| --- |
| license: mit |
| viewer: false |
| tags: |
| - chemistry |
| - raman-spectroscopy |
| - deep-learning |
| - self-supervised-learning |
| - time-series |
| --- |
| |
| # 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: |
|
|
| ```bash |
| # 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 |
