When 5G began its commercialisation journey around 2020, the discussion on the vision of
6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability …

This paper provides an extensive overview of the use of knowledge graphs in the context of
Explainable Machine Learning. As of late, explainable AI has become a very active field of …

Data sharing efforts increasingly contribute to the acceleration of scientific discovery.
Neuroimaging data is accumulating in distributed domain-specific databases and there is …

Background Microbial inhabitants of soils are important to ecosystem and planetary
functions, yet there are large gaps in our knowledge of their diversity and ecology. The …

Scientific workflows are a popular mechanism for specifying and automating data-driven in
silico experiments. A significant aspect of their value lies in their potential to be reused. Once …

Autonomous agents must often detect affordances: the set of behaviors enabled by a
situation. Affordance detection is particularly helpful in domains with large action spaces …

Reductionist science produces causal models of small fragments of complicated systems.
Causal models of entire systems can be hard to construct because what is known of them is …

Figure 1| From multi-modal connectivity data to integrated connectomes. Image panels
illustrating recent progress, selected and modified from contributions to the Research Topic …

OBJECTIVES: This paper presents a methodology for recovering and decomposing
Swanson's Raynaud Syndrome–Fish Oil hypothesis semi-automatically. The methodology …

Abstract Editor's Summary In order to draw meaning from the exponentially increasing
quantity of healthcare data, it must be dealt with from a big data perspective, using …