LLMCrater: Lifecycle-Aware FAIR Metadata Generation using Large Language Models

2026-08-24

AI Summary

为解决科研生命周期中FAIR元数据创建和维护的手动问题，提出LLMCrater框架，利用大型语言模型自动生成并丰富各阶段的元数据。

Abstract

FAIR (Findable, Accessible, Interoperable, and Reusable) metadata is essential for the discovery, interoperability, and reuse of scientific research assets. However, creating and maintaining FAIR metadata remains largely manual, making the process time-consuming for heterogeneous research artifacts generated throughout the research lifecycle. Existing approaches primarily generate metadata at publication time, missing opportunities to capture contextual information as it becomes available. To address this limitation, we present \emph{LLMCrater}, a lifecycle-aware metadata generation framework that combines Large Language Models (LLMs) with stage-specific RO-Crate metadata profiles. The framework progressively enriches metadata across four research lifecycle stages (Design, Development, Deployment, and Execution \& Provenance) while remaining compatible with RO-Crate~1.1 and EOSC metadata recommendations. It automatically extracts metadata from heterogeneous artifacts, generates and validates machine-actionable RO-Crates, and supports publication to FAIR repositories and PID services (e.g., Zenodo). We demonstrate the approach using two representative use cases: a 5G experimentation environment within SLICES-RI and an experiment on GreenDIGIT's EcoJupyter platform. Results show that LLMCrater progressively enriches metadata throughout the research lifecycle and generates valid RO-Crates conforming to the RO-Crate~1.1 specification.

Problem

• FAIR metadata
• research lifecycle
• heterogeneous research artifacts
• metadata generation
• contextual information

Innovation

• Lifecycle-Aware Metadata
• Large Language Models
• RO-Crate
• FAIR Principles
• Automated Metadata Extraction