"Two More Throws against Oblivion": Walt Whitman and the New York Herald in 1888

Examines Whitman\u27s complex publishing relationship with the New York Herald from December 1887 through August 1888, when the poet published "a total of thirty-six pieces" there, more than he published in any other periodical, and proposes that this relationship reveals Whitman\u27s understanding of "certain formal qualities" expected of newspaper poetry as he "worked within this poetic tradition, crafting short poems that could be understood by a mass readership and that participated in the public discourse of the community in which they were published.

Patterns, Collaboration, Practice: Algorithms as Editing for Historic Periodicals

This presentation positions my recent work on the algorithmic “discovery” of poetic material in historic newspapers within the contexts of my various roles as an editor of periodical literature and also consider how duplicative processes and algorithms encode principles and values and function as editorial acts. Ultimately, I hope to pose a range of questions to prompt discussion around the place (or not) of machine learning in identifying and selecting texts and bodies of work; what ideas we’re actually exploring/are able to explore when we enlist technology in stages of this work; and the stakes of these activities, whether human or machine, for periodicals from under-represented communities in particular

Image Analysis for Archival Discovery (Aida)

Images created in the digitization of primary materials contain a wealth of machine-processable information for data mining and large-scale analysis, and this information should be leveraged both to connect researchers with the resources they need and to augment interpretation of human culture, as a complement to and extension of text-based approaches. The proposed project, "Image Analysis for Archival Discovery" (Aida), applies image processing and machine learning techniques from computer science to digitized materials to facilitate and promote archival discovery. Beginning with the automatic detection of poetic content in historic newspapers, this project will develop image processing as a methodology for humanities research and analysis. In doing so, it will advance work on two fronts: 1) it will contribute to the reevaluation of newspaper verse in American literary history; 2) it will assess the application of image analysis as a method for discovery in archival collections

Teaching Digital Humanities through a Community-Centered, Team-Based Pedagogy

Through a focus on the Digital Humanities Practicum course at the University of Nebraska-Lincoln (UNL), this paper explores two areas of current--and recurrent--interest in digital humanities teaching and learning: DH pedagogy in the undergraduate classroom and DH and skills training. While the presentation emphasizes particulars of the course, including its design, what has worked well, and what we are still learning, we also want to think beyond the single course and prompt further discussion around several themes, including team-based problem-solving and connecting digital humanities with community-engaged learning. Ultimately, we argue that a team-based, community-engaged approach can be an effective strategy for teaching digital humanities practice to students. Furthermore, we believe that this approach can powerfully illustrate the societal benefit of humanities-centered approaches to problem-solving. Students in the Digital Humanities Practicum course get an opportunity to work together creatively, analyze a problem and conceive a solution, build something, and have a positive impact on their community

Final Report, HD-51897-14, Image Analysis for Archival Discovery (Aida), October 2016

With its Office of Digital Humanities Start-up Grant, the Image Analysis for Archival Discovery (Aida) team set out to further develop image analysis as a methodology for the identification and retrieval of items of relevance within digitized collections of historic materials. Specifically, we sought to identify poetic content within historic newspapers, using Chronicling America\u27s newspapers (http://chroniclingamerica.loc.gov/) as our test case. The project activities we undertook—both those completed and those in process—support this goal and align well with the activities proposed in our original funding application and as approved by NEH. To achieve our goal of creating an image processing-based system to identify poetic content in historic newspaper collections, however, we also made strategic decisions along the way that shifted some of our efforts from those we initially planned when we drafted our funding proposal three years ago. During the grant period, the Aida team developed, trained, and tested a machine learning classifier that can identify poetic content in pages of digitized historic newspapers based only on visual signals. We published early results of this work in D-Lib Magazine in summer 2015. We have since undertaken a detailed case study that tests the application of our classifier and methodology to a test set of more than 22,000 newspaper page images from the period 1836-1840. Significantly, we shifted our emphasis from processing all pages from Chronicling America to conducting this thorough, critical analysis and case study. This shift in plans corresponds with our desire to explore image analysis as a methodology for connecting users of digital archives with materials of relevance

Tools for the digital humanities : a librarian's guide

This brochure presents a collection of digital humanities tools that are currently being used by professional scholars, graduate students and persons in information science. The tools selected are aimed at humanities subject librarians who might be asked for ideas by their patrons. These tools are also an attractive option for librarians and information science professionals who are looking for options to digitize their institution’s collections or who would like to publish research projects. Many of these tools are created with users in mind who may not have any IT experience. Because of this, more people have the capability of using technologies they wouldn’t have before.Data preparation and early-stage research technologies. Scrivener ; OpenRefine ; Medieval Handwriting App -- Analysis technologies. Voyant ; Piktochart ; Gephi ; Palladio -- Publishing technologies. Scalar ; Camtasia ; Omeka

