Data management plan for the grant, "Collaborative Research: Diversifying Human-Centered Data Science through the Research and Design of Ethical Games." This project studies collaborative learning in a virtual setting for groups of peers with conflicting methodological cultures and diverse backgrounds. An additional artifact of this two-year, interdisciplinary project will be a prototype of an educational game, targeted towards the underrepresented communities and the greater public, that will bring more people into critical conversations about the roles of AI and data science in our society. The primary aim of this research is the production of novel theories in learning sciences regarding collaborative learning in the face of diverse backgrounds and cultural norms.

Data management plan for the grant "Programmatic Extraction of ‘Documents' from Web Archives." This research project seeks to evaluate the use of machine learning algorithms to successfully identify and extract publications contained in existing Web archives. Identifying these documents will empower libraries, archives, and museums to meet their curatorial missions.

Data management plan for a grant seeking to analyze existing support structures for transfer students in Chemistry between the University of North Texas and Dallas College.

Data management plan for the grant, "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure." This project aims to develop a novel set of interactive training materials, including hands-on lecture modules, invited research talks from renowned researchers, and an interdisciplinary collaborative project in an intensive workshop, integrating a wide variety of advanced and inter-connected techniques employed by research workforce for deep learning (DL) systems in advanced GPU cyberinfrastructure (CI). Specifically, this project focuses on training seniors, graduate students, and researchers on how advanced GPU CI can be efficiently utilized and improved to enable high-performance DL systems for data-intensive DL applications in geoscience (GS) and computer science and engineering (CSE) research. The goal is to foster future CI users and contributors to adopt, develop, and improve advanced GPU CI for DL systems in their research.

Data management plan for the grant, "UNT Texas Fashion Collection: Assessment and Preservation Training."