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Astronautics Synthetic Instruction Dataset
Synthetic Data Generation
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About
Simulated dialogues tailored specifically for the fields of astronautics and space mission engineering provide a specialised resource for refining large language models. By capturing 901 synthetic conversations, the material addresses the need for high-quality instructional data in niche technical domains. These interactions, generated using advanced models and inspired by established research methodologies, bridge the gap between general-purpose AI and the precise requirements of space engineering, allowing for more accurate and context-aware responses in professional aerospace applications.
Columns
- id: A unique alphanumeric identifier assigned to each specific conversation to ensure traceability and facilitate merging with other STEM datasets.
- topic: The broad area within astronautics or space mission engineering being discussed, such as Space Propulsion Systems or Space Law.
- subtopic: A specific niche within the main topic, providing granular detail on subjects like Injector Design or Electric Ion Thrusters.
- persona: A descriptive profile of the simulated user, ranging from basic technicians seeking practical solutions to researchers requiring data-driven analysis.
- opening_question: The initial query posed by the simulated user to trigger the AI-assistant's response and start the dialogue.
- messages: A structured list of the entire conversation between the user and the assistant, formatted for immediate use with standard transformer libraries.
Distribution
The data is delivered in a CSV format under the filename
data.csv, with a total file size of 4.41 MB. It contains 901 distinct records, each representing a complete dialogue. The resource exhibits a 100% validity rate across all six columns, with no missing or mismatched entries. While currently a static collection of 901 instances, the material is expected to undergo annual updates to incorporate community feedback and broader model insights.Usage
This resource is primarily intended for the supervised fine-tuning of chat-based large language models to improve their performance in technical scientific domains. It serves as an excellent foundation for training assistants that can handle complex queries regarding orbital mechanics, satellite subsystems, and space policy. For optimal results, users are encouraged to augment this data with broader science, technology, engineering, and maths datasets to bolster the model's underlying knowledge base.
Coverage
The scope is strictly technical, focusing on the domain of space mission engineering and astronautics. All records are provided in English. The topical range is vast, covering twenty-three major categories including Human Spaceflight, Planetary Rovers, Space Business, and Entry Descent and Landing (EDL). The simulated personas vary in expertise, ensuring the data reflects a range of professional interactions within the aerospace industry.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
AI researchers and developers can utilise these dialogues to fine-tune models for specialised aerospace applications. Space engineers and students may use the dataset to explore synthetic interactions in their field or to benchmark the performance of domain-specific chatbots. Additionally, data scientists working on STEM-focused language models can integrate this material into larger training pipelines to enhance technical accuracy.
Dataset Name Suggestions
- AstroChat: Space Engineering Dialogue Corpus
- Astronautics Synthetic Instruction Dataset
- Space Mission Engineering Fine-Tuning Collection
- Aerospace Technical Conversation Records
Attributes
Original Data Source: Astronautics Synthetic Instruction Dataset
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