Explainable Machine Learning Approach for Brain Tissue Characterization through Photonic Crystal Fiber Sensing

This dataset contains simulation-generated and processed data used to train, validate, and test machine learning models for optimizing the structural and optical parameters of monolithic gold plasmonic sensors. The dataset includes refractive index (ri) values, real and imaginary values of x and y axes for different wavelength, loss curve and sensitivity. All data were generated through numerical simulations and systematically curated to ensure consistency and reproducibility. This dataset may be used for research, validation, and comparative studies related to plasmonic sensor optimization and data-driven photonic design.本数据集包含仿真生成并经后处理的数据，用于训练、验证及测试机器学习模型，以优化单片金基等离子体激元传感器（monolithic gold plasmonic sensors）的结构与光学参数。本数据集涵盖折射率（refractive index，简称RI）数值、不同波长下x轴与y轴的实部及虚部数值、损耗曲线及灵敏度数据。所有数据均通过数值仿真生成，并经过系统性整理与质控，以确保数据的一致性与可复现性。本数据集可用于等离子体激元传感器优化及数据驱动光子学设计相关的研究、验证与对比研究工作。