A machine-learning-driven data labeling pipeline for scientific analysis in <i>MLExchange</i>

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where <i>Data Clinic</i> and <i>MLCoach</i> enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while <i>Label Maker</i> is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through <i>Tiled</i> for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.