We’re building TheorIA— an open, high quality dataset of theoretical physics results: equations, derivations, definitions, and explanations — all in structured, machine- and human-readable JSON.
Why?
Physics is rich with beautiful, formal results — but most of them are trapped in PDFs, LaTeX, or lecture notes. That makes it hard to:
- train symbolic/physics-aware ML models,
- build derivation-checking tools,
- or even just teach physics interactively.
THEORIA fills that gap. Each entry includes:
A result name (e.g., Lorentz transformations)
Clean equations (AsciiMath)
Straightforward step-by-step derivation with reasoning
Symbol definitions & assumptions
Programmatic validation using sympy
References, arXiv-style domain tags, and contributor metadata
Everything is in open, self-contained JSON files. No scraping, no PDFs, just clear structured data for physics learners, teachers, and ML devs.
Contributors Wanted:
We’re tiny right now and trying to grow. If you’re into physics or symbolic ML:
Add an entry (any result you love)
Review others' derivations
Build tools on top of the dataset
GitHub
https://github.com/theoria-dataset/theoria-dataset/
Licensed under CC-BY 4.0, and we welcome educators, students, ML people, or just anyone who thinks physics deserves better data.
Not sure if it fits but I still have ~20k currated step by step solution for mathematics (pedagogical math) "lying" around from my previous startup. They are all hand currated. And could even be used for fine tuning or so.
Here are some details: The dataset has 20.600 Abstract Exercises which turn into 1.193.958 Concrete Exercises.
An Abstract Exercise looks like this: a + b = c A Concrete Exercise looks like this: 2 + 3 = 5 Tital compiled file size (JSONL): 11.6GB
And here is an explorer to see some of the data https://curriculum.amy.app/ToM