About
Teaching through data. Learning through wonder.
Found in Space exists because data is a wonderful way to learn about the universe, and the universe is a wonderful way to learn about data. The two things feed each other, and this project is an attempt to let that loop run in the open.
What this project is
At its heart, Found in Space is an educational project. It takes real astronomical data — starting with ESA's extraordinary Gaia mission, but reaching into many other catalogues too — and tries to make that data come alive through interactive visualisations, guided explorations, and clear explanations.
But it doesn't stop at showing you a pretty picture. The whole pipeline is on display: how to find and download stellar catalogues, how to clean and process them, how to analyse and index them for the browser, and how to turn them into something you can actually explore. Every step is shared, every line of code is open on GitHub.
The site is part exhibit, part classroom, part lab. It might show you a constellation and then pull the stars apart to reveal the depths behind the flat pattern. It might ask you a question instead of giving you an answer, because the best way to understand something is often to work it out for yourself.
Why Gaia?
The Gaia spacecraft has measured the positions, distances, motions, and colours of nearly two billion stars with astonishing precision. It is one of the richest scientific datasets ever assembled, and almost all of it is publicly available. That combination — depth, precision, and openness — makes it an ideal foundation for teaching and exploration.
Gaia is the starting point, but not the boundary. Found in Space pulls in data from other missions and surveys wherever it helps tell a richer story, from photometric catalogues to exoplanet databases.
Questions over answers
A lot of educational material tells you facts and asks you to remember them. Found in Space tries something different: it sets up situations where curiosity can do the work. Why do these stars look different colours? What happens to a constellation when you leave Earth behind? How far away is "nearby" in astronomical terms?
The hope is that by exploring data directly — rotating a star field, comparing measurements, following a guided journey — ideas click in a way that reading alone can't quite achieve.
Open everything
All the code behind Found in Space is published at github.com/Found-in-Space: the data pipelines, the spatial indexing, the streaming formats, the interactive viewer, and the website itself. If you want to see how a visualisation works, you can read the source. If you want to build on it, you can fork it.
This isn't openness for its own sake. It's because the process of turning raw measurements into something people can explore is itself a lesson worth sharing. Understanding how data becomes knowledge is just as valuable as the knowledge itself.
Who's behind this
Found in Space is built by Kaj Siebert — an astrophysicist by training who went on to spend two decades in data science and technology, mostly working on projects that try to make complex systems more legible and more humane.
But astronomy never really went away. The PhD might be a long time ago, but the fascination with what's out there, and the belief that data is a beautiful way to get closer to it, stuck around. Found in Space is what happens when that thread finally gets pulled properly: a return to the stars, armed with everything learned along the way about data, code, visualisation, and — maybe most importantly — how people actually learn.
Kaj has spent the last decade running science outreach with CERN through The Big Bang Collective, and has always believed that the best learning happens through play. Found in Space carries that same spirit: serious data, but never a dry lecture. The aim is to build something people can wander through, get curious about, and genuinely enjoy.