Hi HN, I’ve always been frustrated by this: we live in an era of exponential scientific progress, but almost no one can clearly trace a cutting-edge discovery back to the first principles it’s built on. We learn science in silos, not as a connected, logical tree. So I built The Map of Science — an interactive dependency graph for the entirety of human scientific knowledge. It’s simple: every scientific theory is a node, every derivation/dependency is an edge. You can trace a path from basic math axioms to quantum field theory, from early computing to modern LLMs, and see exactly how each breakthrough builds on what came before. The stack: React + Cytoscape for the frontend, Node.js + Neo4j for the backend (a relational DB couldn’t handle the deep recursive queries for multi-layer derivation chains). It’s fully open source under MIT, and we’ve already seeded the core dataset for math, physics, and computer science. This is a project that can never be finished by one person. I need your help: If you’re a domain expert, help us fix incorrect links or expand into biology, chemistry, social sciences, and more. If you’re a developer, help us optimize the graph rendering for even larger datasets. If you just love science, tell us what works, what doesn’t, and what you want to see next. I’d specifically love your thoughts on: We’re using a directed acyclic graph (DAG) tree structure to show derivation dependencies. Does this make cross-field interdisciplinary links clear enough? Is there a better way to visualize these relationships? What’s the cleanest way to represent superseded or disputed theories (like Newtonian vs. relativistic mechanics, or phlogiston theory vs. oxidation)? We want to honor their historical importance without cluttering the core, up-to-date knowledge chain. Repo: https://github.com/slepybear/The-Map-of-Science Live Demo: https://build-five-dun.vercel.app/map (best viewed on desktop for full graph interactivity) Thanks for looking!