Efficient data movement is a critical challenge in high-performance computing (HPC) and artificial intelligence (AI) cyberinfrastructures due to the massive volumes of data generated by modern data-intensive applications. Existing methods often struggle with performance bottlenecks, particularly when transferring data across parallel and distributed computing environments. To address these limitations, this project -- the Open DPU-Offloading data Transfer Architecture (OpenDOTA) -- provides a framework that leverages Data Processing Units (DPUs) to accelerate data movement. By enhancing efficiency in DPU-powered systems, OpenDOTA aims to advance scientific simulations, drive AI advancements, and strengthen computational research infrastructure. The project fosters collaboration and contributes to the evolution of state-of-the-art data movement technologies, benefiting a wide range of users in academia and industry. This project focuses on designing OpenDOTA as a high-performance, scalable framework for DPU-offloaded data movement in HPC and AI cyberinfrastructures. The research is structured around three key thrusts: (1) Adaptive point-to-point data movement, which employs diverse offloading strategies to optimize data transfer over DPUs; (2) Accelerating collective communication by leveraging advanced DPU offloading techniques to improve scalability; and (3) Deep reinforcement learning (DRL)-based optimization, which dynamically adapts data movement strategies for maximum