Data management plan for the grant, "CAREER: Orbital-based Descriptors for Dynamical Properties of Quantum Defects."

Data management plan for the grant, "CAS: Highly Interacting Panchromatic Push-Pull Systems: Symmetry Breaking and Quantum Coherence in Electron Transfer."

Data management plan for the grant "Charge Transport in Two-Dimensional Materials Based Integrated Flexible Energy System."

Data management plan for the grant, "Collaborative Research: Topological Defects and Dynamic Motion of Symmetry-breaking Tadpole Particles in Liquid Crystal Medium."

Data management plan for the grant, "Conference: Dynamical Systems and Fractal Geometry."

Data management plan for the grant "Feasibility and initial efficacy of a wheelchair exercise-training intervention for persons with multiple sclerosis." The proposed study will examine the feasibility and initial efficacy of a stakeholder-informed, home-based, remotely supported and supervised exercise training program for wheelchair users with multiple sclerosis (MS). The proposed exercise training program was iteratively developed in partnership with community members using semi-structured interviews, a community advisory board, and focus group feedback wherein community members provided insights to develop and refine an exercise training program that suit the needs and preferences of wheelchair users with MS. Ultimately, the proposed study extends this line of research and may initiate a significant paradigm shift in rehabilitation research and practice by providing a critically needed home-based exercise training program for enhancing health, quality of life, participation, and independence of wheelchair user with MS.

Data management plan for the grant "A Qualitative Inquiry into Hispanic-Serving Institutions/R1 Identity and Organizational Culture."

Data management plan for the grant, "RAPID: Immediate Responses during a Major Nighttime Tsunami Event."

Data management plan for the grant "School Mental Health Advocates for Students and Educators (SMHASE)."

Data management plan for the grant, "Ultra-High Temperature Stable Morphing Materials for Hypersonic Applications"

Data management plan for the research grant, "Using uncertainty quantification and machine learning techniques to study the evolution of odor capture." This research proposes the application of uncertainty quantification (UQ) and machine learning (ML) to a CFD model of odor capture to understand the role of hair-array morphology, kinematics, and fluid environment in odor capture. The combination of CFD modeling and UQ&ML techniques can map out the performance space under which these chemosensory hair arrays operate and the relative sensitivity of each parameter of odor capture to construct a global, quantitative understanding of how parameters control odor-capture performance. Furthermore, this analysis can eliminate parameters that have no influence on odor capture, extracting the root principles of odor capture and providing a more efficient way to construct bioinspired devices for chemical detection. This work is of interest to the Army for extracting design principles that can be used for biomimetic and/or bioinspired devices for sensing hazardous chemicals in the environment (e.g. explosives).