# From Sky to Earth: Data Science Methodology Transfer

## TL;DR

This paper explores how data science methodologies from astronomy can be transferred to earth sciences, emphasizing the role of metadata, ontologies, and infrastructure like EarthCube's Virtual Observatory to facilitate cross-disciplinary tool adaptation.

## Contribution

It highlights the parallels between astronomy and earth science datasets and discusses a framework for transferring data analysis methods using infrastructure like EarthCube.

## Key findings

- Meta-data and ontologies are crucial for methodology transfer.
- EarthCube's Virtual Observatory can facilitate cross-disciplinary tool sharing.
- Bidirectional learning between astroinformatics and geoinformatics is possible.

## Abstract

We describe here the parallels in astronomy and earth science datasets, their analyses, and the opportunities for methodology transfer from astroinformatics to geoinformatics. Using example of hydrology, we emphasize how meta-data and ontologies are crucial in such an undertaking. Using the infrastructure being designed for EarthCube - the Virtual Observatory for the earth sciences - we discuss essential steps for better transfer of tools and techniques in the future e.g. domain adaptation. Finally we point out that it is never a one-way process and there is enough for astroinformatics to learn from geoinformatics as well.