Demonstrating Data-to-Knowledge Pipelines for Connecting Production Sites in the World Wide Lab

The digital transformation of production requires methods for integrating, storing, and operationalizing data across organizational boundaries, yet most existing approaches remain siloed and unidirectional, lacking a systematic loop from raw data to actionable knowledge and back. We introduce Data-to-Knowledge (D2K) and Knowledge-to-Data (K2D) pipelines as a universal production concept built on networks of Digital Shadows. The Data-to-Knowledge (D2K) pipeline is realized as a cross-organizational proof of concept that captures and semantically annotates robotic trajectory data from three independent research institutes and uses those data to train an inverse-dynamics foundation model for robot control. Centralized aggregation via an existing FAIR-compliant research data repository was chosen deliberately over federated alternatives to maximize semantic interoperability and reuse of shared infrastructure; federated and privacy-preserving extensions are identified as a promising future direction. Fine-tuning the cross-organizationally trained foundation model reduces training time by approximately 85% relative to end-to-end training from scratch, while achieving comparable accuracy on a standardized inverse-dynamics benchmark. These gains are attributable to the combination of cross-site data aggregation and transfer learning; isolating the contribution of semantic annotation alone remains a topic for future ablation work. The implementation demonstrates that semantically enriched, cross-organizational D2K pipelines can accelerate model development and reduce redundant data collection within a constrained but practically relevant class of robotics tasks. We further discuss limitations, governance challenges, and how these pipelines can contribute to a broader World Wide Lab for collaborative production research.