| Abstract Scope |
The growing demand for high-throughput, reproducible, and scalable analyses is driving a paradigm shift toward digitally enabled, autonomous laboratories. Traditional workflows are often constrained by manual interventions, rigid experimental pipelines, and disjointed data systems that hinder real-time responsiveness. As advanced characterization tools evolve in their ability to generate rich, high-dimensional datasets, they expose a new bottleneck: the lack of integrated data flow architectures capable of dynamically informing experimental decisions. The Lab-in-the-Loop concept is built on a linking instrument control, sample tracking, data processing, and machine learning-driven decision logic into a seamless operational loop. I discuss the required architecture consisting of (1) Data Acquisition, interfacing with scientific instrumentation for high-resolution readouts; (2) Data Integration and Orchestration that harmonizes diverse data modalities and metadata streams using structured ontologies and APIs; (3) Analytics and Inference and (4) Execution Layer, where robotic or scripted agents adjust protocols in real time based on feedback. |