  Recent advancements in Earth system science have been marked by the
exponential increase in the availability of diverse, multivariate datasets
characterised by moderate to high spatio-temporal resolutions. Earth System
Data Cubes (ESDCs) have emerged as one suitable solution for transforming this
flood of data into a simple yet robust data structure. ESDCs achieve this by
organising data into an analysis-ready format aligned with a spatio-temporal
grid, facilitating user-friendly analysis and diminishing the need for
extensive technical data processing knowledge. Despite these significant
benefits, the completion of the entire ESDC life cycle remains a challenging
task. Obstacles are not only of a technical nature but also relate to
domain-specific problems in Earth system research. There exist barriers to
realising the full potential of data collections in light of novel cloud-based
technologies, particularly in curating data tailored for specific application
domains. These include transforming data to conform to a spatio-temporal grid
with minimum distortions and managing complexities such as spatio-temporal
autocorrelation issues. Addressing these challenges is pivotal for the
effective application of Artificial Intelligence (AI) approaches. Furthermore,
adhering to open science principles for data dissemination, reproducibility,
visualisation, and reuse is crucial for fostering sustainable research.
Overcoming these challenges offers a substantial opportunity to advance
data-driven Earth system research, unlocking the full potential of an
integrated, multidimensional view of Earth system processes. This is
particularly true when such research is coupled with innovative research
paradigms and technological progress.
