Presented at the 2015 Digital Library Federation Forum as part of the panel, "Think Globally, Act Locally: How Working with DPLA has Improved Our Collections." This presentation provides an overview of how working as an aggregator for the Digital Public Library of America (DPLA) has changed workflows related to digital collections for The Portal to Texas History.
This is the collection for the End of Term Presidential Harvest 2016, an effort by the Library of Congress, the California Digital Library, the University of North Texas Libraries, the Internet Archive, George Washington University Libraries, Stanford University Libraries, and the U.S. Government Printing Office to preserve public United States Government web sites at the end of the presidential term that ended January 20, 2017. This collection documents federal agencies' presence on the World Wide Web during the transition of Presidential administrations.
This paper reports on exploratory investigations by the Texas Digital Newspaper Program to understand aggregate patterns in the generation of born-digital news editions by analyzing technical metadata extracted from the 3 million pages currently in the preservation collection.
Photograph of the spillway for Lake Texoma near Denison, Texas. There are multiple images of the same people in the image as they move around the camera while the image is being taken. This photograph is a stitched 360-degree panoramic image.
Presentation for the 2017 International Conference on Knowledge Management as part of the panel "Big Data and Government Information." This presentation describes the work of the End of Term Archive to preserve the federal web.
Book chapter on the Trusted Repository Audit and Checklist (TRAC) process through the lens of contextual dimensions within knowledge management, using the case study of the University of North Texas Libraries.
October 26, 2017
Krahmer, Ana; Andrews, Pamela; Tarver, Hannah; Phillips, Mark Edward & Alemneh, Daniel Gelaw
Poster presented at the 2017 Annual Meeting of the Association for Information Science & Technology. This poster describes a study to investigate automatic type classification using machine learning approaches.