Final Report, HD-51897-14, Image Analysis for Archival Discovery (Aida), October 2016

With its Office of Digital Humanities Start-up Grant, the Image Analysis for Archival Discovery (Aida) team set out to further develop image analysis as a methodology for the identification and retrieval of items of relevance within digitized collections of historic materials. Specifically, we sought to identify poetic content within historic newspapers, using Chronicling America\u27s newspapers (http://chroniclingamerica.loc.gov/) as our test case. The project activities we undertook—both those completed and those in process—support this goal and align well with the activities proposed in our original funding application and as approved by NEH. To achieve our goal of creating an image processing-based system to identify poetic content in historic newspaper collections, however, we also made strategic decisions along the way that shifted some of our efforts from those we initially planned when we drafted our funding proposal three years ago. During the grant period, the Aida team developed, trained, and tested a machine learning classifier that can identify poetic content in pages of digitized historic newspapers based only on visual signals. We published early results of this work in D-Lib Magazine in summer 2015. We have since undertaken a detailed case study that tests the application of our classifier and methodology to a test set of more than 22,000 newspaper page images from the period 1836-1840. Significantly, we shifted our emphasis from processing all pages from Chronicling America to conducting this thorough, critical analysis and case study. This shift in plans corresponds with our desire to explore image analysis as a methodology for connecting users of digital archives with materials of relevance

White Paper, HD-51897-14, Image Analysis for Archival Discovery (Aida), October 2016

With its Office of Digital Humanities Start-up Grant, the Image Analysis for Archival Discovery (Aida) team set out to further develop image analysis as a methodology for the identification and retrieval of items of relevance within digitized collections of historic materials.1 Specifically, we sought to identify poetic content within historic newspapers, using Chronicling America\u27s newspapers (http://chroniclingamerica.loc.gov/) as our test case. The project activities we undertook—both those completed and those in process—support this goal and align well with the activities proposed in our original funding application and as approved by NEH. To achieve our goal of creating an image processing-based system to identify poetic content in historic newspaper collections, however, we also made strategic decisions along the way that shifted some of our efforts from those we initially planned when we drafted our funding proposal three years ago. During the grant period, the Aida team developed, trained, and tested a machine learning classifier that can identify poetic content in pages of digitized historic newspapers based only on visual signals. We published early results of this work in D-Lib Magazine in summer 2015. We have since undertaken a detailed case study that tests the application of our classifier and methodology to a test set of more than 22,000 newspaper page images from the period 1836-1840. Significantly, we shifted our emphasis from processing all pages from Chronicling America to conducting this thorough, critical analysis and case study. This shift in plans corresponds with our desire to explore image analysis as a methodology for connecting users of digital archives with materials of relevance

Interim Performance Report, LG‐71‐16‐0152‐16, Extending Intelligent Computational Image Analysis for Archival Discovery, March 2019

The primary goal of Extending Intelligent Computational Image Analysis for Archival Discovery is to investigate the use of image analysis as a methodology for content identification, description, and information retrieval in digital libraries and other digitized collections. Building on work started under a National Endowment for the Humanities\u27 Office of Digital Humanities Start-up Grant, our IMLS project seeks to 1) analyze and verify our previously developed image analysis approach and extend it so that it is newspaper agnostic, type agnostic, and language agnostic; 2) scale and revise the intelligent image analysis approach and determine the ideal balance between precision and recall for this work; 3) distribute metadata and develop a new digital collection using the extracted content; and 4) disseminate results, including adding to the scholarly literature on these topics and providing training for members of library and archive communities. In the second year of the project, the Aida team made considerable headway in the goals of our grant. While we have continued to focus exclusively on poetic content to this point, year two was an important year for assessing the efficacy of the approach and extending it such that it might be newspaper- and language-agnostic. In addition, we assembled a large set of data and evidence to help us consider the balance of precision and recall as well as to consider revisions to the overall approach given what we’re learning in this area. We also have a functional metadata model and have made major steps toward developing a new digital collection out of the poetic content observed during the project, and for distributing metadata about the content. Finally, team members shared about the work at four major conferences, to audiences of digital library professionals and specialists and literary scholars. Team members prepared three publications, which are currently out for review, a detailed report analyzing the extension of the approach to a new corpus and have generated notes toward additional articles and other writing for year 3