LLM-Based Metadata and Context Extraction for Agricultural Long-Term Experiments

Long-term agricultural field experiments (LTEs) provide essential data for crop modeling and understanding agricultural processes under global change. Their reuse is limited by poor FAIR data practices and limited focus on specific experimental factors. Initiatives like the LTE-Overview Map have made a leap in metadata collection. However, manual processes remain inefficient.Our approach uses Large Language Models (LLMs) to automatically extract standardized, interoperable metadata from scientific literature, enhancing data discovery and reusability.