--- 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: ```json { "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.