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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](https://github.com/samyama-ai/samyama-graph).
Loader and ETL: [samyama-ai/edtech-kg](https://github.com/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.
```cypher
// 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](https://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`](https://catalog.pwcs.edu/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](https://github.com/samyama-ai/edtech-kg) and
> it will be actioned.
**Attribution:**
> Course catalogue data from **Prince William County Schools**,
> [catalog.pwcs.edu](https://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
```python
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
```
|