MultiphaseHUB for Evidence-Grounded Metadata Curation and Task-Aware Compression of Multiphase Data

Abstract

Multiphase transport experiments and simulations generate high-dimensional data whose scientific value depends on the physical conditions, sensing configuration, calibration, synchronization, uncertainty, and provenance accompanying the raw signals. These data are difficult to reuse when experimental context is scattered across publications and repositories, while large measurement volumes increase the cost of transfer and analysis. In this work, we introduce MultiphaseHUB as a community infrastructure and an integrated framework for organizing, discovering, and reusing multiphase transport data. The infrastructure connects externally hosted datasets with modality-specific metadata, publications, analysis tools, derived features, source repository links, and boundaries for reuse. Within this framework, we develop two complementary components: literature analysis and data curation, comprising Large Language Model (LLM)-assisted metadata extraction and an agentic literature-analyzer for quantitative values from figures; and modality-specific scientific data compression guided by the quantities of interest needed for downstream analysis. These components address the effort required to interpret heterogeneous sources and the cost of accessing useful information from high-dimensional measurements. Public multiphase datasets provide seed resources for illustrating the framework and its workflows. Building on these components, we outline a research roadmap encompassing cross-facility harmonization, multimodal alignment, task-aware retrieval and dataset valuation, and conditional reconstruction of missing modalities. MultiphaseHUB provides a foundation for connecting distributed data resources to physically meaningful comparison and cross-study scientific discovery.