Datasets:
Full dataset: documents, pages (3.1M w/ embeddings), entities (31M), topics, features, keywords, events, relationships
9375119 verified metadata
language:
- en
license: cc0-1.0
task_categories:
- text-classification
- token-classification
- feature-extraction
size_categories:
- 100K<n<1M
tags:
- declassified
- government
- historical
- ocr
- entities
- embeddings
Research Document Archive
Computational analysis of 234,630 declassified U.S. government documents across 7 archival collections. Output of a 13-step ML pipeline extracting OCR text, entities, topics, keywords, redactions, and semantic embeddings from 3.1 million pages.
Files
| File | Rows | Description |
|---|---|---|
documents.parquet |
234,630 | Document metadata: id, source_section, file_path, file_hash, total_pages |
pages/<section>.parquet |
3.1M | Per-page OCR text + 1536-dim sentence-transformer embeddings. Sharded by collection. |
entities/<section>.parquet |
31M | Named entities (spaCy en_core_web_lg): PERSON, ORG, DATE, GPE, FAC, LOC, NORP, EVENT. Sharded by collection. |
document_topics.parquet |
234,629 | BART-large-MNLI zero-shot topic assignments (per doc, top-1 topic + probability) |
document_features.parquet |
1.17M | EAV feature table: redaction_summary, forensic_metadata, bertopic, sentiment, topic_distribution, exact_duplicate |
document_keywords.parquet |
3.5M | TF-IDF keywords (top 15 per document, unigrams + bigrams) |
document_dates.parquet |
234,630 | Inferred document dates (regex + header parsing) |
document_events.parquet |
296K | Document-to-historical-event correlations (20 crisis events) |
historical_events.parquet |
20 | Crisis event dictionary (event_name, date range, category, keywords) |
entity_relationships.parquet |
2.88M | Entity co-occurrence pairs with counts, distances, and sample documents |
Collections
| source_section | Documents | Source |
|---|---|---|
cia_declassified |
1,605 | CIA Reading Room |
cia_mkultra |
1,936 | MKULTRA release |
cia_stargate |
13,937 | Stargate remote viewing program |
doj_disclosures |
— | DOJ public disclosures |
house_resolutions |
181,092 | House.gov bill text (GovInfo API) |
jfk_assassination |
35,979 | National Archives JFK release |
lincoln_archives |
21 | Library of Congress |
Loading
from datasets import load_dataset
docs = load_dataset("datamatters24/research-document-archive", data_files="documents.parquet")
# Or load a specific sharded table:
import pyarrow.parquet as pq
pages = pq.read_table("hf://datasets/datamatters24/research-document-archive/pages/cia_mkultra.parquet")
Methodology
- OCR: Tesseract + PyMuPDF
- NER: spaCy
en_core_web_lg - Topics: BART-large-MNLI (zero-shot) + BERTopic (unsupervised)
- Embeddings: sentence-transformers/all-MiniLM-L6-v2 (384-dim) and OpenAI text-embedding-3-small (1536-dim) per page
- Redaction detection: OpenCV contour analysis on PDF-rendered pages
- Entity relationships: page-window co-occurrence with distance weighting