwise.

Most compelling to me, the symposium drew attention to how the tools we use to examine the objects of our study change our relationship to them. The presenters acknowledged that such an idea does hold dangers – after all, we want museum-goers to consider the objects in a collection, not just spend time perusing an iPad application meant to enrich them. But just as new tools offer new complications, changes in medium also offer changes in perspective. As was illustrated repeatedly at the symposium, drone photography, for all its deeply problematic political and personal valences, can offer you a new way of seeing the world, a new way of looking that is more comprehensive than the one we see from the ground. Even as we hold new methodologies and tools up to critique we can still consider how they might cause us to consider an object, a project, or a classroom differently.

Seeing from a different angle allows us to ask new questions and re-evaluate old ones, an idea that speaks directly to my experience at the symposium. I work at the intersections of digital humanities, literary studies, and sound studies. So my participation in the symposium was as something of an outsider, someone ready to learn about an adjacent and overlapping field but, ultimately, not a home discipline. Thinking through my work from an outsider perspective made me want to ask many questions of my own work. The presenters here were deeply engaged in preserving and increasing access to the cultural record. How might I do the same through text analysis or through my work with audio artifacts? What questions and goals are common to all academic disciplines? How might I more thoroughly engage students in public humanities work?

Obviously, the event left me with more questions than answers, but I think that is ultimately the sign of a successful symposium. I would encourage you to check out the videos of the conference, as this short note is necessarily reductive of such a productive event. The talks will offer you new thoughts on old questions and new ways of thinking about digital scholarship no matter your discipline.