  Extraction and synthesis of structured knowledge from extensive scientific
literature are crucial for advancing and disseminating scientific progress.
Although many existing systems facilitate literature review and digest, they
struggle to process multimodal, varied, and inconsistent information within and
across the literature into structured data. We introduce SciDaSynth, a novel
interactive system powered by large language models (LLMs) that enables
researchers to efficiently build structured knowledge bases from scientific
literature at scale. The system automatically creates data tables to organize
and summarize users' interested knowledge in literature via question-answering.
Furthermore, it provides multi-level and multi-faceted exploration of the
generated data tables, facilitating iterative validation, correction, and
refinement. Our within-subjects study with researchers demonstrates the
effectiveness and efficiency of SciDaSynth in constructing quality scientific
knowledge bases. We further discuss the design implications for human-AI
interaction tools for data extraction and structuring.
