Ontologies have long been employed in the life sciences to formally represent and reason
over domain knowledge and they are employed in almost every major biological database …

It is challenging to determine whether datasets are findable, accessible, interoperable, and
reusable (FAIR) because the FAIR Guiding Principles refer to highly idiosyncratic criteria …

Explainable artificial intelligence typically focuses on data-based explanations, lacking the
semantic context needed to produce human-centric explanations. This is especially relevant …

While the biomedical community has published several “open data” sources in the last
decade, most researchers still endure severe logistical and technical challenges to discover …

Abstract Knowledge graphs have been shown to significantly improve search results.
Usually populated by subject matter experts, relations therein need to keep up to date with …

Metadata play a crucial role in ensuring the findability, accessibility, interoperability, and
reusability of datasets. This paper investigates the potential of large language models …

Open Science aims at sharing results and data widely between different research domains.
Interoperability is one of the keys to enable the exchange and crossing of data between …

With the global increase in experimental data artifacts, harnessing them in a unified fashion
leads to a major stumbling block-bad metadata. To bridge this gap, this work presents a …

High-quality metadata annotations for data hosted in large public repositories are essential
for research reproducibility and for conducting fast, powerful and scalable meta-analyses …

Data labels are an integral input to develop machine learning (ML) models. In complex
domains, labels represent the externalized product of complex knowledge. While prior …