The field of data analysis and scholarly document processing is rapidly evolving, with a focus on developing innovative methods and tools to improve the accessibility, interpretability, and reproducibility of research. Recent developments have centered around the use of large language models (LLMs) and agent-based techniques to enhance data understanding, natural language interfaces, and semantic analysis. The integration of LLMs with data visualization tools has also democratized data analysis, making it more intuitive and accessible to non-technical users. Furthermore, the development of modular, component-based architectures for AI agents has enabled the creation of transparent, evaluable, and accessible data agents that can bridge the gap between natural language interfaces and complex enterprise data warehouses. Noteworthy papers in this area include: A Large-Scale Dataset and Citation Intent Classification in Turkish with LLMs, which introduces a systematic methodology and a foundational dataset for citation intent classification in Turkish. VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture, which presents an AI-assisted graph generation system that empowers users to create meaningful visualizations using natural language. Experiversum: an Ecosystem for Curating and Enhancing Data-Driven Experimental Science, which introduces a lakehouse-based ecosystem that supports the curation, documentation, and reproducibility of exploratory experiments.