Modern geosciences increasingly depend on open algorithms and accessible data to advance reproducibility and cross-disciplinary collaboration. Despite growing adoption of FAIR principles and open-source tools (ObsPy, SPECFEM, WRF, eWaterCycle, NEMO, MOM6), challenges persist in developing sustainable, interoperable software across disciplines.

This session invites contributions on open algorithm development and computational methods across Solid Earth, atmospheric, hydrological, and ocean sciences. We aim to connect researchers analysing observational data, resolving subsurface and fluid-dynamic structures, and tracking processes across Earth system components - whether using physics-based models, interpretable data-driven methods, or hybrid approaches. Data-driven methods with post-hoc explainability are also welcome when backed by thorough verification.

We welcome studies on cross-disciplinary tool adoption, benchmark datasets, uncertainty quantification, and lessons from open science initiatives.

Topics include but are not limited to:
- Forward and inverse modelling
- Uncertainty quantification
- Interpretable and physics-informed machine learning
- Data processing and visualization
- Large-scale HPC implementations
- Cloud-based computational platforms