SuperMat: Construction of a linked annotated dataset from superconductors-related publications

TL;DR

SuperMat is a high-quality, linked annotated dataset from superconductors research publications, enabling advanced text and data mining for materials science.

Contribution

It introduces a novel, expert-validated annotated corpus linking superconductors data from scientific articles, facilitating data-driven materials discovery.

Findings

142 articles included in the dataset

16052 entities annotated with high inter-annotator agreement

1398 links between entities established

Abstract

A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-driven materials design. Herein, we present SuperMat (Superconductor Materials), an annotated corpus of linked data derived from scientific publications on superconductors, which comprises 142 articles, 16052 entities, and 1398 links that are characterised into six categories: the names, classes, and properties of materials; links to their respective superconducting critical temperature (Tc); and parametric conditions such as applied pressure or measurement methods. The construction of SuperMat resulted from a fruitful collaboration between computer scientists and material scientists, and its high quality is ensured through validation by domain experts. The quality of the annotation guidelines was ensured by satisfactory Inter Annotator Agreement (IAA) between the annotators and the domain experts. SuperMat includes the dataset, annotation guidelines, and annotation support tools that use automatic suggestions to help minimise human errors.