The field of data science is witnessing significant developments in data visualization and discovery, with a focus on improving the efficiency and effectiveness of data analysis. Researchers are exploring new methods for visualizing and interacting with large datasets, such as the use of network topologies and attribute-structure synchronization. Additionally, there is a growing emphasis on developing tools and frameworks that can facilitate dataset discovery, such as AI-assisted query reformulations and semantic search. These advancements have the potential to revolutionize the way data is analyzed and interpreted, enabling researchers to gain deeper insights and make more informed decisions. Noteworthy papers in this area include: A Unified Framework for Interactive Visual Graph Matching via Attribute-Structure Synchronization, which proposes a novel framework for interactive visual graph matching. DataScout, which introduces a proactive approach to dataset discovery via AI-assisted query reformulations and semantic search. Mosaic Selections, which describes a model for representing, managing, and optimizing user selections in scalable data visualization systems.