scientific-data-simulation

What problem does it solve?

Generates realistic synthetic scientific data based on physical, chemical, and biological models to augment limited real-world datasets, enabling robust ML development and benchmarking.

Core Features & Use Cases

• Physics/chemistry/biology-based data generation with domain-consistent relationships and realistic noise.
• Multi-domain templates supporting process data, materials data, clinical-like simulations, spectra and signals, enabling benchmarking and validation across Exp-06, Exp-12, Exp-13 patterns.
• Use Case: Suppose you need 500 samples of a process dataset to prototype an ML model when experiments are scarce; this skill can generate that dataset reproducibly.

Quick Start

Generate a synthetic dataset using the provided Exp-12/13 templates to simulate a process with temperature, pressure, and time, seeded for reproducibility.