  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.
