Trends in null hypothesis significance testing: Still going strong.

Null hypothesis significance testing (NHST) is among the most prominent and widely used methods for analyzing data. At the same time, NHST has been criticized since many years because of misuses and misconceptions that can be found extensively in the scholarly literature. Furthermore, in recent years, NHST has been identified as one reason for the replication crisis because many studies place too much emphasis on statistical significance for drawing conclusions.

Complexity data science: A spin-off from digital twins.

Digital twins offer a new and exciting framework that has recently attracted significant interest in fields such as oncology, immunology, and cardiology. The basic idea of a digital twin is to combine simulation and learning to create a virtual model of a physical object. In this paper, we explore how the concept of digital twins can be generalized into a broader, overarching field.

Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation.

The COVID-19 pandemic presented an unparalleled challenge to global healthcare systems. A central issue revolves around the urgent need to swiftly amass critical biological and medical knowledge concerning the disease, its treatment, and containment. Remarkably, text data remains an underutilized resource in this context.