README.md

metadata

license: mit
task_categories:
  - text-classification
  - token-classification
  - text-mining
language:
  - en
tags:
  - government-documents
  - nlp
  - named-entity-recognition
  - declassified
  - jfk
  - cia
  - ocr
  - document-analysis
size_categories:
  - 100K<n<1M

Research Document Archive

234,630 declassified U.S. government documents processed through a 13-step ML pipeline. 3.2 million pages OCR'd, 31 million named entities extracted and linked, 288 topic clusters identified.

Live platform: tanglewoodapp.com

Collections

Collection	Documents	Pages	Size
House Resolutions	181,092	2,719,832	34.2 GB
JFK Assassination Records	35,979	241,860	22.5 GB
CIA Stargate Program	13,937	100,056	5.4 GB
CIA MKUltra	1,936	64,244	3.4 GB
CIA Declassified	1,605	29,744	2.4 GB
Lincoln Archives	21	9,330	962.9 MB

ML Pipeline (13 Steps)

1. Document ingestion and format normalization
2. OCR with Tesseract + post-correction
3. Classification stamp detection (SECRET, CONFIDENTIAL, UNCLASSIFIED, etc.)
4. Redaction detection and boundary mapping
5. Named entity recognition (people, organizations, locations, dates)
6. Entity disambiguation and cross-document linking
7. Relationship extraction
8. Topic modeling (LDA + BERTopic)
9. Timeline event extraction
10. Network graph construction
11. Sentiment and tone analysis
12. Document similarity clustering
13. Index building for search and retrieval

Classification Stamps Detected

Stamp	Count
UNCLASSIFIED	16,501
SECRET	13,736
CLASSIFIED	10,730
EXEMPT	6,739
CONFIDENTIAL	5,554
RESTRICTED	4,722

Key Statistics

• 31M named entities extracted
• 2.9M entity cross-document links
• 59,830 redactions detected and mapped
• 288 topic clusters identified
• 6 document collections spanning 1860s–2000s

Usage

from datasets import load_dataset

ds = load_dataset("datamatters24/research-document-archive")

# Filter by collection
jfk = ds.filter(lambda x: x["collection"] == "jfk_assassination")

# Search by entity
cia_docs = ds.filter(lambda x: "CIA" in x["entities"])

Data Sources

All documents are public record obtained from:

• National Archives (NARA)
• CIA FOIA Reading Room
• Congress.gov
• Library of Congress

Citation

@misc{rubin2026researcharchive,
  author = {Rubin, Theodore},
  title = {Research Document Archive: ML Pipeline for Declassified U.S. Government Documents},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/datamatters24/research-document-archive}
}