构建电池数据互联体系，加速科研知识流动。
Demonstrating Linked Battery Data To Accelerate Knowledge Flow in Battery Science
- 用标准化术语和链接数据构建电池知识网络
- 整合全文搜索与机器可读数据，提升检索效率
- 开源工具支持社区协作，助力电池研究数字化
电池是实现低碳未来的关键，2023年仅在Scopus数据库中就有14,388篇论文提及“锂离子电池”，信息过载使研究人员难以跟进。本文提出基于结构化、语义化和链接数据的策略来应对这一挑战。结构化数据采用机器可读格式；语义数据包含上下文元数据；链接数据通过引用其他语义数据形成信息网络。我们使用电池领域本体BattINFO统一术语，支持自动化数据提取与分析。方法融合全文搜索与机器可读数据，提升数据检索与电池测试效率。目标是整合商业电芯信息，开发无需依赖厂商的循环测试描述工具及大语言模型外部记忆。虽为初步尝试，但显著加速电池研究进程并推动测试数字化，鼓励社区持续改进。相关结构化数据与工具已开源。
原文摘要 · Abstract (English)
Batteries are pivotal for transitioning to a climate-friendly future, leading to a surge in battery research. Scopus (Elsevier) lists 14,388 papers that mention "lithium-ion battery" in 2023 alone, making it infeasible for individuals to keep up. This paper discusses strategies based on structured, semantic, and linked data to manage this information overload. Structured data follows a predefined, machine-readable format; semantic data includes metadata for context; linked data references other semantic data, forming a web of interconnected information. We use a battery-related ontology, BattINFO to standardise terms and enable automated data extraction and analysis. Our methodology integrates full-text search and machine-readable data, enhancing data retrieval and battery testing. We aim to unify commercial cell information and develop tools for the battery community such as manufacturer-independent cycling procedure descriptions and external memory for Large Language Models. Although only a first step, this approach significantly accelerates battery research and digitalizes battery testing, inviting community participation for continuous improvement. We provide the structured data and the tools to access them as open source.
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