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

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

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

Increasing Our Vision for 21st-Century Digital Libraries

This presentation Reads digital library interfaces—or their main door interfaces—as glimpses into what we have thus far valued in the development of digital libraries Frames a visual way of thinking about textual materials Introduces the work of our research team—where we are now, and where we\u27re headed Draws some connections between the parts This presentation is very much a look into thinking in process and work in progress and proposes the following ideas: As a community, we can do much more with the digital images we\u27re creating of textual materials than we\u27ve heretofore done. We aspire to have additional layers or levels of image analysis become part of the default processing work in the creation of digital libraries, not only as something that happens external or parallel to digital libraries, and not only toward the purpose of generating text. We aspire to more processing up front and iterative processing of materials—so that digital libraries\u27 materials are not once and done —and that this more processing is presented to users as additional options for how they can explore digital libraries, find materials of relevance, and imagine new possibilities Even as the digital libraries community focuses on supporting computational use of digital libraries—and our research team recognizes that our project very much depends on that computational use being supported—we should not leave behind, in 1998, those users of digital libraries for whom computational use is not their point of entry. (More on that date in a moment.

Developing an Image-Based Classifier for Detecting Poetic Content in Historic Newspaper Collections

Developing an Image-Based Classifier for Detecting Poetic Content in Historic Newspaper Collections details and analyzes the first stage of work of the Image Analysis for Archival Discovery project team. Our team is is investigating the use of image analysis to identify poetic content in historic newspapers. The project seeks both to augment the study of literary history by drawing attention to the magnitude of poetry published in newspapers and by making the poetry more readily available for study, as well as to advance work on the use of digital images in facilitating discovery in digital libraries and other digitized collections. We have recently completed the process of training our classifier for identifying poetic content, and as we prepare to move in to the deployment stage, we are making available our methods for classification and testing in order to promote further research and discussion. The precision and recall values achieved during the training (90.58%; 79.4%) and testing (74.92%; 61.84%) stages are encouraging. In addition to discussing why such an approach is needed and relevant and situating our project alongside related work, this paper analyzes preliminary results, which support the feasibility and viability of our approach to detecting poetic content in historic newspaper collections

Increasing Our Vision for 21st-Century Digital Libraries

This presentation Reads digital library interfaces—or their main door interfaces—as glimpses into what we have thus far valued in the development of digital libraries Frames a visual way of thinking about textual materials Introduces the work of our research team—where we are now, and where we\u27re headed Draws some connections between the parts This presentation is very much a look into thinking in process and work in progress and proposes the following ideas: As a community, we can do much more with the digital images we\u27re creating of textual materials than we\u27ve heretofore done. We aspire to have additional layers or levels of image analysis become part of the default processing work in the creation of digital libraries, not only as something that happens external or parallel to digital libraries, and not only toward the purpose of generating text. We aspire to more processing up front and iterative processing of materials—so that digital libraries\u27 materials are not once and done —and that this more processing is presented to users as additional options for how they can explore digital libraries, find materials of relevance, and imagine new possibilities Even as the digital libraries community focuses on supporting computational use of digital libraries—and our research team recognizes that our project very much depends on that computational use being supported—we should not leave behind, in 1998, those users of digital libraries for whom computational use is not their point of entry. (More on that date in a moment.

Interim Report, HD-51897-14, Image Analysis for Archival Discovery (Aida), January 2016

In the third six months of work on Image Analysis for Archival Discovery, the project team has made progress toward the goals outlined in our report from June 2015. As we reported in June 2015, we realized that our original plan to analyze 7 million pages from Chronicling America was overly ambitious for the grant period, and we revised our goal to complete a thorough case study of our methodology and code for all newspaper images in Chronicling America from the period 1836-1840. Activities undertaken, toward this and other grant goals, from June 2015–December 2015: Publication of the article, Developing an Image-Based Classifier for Detecting Poetic Content in Historic Newspaper Collections, D-Lib Magazine (July/August 2015). doi: 10.1045/july2015-lorang Completed pre-alpha version of complete code base Continued development of project documentation Made progress in case study of images from 1836-1840 Developed partnership with a researcher from the University of Virginia Discussed future project directions with Institute of Museum and Library Services staff Filed for no-cost extension to continue grant work through June 201

Interim Report, HD-51897-14, Image Analysis for Archival Discovery (Aida), June 2015

In the second six months of work on Image Analysis for Archival Discovery, the project team has continued making strides toward our goal of analyzing more than 7 million newspaper pages in Chronicling America for poetic content. We have hit a few challenging areas in our research and development work, and our work plan has shifted in some ways from that originally set out in our application, but we have implemented these changes with the fundamental goal of performing the major research outlined in our proposal—exploring image analysis as a methodology for discovery in digitized collections of historic materials via a case study of identifying poetic content in historic newspapers. Activities undertaken from December 2014–May 2015: Development of an article describing the creation of our classifier for recognizing poetic content in historic newspapers; accepted and forthcoming in July/August 2015 D-Lib (completed) Development of Python program for parsing Chronicling America JSON files and batch retrieving JPEG 2000 image files (completed) Operationalization of entire process, from image retrieval to image processing, including moving to server environment (in progress) Development of project documentation (completed to current stage) Processing and classifying all Chronicling America images from the period 1836-1840 as a test case (in progress) Communication of results with relevant audiences, such as at the American Literature Association conference and via project website (completed) Pursuit of external partnership and additional source of funding: submission of Google Faculty Research Award application (completed; decision pending