SciPy 2026

FAIR (Findable, Accessible, Interoperable, Reusable) data is widely agreed on as the benchmark for data management and stewardship. Unfortunately, findability is often an afterthought - and datasets that can't be found are difficult to access!
In the earth sciences, we commonly assume that if a dataset can be found by someone who already knows what they are looking for or where to look for it, then it is findable. For small and tight knit research communities, or cloud distributed datasets, this might be the case.
However, within the earth sciences, many datasets remained siloed on HPC systems, where it is often assumed that tribal knowledge that can be obtained from a supervisor, colleague, or collaborator is sufficient to guide new users through these systems.
Worse yet, users are often expected to obtain login access to an HPC simply to discover which datasets are available, before they can even determine whether the data are relevant to their needs.
In practice, this assumption means that datasets are only available to an in group of users who are already familiar with them.

Why is this so often the case? The defacto tool for data analysis in the earth sciences is Python, but the best way to advertise and distribute datasets is through the web. If we want to distribute the data ourselves, without getting experienced web developers involved, this leaves us with a few options: static site generation through tools like readthedocs, writing a python web server, or going all in and learning enough JavaScript to create an interactive data exploration tool.

The key issue? The better the interface, the more time and effort you'd need to sink into learning a new tech stack, toolchain, and way of thinking. The result of this - lots of clunky interfaces to explore and obtain data.

Whilst this is still true, with AI agents now widely available, the cost of learning a new tech stack or creating new data delivery tools has plummeted. For an experienced developer with a hoard of well structured data, 'vibe-coding' a wrapper to advertise and distribute that data is now a serious option.

In this talk, I'll walk through how we created a tool for advertising Australia's trove of earth science data, making it easy to find and discover for anyone with a browser and an internet connection - not just those who already had the right HPC login. Expect to learn:

• Why well structured data, metadata, and documentation are more important than ever - not less - in this new data landscape.
• How we used intake, duckdb-wasm, polars and Vue to create a tool that blends cloud and HPC data delivery.
• Why the proliferation of social media and gamification of content has made data advertising more important than ever.
• How keeping wrappers thin and focusing on the interactive experience lets the data do the talking.
• How we went about testing, gathering feedback, and iterating on an interactive tool in an area where users expect to be provided with static content or a Python API.

Intended Audience: Earth Scientists, people interested in data sharing, people looking to use emerging tools to make their work more impactful