The field of computational research and laboratory management is moving towards increased automation, reproducibility, and efficiency. Researchers are developing innovative systems and tools to streamline experiments, manage complex workflows, and integrate AI-driven decision-making. Virtual laboratories and orchestration systems are being designed to capture rich metadata, support computational reproducibility, and enhance collaboration. Additionally, advancements in computational notebooks, such as two-dimensional code+data space versioning and checkpointing, are improving user productivity and facilitating nonlinear data exploration. Noteworthy papers include: Accelerating drug discovery with Artificial, which presents a comprehensive orchestration and scheduling system for self-driving labs, and Enhancing Computational Notebooks with Code+Data Space Versioning, which introduces a novel versioning mechanism for computational notebooks. Large-scale Evaluation of Notebook Checkpointing with AI Agents also presents a significant study on the impact of checkpointing on user productivity.