Background The automation of data analysis in the form of scientific workflows has become
a widely adopted practice in many fields of research. Computationally driven data-intensive …

The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility,
interoperability and reusability of digital research objects for both humans and machines …

Abstract The field of MultiSector Dynamics (MSD) explores the dynamics and co‐
evolutionary pathways of human and Earth systems with a focus on critical goods, services …

Exascale computing has been a dream for ages and is close to becoming a reality that will
impact how molecular simulations are being performed, as well as the quantity and quality of …

The effectiveness of the machine learning methods for real-world tasks depends on the
proper structure of the modeling pipeline. The proposed approach is aimed to automate the …

Computational workflows describe the complex multi-step methods that are used for data
collection, data preparation, analytics, predictive modelling, and simulation that lead to new …

Research data is accumulating rapidly and with it the challenge of fully reproducible
science. As a consequence, implementation of high-quality management of scientific data …

Workflows are among the most commonly used tools in a variety of execution environments.
Many of them target a specific environment; few of them make it possible to execute an …

As the scale of biological data generation has increased, the bottleneck of research has
shifted from data generation to analysis. Researchers commonly need to build …

Abstract Essential Biodiversity Variables (EBV) are fundamental variables that can be used
for assessing biodiversity change over time, for determining adherence to biodiversity policy …