Datasets:

VaidhyaMegha
/

edtech-kg

license: other
license_name: factual-catalogue-metadata-no-licence-granted
license_link: https://catalog.pwcs.edu/
language:
  - en
tags:
  - knowledge-graph
  - graph-database
  - cypher
  - education
  - curriculum
  - k-12
  - course-catalogue
pretty_name: Education-to-Career Pathways KG (one district)
size_categories:
  - 1K<n<10K
configs:
  - config_name: course
    data_files: nodes/course.csv
  - config_name: requirement
    data_files: nodes/requirement.csv
  - config_name: subject
    data_files: nodes/subject.csv
  - config_name: pathway
    data_files: nodes/pathway.csv
  - config_name: edge_in_subject
    data_files: edges/in_subject.csv
  - config_name: edge_requires
    data_files: edges/requires.csv
  - config_name: edge_includes
    data_files: edges/includes.csv
  - config_name: edge_has_requirement
    data_files: edges/has_requirement.csv

Education-to-Career Pathways Knowledge Graph

1,098 nodes. 1,287 edges. One US school district's published high-school course catalogue as a graph you can walk — which courses exist, what subject they sit in, what each requires, and which specialty pathways include them.

Built with Samyama Graph. Loader and ETL: samyama-ai/edtech-kg.

Why a graph

A student picks courses four times in high school. The decisions compound, they are close to irreversible, and they are made with almost no view of what each one leads to. A catalogue is a list; the thing a student actually needs is the reachability question — if I take this now, what opens later, and what have I closed off? That is a traversal, not a lookup.

// What does this course unlock, two steps out?
MATCH (c:Course {name: 'AP Computer Science Principles'})<-[:REQUIRES*1..2]-(later:Course)
RETURN DISTINCT later.name

// Which pathways can still be completed if a prerequisite was missed?
MATCH (p:Pathway)-[:INCLUDES]->(c:Course)-[:REQUIRES]->(pre:Course)
RETURN p.name, count(DISTINCT c) AS courses, collect(DISTINCT pre.name)[..5] AS gates
ORDER BY courses DESC

Source and licence — read this before reuse


Source	catalog.pwcs.edu — Prince William County Schools, Virginia, USA
Collected	2026-08-29, from the district's own published sitemap (960 pages)
Licence	None granted. See below.
Loader code	Apache-2.0, in the GitHub repo. This does not govern the data.

The district publishes this catalogue for public information; it does not attach an open licence to it. This dataset is offered on the following basis, which you should satisfy yourself with before relying on it:

The content here is factual catalogue metadata — course names, subject names, pathway names, short prerequisite statements, and source URLs. No course descriptions, no prose, no images are reproduced. Facts about what a public body offers are not themselves creative expression.
Collection followed the district's own published sitemap, and robots.txt disallows only administrative paths (/admin/, /core/, /user/*) — not catalogue content.
Every node carries its url, so the district's own page remains the authoritative record and this dataset points back to it rather than replacing it.

This is a weaker licence basis than the other datasets in this organisation, which rest on explicit public-domain or Creative Commons terms. It is stated plainly rather than glossed. If you are Prince William County Schools and would like this removed or amended, please open an issue on the GitHub repository and it will be actioned.

Attribution:

Course catalogue data from Prince William County Schools, catalog.pwcs.edu, collected 2026-08-29. The district does not endorse this dataset or any analysis derived from it.

Files

Nodes carry an id; edges reference those ids as src and tgt.

`nodes/`

File	Rows	Columns
`course.csv`	795	`id, district, name, source, url`
`requirement.csv`	138	`id, requirement_id, source, text`
`subject.csv`	127	`id, district, name, source, url`
`pathway.csv`	38	`id, district, kind, name, source, url`

`edges/`

File	Rows	Connects
`in_subject.csv`	724	Course → Subject
`requires.csv`	240	Course → Course
`includes.csv`	185	Pathway → Course
`has_requirement.csv`	138	Course → Requirement

REQUIRES is the edge that makes this a graph rather than a spreadsheet — it is what turns "which courses exist" into "what can this student still reach".

Usage

from datasets import load_dataset
courses = load_dataset("VaidhyaMegha/edtech-kg", "course", revision="v1.0")

Verification

Totals reconcile against the snapshot header: 1,098 nodes, 1,287 edges, and match the source repository's stated figures exactly.
The loader verified its own writes back out of the engine: "verified — every count read back from the engine", 3,698 statements.
0 dangling edges.
65 orphan nodes — 38 Subject, 23 Course, 4 Pathway. These are expected and were reported by the loader itself: "71 courses have no published subject page; left unattached rather than attached to something invented." Leaving them unlinked is the honest choice.

Limitations

One district, one point in time. Prince William County Schools, collected 2026-08-29. Catalogues change every year. This is not a national dataset and is not current by construction.
Prerequisites are as published, in prose. Requirement.text holds short strings such as "Enrolled in Agriculture Specialty Program". They are not parsed into machine-checkable logic, and an unparsed requirement is not the same as no requirement.
4 pages publish a pathway course table but load as courses, so their rows are not written as INCLUDES edges — a known gap tracked as issue #87 in the source repository.
17 published rows place a course in a second section of the same pathway; the graph holds one edge per pair with the section names joined onto it.
No college or career layer. The upstream repo measures IPEDS, CIP-SOC, BLS and College Scorecard for the national spine, but none of it is loaded here. The name says "education-to-career"; this release is the education half only.
Nothing here is advice about what any student should take. Check the district's own catalogue and a counsellor.

Citation

Education-to-Career Pathways Knowledge Graph, v1.0 (1,098 nodes, 1,287 edges).
Built with Samyama Graph. https://huggingface.co/datasets/VaidhyaMegha/edtech-kg
ETL: https://github.com/samyama-ai/edtech-kg
Source: Prince William County Schools course catalogue, https://catalog.pwcs.edu/
Collected: 2026-08-29