  Deep learning models are increasingly data-hungry, requiring significant
resources to collect and compile the datasets needed to train them, with Earth
Observation (EO) models being no exception. However, the landscape of datasets
in EO is relatively atomised, with interoperability made difficult by diverse
formats and data structures. If ever larger datasets are to be built, and
duplication of effort minimised, then a shared framework that allows users to
combine and access multiple datasets is needed. Here, Major TOM (Terrestrial
Observation Metaset) is proposed as this extensible framework. Primarily, it
consists of a geographical indexing system based on a set of grid points and a
metadata structure that allows multiple datasets with different sources to be
merged. Besides the specification of Major TOM as a framework, this work also
presents a large, open-access dataset, MajorTOM-Core, which covers the vast
majority of the Earth's land surface. This dataset provides the community with
both an immediately useful resource, as well as acting as a template for future
additions to the Major TOM ecosystem. Access: https://huggingface.co/Major-TOM
