Demonstrating Linked Battery Data To Accelerate Knowledge Flow in Battery Science

TL;DR

This paper introduces a linked data framework using an ontology to standardize and connect battery research data, significantly improving data accessibility, analysis, and community collaboration in battery science.

Contribution

It presents a novel approach combining structured, semantic, and linked data with an ontology to enhance data management and accelerate battery research.

Findings

Developed BattINFO ontology for battery data standardization

Enabled automated data extraction and analysis

Provided open-source tools and structured data for community use

Abstract

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.