The Open Taxonomy of Global Higher Education
An open dataset and knowledge graph for university courses, prerequisites, degree pathways, skills, careers, and global credit frameworks.
"A polymath is someone whose knowledge spans a significant number of subjects, known to draw on complex bodies of knowledge to solve specific problems."
Polymath maps the architecture of higher education — across disciplines, degree levels, credit systems, and career pathways — into a single, open, machine-readable knowledge graph.
SEO keywords: higher education taxonomy, university course dataset, curriculum knowledge graph, prerequisite graph, academic disciplines, degree pathways, credit transfer systems, ECTS, US credits, ISCED-F, CIP codes, skills taxonomy, career pathways, open education data.
Current release: 94 university courses, 90 prerequisite relationships, 15 academic disciplines, 59 skills, 28 career pathways, 5 degree pathways, and 4 global credit / qualification frameworks.
Every node is a university course, colored by discipline cluster. Every connection is a prerequisite, a credit pathway, or a career link. Explore it interactively →
| Existing data | Polymath |
|---|---|
| Flat course catalogs per institution | Cross-institutional, cross-border knowledge graph |
| Siloed accreditation frameworks | Unified mapping between US Credits, ECTS, UK CATS, and more |
| Degree requirements buried in PDFs | Machine-readable degree pathways with prerequisites |
| Career skills detached from curriculum | Direct links from courses → skills → careers |
| K-12 taxonomies (Marble, etc.) | Built for higher education from the ground up |
┌──────────────┐
│ FRAMEWORKS │ ← ECTS, US Credits, AQF, NQF...
└──────┬───────┘
│ governs
┌───────────────┼───────────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ DEGREES │───▶│ COURSES │───▶│ SKILLS │
└──────────┘ └────┬─────┘ └────┬─────┘
▲ │ │
│ prerequisites leads to
│ │ │
│ ▼ ▼
│ ┌──────────┐ ┌──────────┐
└──────│DISCIPLINES│ │ CAREERS │
└──────────┘ └──────────┘
| Entity | Description | Example |
|---|---|---|
| Disciplines | Fields of study aligned to ISCED-F 2013 + CIP 2020 | Computer Science, Finance, Cognitive Psychology |
| Courses | Standardized university-level courses with credits | CS 201: Data Structures & Algorithms (3 US credits / 6 ECTS) |
| Prerequisites | Directed dependency graph between courses | Machine Learning → requires → Linear Algebra |
| Degrees | Degree programs with course requirements | B.Sc. Computer Science (120 US credits) |
| Skills | Competencies acquired from courses | Python programming, Financial modeling, Critical thinking |
| Careers | Professional roles linked to skills and courses | Data Scientist, Financial Analyst, UX Researcher |
| Frameworks | Regional qualification and credit systems | Bologna Process, AQF Level 7, US Semester Credit Hours |
| Discipline | Courses | Degrees | Skills |
|---|---|---|---|
| Computer Science | 22 | 2 | Shared skills graph |
| Management | 13 | 1 | Shared skills graph |
| Business Administration | 10 | 1 | Shared skills graph |
| Artificial Intelligence | 9 | 1 | Shared skills graph |
| Data Science | 7 | - | Shared skills graph |
| Economics | 7 | - | Shared skills graph |
| Finance | 6 | - | Shared skills graph |
| Software Engineering | 5 | - | Shared skills graph |
| Mathematics | 5 | - | Shared skills graph |
| Marketing | 5 | - | Shared skills graph |
| Psychology | 5 | - | Shared skills graph |
| Total | 94 | 5 | 59 skills |
Suggested GitHub topics for discovery:
higher-education taxonomy knowledge-graph curriculum open-data education-data university-courses prerequisites degree-pathways skills-taxonomy career-pathways ects isced-f cip-codes credit-transfer
All data lives in data/ as UTF-8 JSON. See schema/ for JSON Schemas.
| File | Contents |
|---|---|
data/disciplines.json |
Discipline taxonomy with ISCED-F and CIP codes |
data/courses.json |
University courses (graph nodes) |
data/prerequisites.json |
Course prerequisite edges |
data/degrees.json |
Degree programs and their course requirements |
data/skills.json |
Skills taxonomy with course and career links |
data/careers.json |
Career profiles with skill requirements |
data/frameworks.json |
Regional qualification frameworks |
data/manifest.json |
Counts, checksums, version info |
{
"id": "course_cs_data_structures",
"name": "Data Structures & Algorithms",
"disciplineId": "disc_computer_science",
"level": "bachelor",
"credits": {
"us": 3,
"ects": 6,
"uk": 15
},
"description": "Abstract data types, algorithm analysis, and fundamental data structures including arrays, linked lists, trees, graphs, hash tables, and heaps. Emphasis on both theoretical foundations and practical implementations.",
"learningOutcomes": [
"Analyze time and space complexity using Big-O notation",
"Implement and compare fundamental data structures",
"Select appropriate data structures for given problem constraints",
"Design and implement graph traversal algorithms"
],
"skillIds": ["skill_python", "skill_algorithm_design", "skill_complexity_analysis"],
"prerequisiteIds": ["course_cs_programming_1", "course_math_discrete"]
}{
"courseId": "course_cs_machine_learning",
"prerequisiteId": "course_math_linear_algebra",
"strength": "hard",
"rationale": "ML models require understanding of vector spaces, matrix operations, and eigendecomposition"
}{
"id": "career_data_scientist",
"title": "Data Scientist",
"category": "Technology & Analytics",
"medianSalaryUsd": 108000,
"growthOutlook": "strong",
"requiredSkillIds": ["skill_python", "skill_machine_learning", "skill_statistics", "skill_data_visualization"],
"recommendedDegreeIds": ["degree_ms_data_science", "degree_ms_computer_science"]
}Pure data — no runtime, no dependencies. Load the JSON and go.
