This paper presents a case study of the work carried out by the University of North Texas (UNT) Libraries and the intersection of metadata modeling, metadata input rules and documentation, metadata quality assessments, and technology to empower metadata editors to create high-quality metadata.

This presentation contains a pre- and post-workshop surveys, with an overview of digital libraries, digital lifecycle management, and digital preservation.

Poster presented at the 2013 Texas Conference on Digital Libraries. This poster discusses research on the current quality assurance practices in the web archiving community.

This document provides a status report on the Z39.50 Interoperability Testbed Project (Z-Interop) covering the period of December 1, 2000 through April 30, 2001. The authors highlight activities and accomplishments to communicate to IMLS progress on their project. This period can be considered a project startup period.

This report is part of the Web-at-Risk project. The Web-at-Risk project is one of eight digital preservation projects funded in 2004 by the Library of Congress. Each of the projects represents a collaborative effort to preserve for future generations born-digital or digitized cultural heritage materials and collections. The Web-at-Risk project is a 3-year collaborative effort of the California Digital Library, the University of North Texas (UNT), and New York University. The project will develop a Web Archiving Service that enables curators to build collections of web-published materials. The content will be collected largely from US federal and state government agencies, but will also include political policy documents, campaign literature, and information surrounding political movements. The project work will be conducted along four paths of overlapping activities. The Web-at-Risk Project work paths include (1) Content identification, selection, and acquisition; (2) Content harvest and analysis; (3) Content ingest, retention, and transfer; and (4) Partnership building.

This paper presents a method for automatic topic identification using an encyclopedic graph derived from Wikipedia. The system is found to exceed the performance of previously proposed machine learning algorithms for topic identification, with an annotation consistency comparable to human annotations.