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license: agpl-3.0

COMMANDNET Military Science Dataset (1900-1999)

Format: ShareGPT JSONL Language: English Time span: 1900-1999 Rows: 10k

This dataset contains synthetic instruction-tuning examples for historical and doctrinal military analysis.

Dataset Summary

  • Task: chat instruction tuning
  • Domain: historical military analysis
  • Time span: 1900-1999
  • Format: ShareGPT JSONL
  • Languages: English
  • Size: 10,000 rows in the included generated artifact
  • License: See repository or dataset hosting metadata

It focuses on a consistent strategic voice and explicit:

  • Causal Analysis
  • Counterfactual Analysis

It covers both conventional and asymmetric contexts, constrained to the historical window 1900-1999.

Splits

  • train: single full JSONL file
  • validation: not provided
  • test: not provided

Format

Each record is a JSON object with a ShareGPT-style conversation and metadata including:

  • year
  • decade
  • warfare_type
  • doctrine_family
  • military_science_tags

Record Structure

Example:

{
  "id": "skynet-000001",
  "conversations": [
    {"from": "system", "value": "..."},
    {"from": "human", "value": "..."},
    {"from": "gpt", "value": "..."}
  ],
  "metadata": {
    "year": 1966,
    "decade": "1960s",
    "warfare_type": "conventional",
    "doctrine_family": "Amphibious Operational Sequencing",
    "military_science_tags": ["..."]
  }
}

Included Material

The dataset is organized around doctrine families such as:

  • Industrial Attrition and Trench Penetration
  • Infiltration and Decentralized Assault Groups
  • Deep Operation
  • Blitz and Mobile Combined Arms
  • Amphibious Operational Sequencing
  • Protracted People's War
  • Population-Centric Counterinsurgency
  • Maneuver Warfare and Decision-Cycle Pressure
  • AirLand Battle
  • Deterrence and Escalation Management

Quality Characteristics

  • Historical bound checks within 1900-1999
  • Explicit causal and counterfactual sections in the assistant text
  • Duplicate suppression using scenario fingerprints
  • ShareGPT-style formatting for chat fine-tuning workflows

Citation

If you use this dataset, cite the dataset card and any downstream work built from it according to your project requirements.