import courses from './data/courses.json' with { type: 'json' };
import prereqs from './data/prerequisites.json' with { type: 'json' };
const byId = new Map(courses.courses.map(c => [c.id, c]));
// Find all prerequisites for Machine Learning
const mlPrereqs = prereqs.prerequisites
.filter(p => p.courseId === 'course_cs_machine_learning')
.map(p => ({
course: byId.get(p.prerequisiteId).name,
strength: p.strength,
rationale: p.rationale
}));Open explorer/index.html in any browser, or visit aclascollege.github.io/polymath/explorer. Features:
- Liberty of Knowledge view — Statue of Liberty-inspired graph of all courses, colored by discipline
- Pathway trace — click any course to highlight prerequisites and unlocks
- Credit calculator — convert between US Credits, ECTS, and UK CATS
- Career mapper — select a career to see recommended courses and degrees
node scripts/validate.mjsChecks referential integrity, schema compliance, prerequisite acyclicity, and credit consistency.
- A reference taxonomy for higher education data interchange
- A graph — courses connect to prerequisites, degrees, skills, and careers
- Multi-system — one course can declare its credit value in US, ECTS, and UK systems simultaneously
- Institution-agnostic — describes what is taught, not who teaches it
- Extensible by design — anyone can add new disciplines, courses, or framework mappings
- ❌ A learning management system
- ❌ An institutional accreditation database
- ❌ A replacement for official curriculum standards
- ❌ A K-12 skill taxonomy (that's Marble's job)
We admire Marble's work on the K-12 problem. Polymath is built for a fundamentally different domain:
| Marble Skill Taxonomy | Polymath | |
|---|---|---|
| Domain | Primary/elementary education (ages 4–14) | Higher education (undergraduate through doctorate) |
| Granularity | Micro-topics ("Building sentences") | University courses ("Data Structures & Algorithms") |
| Core structure | Topic → prerequisite DAG | Course → prerequisite DAG + degree requirements + career maps |
| Alignment | NGSS, Common Core, UK National Curriculum | ISCED-F 2013, CIP 2020, Bologna Process, AQF |
| Credit system | N/A (age-based progression) | Multi-system: US Credits, ECTS, UK CATS |
| Career dimension | Not included | First-class: skills → careers mapping |
| Degree programs | Not included | First-class: courses → degree pathways |
| Visual metaphor | 3D rotating globe | Constellation network graph |
| Data dimensions | 2 (topics + dependencies) | 6 (courses + disciplines + degrees + skills + careers + frameworks) |
This dataset is multi-licensed:
| Layer | License |
|---|---|
| The database — collection, structure, IDs, entity relationships | ODbL 1.0 — free for research and commercial use, attribution required, share-alike for derivative databases |
| Textual content — course descriptions, learning outcomes, skill definitions, career profiles | CC BY-SA 4.0 — attribution + share-alike |
| Framework references — ISCED-F, CIP, Bologna, AQF codes | Referenced under fair use; each framework is owned by its respective governing body |
Commercial use is welcome. ODbL distinguishes a derivative database (must stay open) from a produced work (your product stays yours). Build your app on Polymath — just contribute improvements to the taxonomy back.
Polymath Taxonomy (v0.1) · © ACLAS College · https://aclascollege.github.io/polymath/explorer/ · licensed under ODbL 1.0 (database) and CC BY-SA 4.0 (content).
- Computer Science course graph
- Business, management, finance, and marketing course graph
- AI, data science, mathematics, economics, and psychology expansion
- Interactive constellation explorer
- Community contribution workflow
- REST API for programmatic access
- Course equivalency mappings between institutions
See CONTRIBUTING.md. We welcome:
- New disciplines — submit a discipline proposal with ISCED-F alignment
- New courses — follow the schema and include learning outcomes
- Framework mappings — add your country's qualification framework
- Career data — link skills to real-world job profiles
- Translations — course names and descriptions in additional languages
@dataset{polymath2026,
title = {Polymath: The Open Taxonomy of Global Higher Education},
author = {{ACLAS College}},
year = {2026},
version = {v0.1},
publisher = {ACLAS College},
url = {https://github.com/aclascollege/polymath}
}Built with ❤️ by ACLAS College — Open to Global Students