SchoolData/us-k12-schools

US K–12 Schools — Open Dataset for the AI Era

One encyclopedic paragraph for every one of the 122,675 K–12 schools in the United States, ready to use as pretraining text.

Ask a language model about a large suburban high school and it will answer. Ask it about the K–8 school in a rural county of 4,000 people and it has nothing to say — because nothing about that school was ever written down on the open web. Only about 12% of American schools have a Wikipedia article at all, and 11.3% of the rows here carry Wikipedia text once unreliable matches are excluded. This dataset covers the rest too, by deterministically rendering public federal data into prose, so that every school is visible to a model at the point where models actually learn: the corpus.

from datasets import load_dataset
ds = load_dataset("SchoolData/us-k12-schools", "articles", split="train")

Dataset Summary

Rows (one per school)	122,675
from_wikipedia = 1 (Wikipedia text)	13,853 (11.3%)
from_wikipedia = 0 (generated from NCES)	108,822 (88.7%)
Public / private	100,435 / 22,240
States and territories	56
Total text	~59.8M characters

A single config, articles. One row per school, keyed by NCES school ID. Every row has a text field; from_wikipedia tells you where that text came from.

Data Fields

Field	Type	Description
nces_id	string	NCES school ID — the primary key, unique across the dataset
name	string	School name as recorded by NCES
state	string	Two-letter state or territory code
sector	string	public (CCD) or private (PSS)
text	string	The school's encyclopedic paragraph
from_wikipedia	int8	1 = text derived from Wikipedia, 0 = generated from NCES
wikipedia_title	string	Source article title (from_wikipedia = 1 only)
wikidata_qid	string	Wikidata item for the school (from_wikipedia = 1 only)
wikipedia_revid	string	Exact source revision id (from_wikipedia = 1 only)
source	string	Article URL, or NCES CCD/PSS 2023-24
license	string	CC-BY-SA-4.0 or CC0-1.0 — see Licensing

Licensing

The dataset as a whole is distributed under CC BY-SA 4.0.

It mixes two license regimes, and the per-row license field records which applies:

Rows	Origin	Row license
from_wikipedia = 1	English Wikipedia	CC-BY-SA-4.0
from_wikipedia = 0	NCES CCD / PSS — US federal works, public domain	CC0-1.0

Because Wikipedia text is included, ShareAlike propagates to the combined distribution, so the whole dataset ships as CC BY-SA 4.0. If you need a permissively licensed subset, filter it out:

permissive = ds.filter(lambda r: r["from_wikipedia"] == 0)   # 108,822 rows, CC0-1.0

Wikipedia Attribution

Rows with from_wikipedia = 1 contain text derived from English Wikipedia, licensed CC BY-SA 4.0. Each such row links to its source article via wikipedia_title / wikidata_qid / wikipedia_revid; authorship is the article's revision history. Text was extracted, trimmed, and cleaned — it is not verbatim full articles. This dataset is redistributed under CC BY-SA 4.0.

Concretely, the changes made to Wikipedia content are: only the lead section was taken (via the MediaWiki extracts API with exintro, not the full article); it was converted to plain text; and surviving markup — unexpanded {{templates}}, raw [[wikilinks]], and inline [12] citation markers — was stripped. Wikipedia content was retrieved on 2026-09-07; wikipedia_revid pins the exact revision each row was taken from.

Non-Wikipedia rows

The 108,822 rows with from_wikipedia = 0 are rendered from NCES public-domain data by a deterministic template — no language model, no randomness, no inference. Every clause is optional and appears only when the underlying field exists, so a missing value produces a shorter sentence rather than a guess. Nothing in these rows is invented.

Limitations & Biases

Wikipedia coverage is unevenly distributed, and that is the problem this project exists to document. Coverage ranges from 0.2% (Puerto Rico), 3.9% (South Dakota) and 5.5% (Montana) up to 17.6% (Ohio) and 22.2% (Arkansas). Schools in rural and under-resourced communities are markedly less likely to have an article. Training on from_wikipedia = 1 alone would inherit that bias wholesale.

Generated rows are thin by construction. They contain only what NCES records — location, grade range, district, enrollment, staffing, and for private schools religious affiliation, demographics and facilities. No history, no notable alumni, no local context. They establish that a school exists and state verifiable facts about it; they are not a substitute for a written article.

Wikipedia matching is imperfect. Articles are linked to NCES schools by Wikidata NCES ID (11,254 matches), exact name + state (2,725), or fuzzy name similarity within a state (1,268). The fuzzy tier is the weakest: a June 2026 audit found it matching high-school articles to same-named elementary schools, because the matcher strips "high"/"elementary" as generic tokens before scoring. This release therefore rejects any match whose Wikipedia level cannot overlap the school's actual NCES grade range — 522 matches were dropped on that basis and those schools fall back to generated text. Residual risk remains on rows the gate cannot judge: articles that state no level, and schools with no usable grade range. Treat fuzzy-tier rows as the least reliable part of the dataset.

A few articles describe an institution larger than the school. Some Catholic parish schools link to an article covering the parish church and its school together, so the text includes material about the church. This affects a small number of rows.

The data is a snapshot. NCES figures are from the 2023–24 CCD and PSS collections; Wikipedia text was retrieved 2026-09-07. Schools open, close, and merge; enrollment moves.

Text length is modest. Median 376 characters for Wikipedia rows, 498 for generated rows — these are paragraphs, not long-form articles.

Provenance & Fiscal Host

Source	Contributes	Original license
NCES Common Core of Data (CCD) 2023–24	Public school records	US federal work, public domain
NCES Private School Survey (PSS) 2023–24	Private school records	US federal work, public domain
Wikidata	NCES ID linkage, QIDs	CC0
English Wikipedia	Lead-section text for 13,853 schools	CC BY-SA 4.0

This is a non-profit, community-verified civic data project, fiscally sponsored by Hack Club Bank, a 501(c)(3) organization.

Reproducing this release

Every stage of this dataset — the NCES crawl, the Wikipedia crawl and matcher, and the release build — is open source at github.com/bxshan/SchoolData.

The release is a pure function of the repository's data products: same inputs, identical output, no model and no randomness.

git clone https://github.com/bxshan/SchoolData
cd SchoolData/data/data_publish

python fetch_wikipedia_extracts.py     # Wikipedia lead sections + revision ids
python prep_data_publish.py            # -> output/dist/data/articles/*.parquet
python validate_publish.py --expect-rows 122000

prep_data_publish.py --state CA builds a single-state slice; --skip-match builds the NCES-only subset with no Wikipedia join. See CHANGELOG.md for the input commit hashes behind each version.

Citation

@misc{us_k12_schools,
  title  = {US K--12 Schools: An Open Dataset for the AI Era},
  author = {Shan, Boxuan and {The School Data Project}},
  year   = {2026},
  url    = {https://huggingface.co/datasets/SchoolData/us-k12-schools},
  note   = {CC BY-SA 4.0. Contains text from English Wikipedia.}
}