From manual entry to machine precision: challenges and evolution of metadata schema development in collaborative research centers

Objective Metadata standardization often struggles to balance broad interoperability with the specific needs of diverse research domains. This study demonstrates a "parent template" approach to schema development within large-scale German Collaborative Research Centers. We describe the iterative adaptation of a baseline schema developed for a nephrology-focused consortium (NephGen) to meet the distinct data management requirements of subsequent initiatives in tumor immunology (OncoEscape) and perinatal immune development (Pilot). Results The derivation process produced three structurally compatible schemas with varying vocabularies. The Pilot schema required the highest granularity (342 levels), followed by NephGen and OncoEscape (283). Despite the shared template, quantitative overlap was limited. Only 47% of OncoEscape levels and less than 34% of the Pilot vocabulary were retained from the NephGen baseline. Consequently, the Pilot schema had to introduce unique items like "Timeline" to capture immune development, while OncoEscape focused on "Oncogenes". While this strategy balanced standardization with specificity, limiting schema complexity just to suit manual entry is becoming obsolete. Since Large Language Models (LLMs) don't suffer from human fatigue, we argue that future schemas should stop being static forms. Instead, they need to become flexible, high-dimensional "instruction sets" tailored for AI extraction.