JunhanCai/GEMS-Raman-Dataset

GEMS: Multi-Source Raman Spectral Dataset

This repository hosts the comprehensive, multi-domain Raman spectral datasets utilized in the GEMS framework.

The data is meticulously structured to support our proposed multi-stage training methodology, encompassing initial pre-training, contrastive learning, and diverse downstream fine-tuning tasks across different domains.

Data Pre-processing & Format

All spectral data across the sub-directories have been uniformly pre-processed. They are ready for direct model input without requiring additional transformations:

• Spectral Range: 0 – 3500 cm⁻¹
• Sequence Length: Exactly 3,500 data points per spectrum.
• Normalization: Min-Max normalization has been applied to all samples.
• Format: Standard array formats ready for PyTorch/NumPy ingestion.

Dataset Structure & Training Stages

The repository is organized into 7 distinct subsets. Each directory serves a highly specific role in the GEMS pipeline, ensuring continuous model optimization and rigorous experimental validation.

Core Training Stages

Directory	Stage	Primary Purpose
QMe14S	Stage 1	Foundation Pre-training. Used to train the initial foundational encoder.
RRUFF_CL	Stage 2	Contrastive Learning. Directly inherits the parameters from Stage 1 for continued training, aiming to build robust, generalized feature representations.

Downstream Fine-Tuning & Analytical Experiments

The remaining datasets leverage the pre-trained weights from Stage 2 for specific downstream applications and model evaluation:

Directory	Experimental Focus / Task
RRUFF_FT	Architecture & Hyperparameter Optimization. Used as the benchmark to optimize the model's structural design and training configurations.
Bacteria_ID	Few-Shot Learning Study. Evaluates the model's generalization capabilities and performance stability when fine-tuned on highly limited annotated data.
skincancer	Interpretability Analysis. Investigates which specific spectral wavebands and features the deep learning model focuses on for medical diagnostics.
Mutant_wheat	Downstream Fine-Tuning. Agricultural domain classification task.
microplastic	Downstream Fine-Tuning. Environmental monitoring and material classification task.

Usage

You can easily download the entire dataset or specific sub-directories using the official Hugging Face CLI:

# Download the entire dataset
hf download YourUsername/YourDatasetName --repo-type dataset --local-dir ./data

# Or download a specific subset (e.g., Stage 1 Pre-training data)
hf download YourUsername/YourDatasetName QMe14S/* --repo-type dataset --local-dir ./data/QMe14S