Natural history collections house immense amounts of data, but the majority of data is only accessible by locating the collection label, which is usually attached to the physical specimen. This method of data retrieval is time consuming and can be very damaging to fragile specimens. Digitizing the collections is the one way to reduce the time and potential damage related to finding the collection objects. The Elm Fork Natural Heritage Museum is a natural history museum located at the University of North Texas and contains collections of both vertebrate and invertebrate taxa, as well as plants. This project designed a collection digitization workflow for Elm Fork by working through digitizing the Benjamin B. Harris Herbarium. The collection was cataloged in Specify 6, a database program designed for natural history collection management. By working through one of the museum’s collections, the project was able to identify and address challenges related to digitizing the museum’s holdings in order to create robust workflows. The project also produced a series of documents explaining common processes in Specify and a data management plan.

This study examined the impact of the National Science Foundation's Research Experience for Teachers (RET) in engineering at University of North Texas on students after their teachers' participation in the program. Students were evaluated in terms of self-efficacy, knowledge of engineering, perceptions of engineering, and interest in engineering. A 22-item Likert pre/post survey was used for analysis, and participants included 589 students from six high schools, one middle school, and one magnet school. Paired surveys were analyzed to determine if there was a statistically significant difference in attitudes and knowledge after teachers implemented lessons from their time at the RET. Surveys were also analyzed to determine if there was a statistically significant difference in student response based on gender or student school type. Results showed no statistically significant difference in the self-efficacy of students, however there was a statistically significant difference in knowledge, perceptions, and interest in engineering. In addition, there was a statistically significant difference between genders on an isolated question, and seven out of the 22 Likert questions showed a statistically significant difference between student school types.