Text of the keynote address delivered during the University of North Texas Lavender Graduation at the end of the Fall 2015 semester.

This two-year project, led by the University of North Texas Libraries, will broaden access to 141 books selected by one of three publishers or by the UNT Libraries for their relevance to the history of Texas and Oklahoma. It will also broaden access to five humanities-related books to be selected once the grant starts. Those books not yet available online through the Gateway to Oklahoma History, Portal to Texas History, or UNT Digital Library websites will be digitized, with full-text searching, and added to the appropriate site. All books will be converted to EPUB and Kindle formats, made available to download with the digitized version online, and added to the UNT Library Catalog and to WorldCat. The project also includes the production of “print-ready” PDFs from the scans of many of the books to allow them to be made available for sale in print again using print-on-demand technology. This proposal was submitted in June 2015 and funded in December 2015 by the National Endowment for the Humanities for $95,599.

Evaluating the impact of learning from climate data, in addition to bridge data, on the performance of concrete deck condition rating prediction is critical for identifying the right data needed to enhance bridge maintenance decision making. Few studies have considered such an evaluation and utilized a small size of samples that prevent revealing the knowledge hidden within the big size of data. Although, such evaluation over big data seems quite necessary, class imbalance problem makes it challenging. To alleviate such a problem, five states, including Alabama, Iowa, New York, Pennsylvania, and South Carolina, were selected as the case study. Not only are the states located in three different climatically consistent regions defined by the National Ocean and Atmospheric Administration (NOAA), but also their concrete deck conditions ratings are somewhat balanced. To conduct the evaluation, this research developed the bridge data set pertaining to 56,288 bridges across the afore-mentioned states through employing the GIS technology. The bridge data set contains bridge data derived from National Bridge Inventory (NBI), and climate data derived from Parameter-elevation Relationships on Independent Slopes Model (PRISM) climate maps and NOAA. Then, two machine learning algorithms, including random forest and GBM, were trained - with and without climate …

Notes accompanying a presentation on the Crazy Water radio broadcasts in the UNT Music Library. This presentation was selected for the 2020 Best of Chapters presentations at the Music Library Association conference in Norfolk, VA held on February 26-March 1, 2020.