M@TE — Model Atlas of the Earth

M@TE

Model Atlas of the Earth

M@TE is a purpose-built digital platform for geoscience that provides access to computational models of Earth's processes, from the Geosphere to the Hydrosphere, like Zenodo for geoscience but bigger and better.


Funded by:
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What is M@TE?

M@TE (Model Atlas of the Earth) is designed to meet geoscientists where they are: from educators and students using visualizations in the classroom to researchers developing and benchmarking new numerical models. It lowers the learning curve of numerical models and bridges the gap between traditional geology and numerical geosciences.

M@TE enables you to:

  • Explore and visualize models through an intuitive web interface: play animations, use them in your teaching or to better understand Earth system concepts.
  • Download and build on models by accessing input files and configuration scripts to reproduce or expand upon simulations on your own.
  • Analyze and benchmark model outputs by comparing results across models and datasets without having to recreate simulations from scratch.
  • Upload your own models using streamlined tools for packaging code, metadata and outputs — stored, preserved and assigned a DOI for citation.

Why M@TE?

The field of numerical modeling of Earth’s systems is rapidly expanding, enabling new insights into the coevolution of the solid Earth and surface processes. These models often require specialized high-performance computing resources, can take days to weeks to run, and generate large volumes of heterogeneous output data. Without proper curation and community standards, it is difficult to access, interpret, and build upon published work.

M@TE addresses these needs by providing:

  • Integrated management of the full model life cycle: code, documentation, output data, publications, figures, animations and metadata
  • Purpose-built infrastructure for storing, preserving, and sharing computational models
  • A central hub connecting educators, model developers, and geoscientists
  • Support for reproducibility and reuse
  • Built-in DOI creation for persistent, citable results
  • Scalable and sustainable research infrastructure
  • Flexibility for users with different levels of expertise
  • Capabilities beyond general-purpose repositories (e.g., Zenodo, Figshare)
  • ✅ Open source and free
  • 🔎 FAIR data principles
  • ♻️ Reproducible models
  • 🏗️ Built on sustainable infrastructure
  • 📦 RO-Crate packaging
  • 🆔 DOI-assigned datasets