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README.md

@@ -551,3 +551,120 @@ configs:
   - split: fd
     path: wtpg/fd-*
 ---
+
+<h1 align="center">
+  RMIS Benchmark Datasets
+</h1>
+
+## Introduction
+
+RMIS is a benchmark dataset collection for evaluating representation learning on **multi-modal industrial signals**. It brings together the datasets used in the RMIS benchmark, covering **anomaly detection** and **fault diagnosis** across four modalities: **sound, vibration, voltage, and current**.
+
+This Hugging Face repository is meant to provide the **benchmark datasets themselves** in an easy-to-load format. If you are looking for the full benchmark codebase, evaluation pipeline, or leaderboard, please refer to the [RMIS GitHub repository](https://github.com/jianganbai/RMIS).
+
+RMIS is closely related to **FISHER**:
+
+- **FISHER** is the foundation model proposed for industrial signal representation.
+- **RMIS** is the benchmark used to evaluate FISHER and other signal models.
+- This Hugging Face repository hosts the **dataset side** of RMIS, while the GitHub repository hosts the **benchmark code and evaluation pipeline**.
+
+In the current release, the dataset includes **19 configurations**:
+
+- **Anomaly detection**: `dcase20`, `dcase21`, `dcase22`, `dcase23`, `dcase24`, `dcase25`
+- **Fault diagnosis**: `iica`, `iiee`, `mafaulda_sound`, `mafaulda_vib`, `pu_cur`, `pu_vib`, `sdust_bearing`, `sdust_gear`, `umged_cur`, `umged_sound`, `umged_vib`, `umged_vol`, `wtpg`
+
+## What is included
+
+Each configuration corresponds to one benchmark subset. The exact schema varies by subset, but all configurations provide an `audio` column together with file-level metadata such as `file_name`, and task-specific annotations such as `status`, `scene`, `mt`, `ori`, `domain`, or related attributes.
+
+The split names follow the benchmark tasks:
+
+- `ad`: anomaly detection subsets
+- `fd`: fault diagnosis subsets
+
+Please note that this repository focuses on **dataset hosting and loading**. Detailed benchmark construction, preprocessing rationale, evaluation protocol, and model integration are documented in the RMIS GitHub repository.
+
+## Usage
+
+Load one subset with `datasets`:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("jiangab/RMIS", "dcase20", split="ad")
+print(ds)
+print(ds[0])
+```
+
+If you want decoded waveforms instead of deferred audio objects:
+
+```python
+from datasets import load_dataset, Audio
+
+ds = load_dataset("jiangab/RMIS", "dcase20", split="ad")
+ds = ds.cast_column("audio", Audio(sampling_rate=16000))
+
+sample = ds[0]
+audio = sample["audio"]["array"]
+sr = sample["audio"]["sampling_rate"]
+print(audio.shape, sr)
+```
+
+Load another configuration in the same way:
+
+```python
+from datasets import load_dataset
+
+# fault diagnosis example
+fds = load_dataset("jiangab/RMIS", "umged_vib", split="fd")
+print(fds.column_names)
+```
+
+If you want to download the **entire dataset repository at once** instead of loading one configuration at a time, you can download the whole Hugging Face dataset repo locally:
+
+```python
+from huggingface_hub import snapshot_download
+
+local_dir = snapshot_download(
+    repo_id="jiangab/RMIS",
+    repo_type="dataset",
+)
+print(local_dir)
+```
+
+You can also use the Hugging Face CLI:
+
+```bash
+hf download jiangab/RMIS --repo-type dataset
+```
+
+Please note that `load_dataset` is typically used **one configuration at a time**, while whole-repository download is useful when you want all released subsets stored locally.
+
+## Recommended usage in the RMIS project
+
+If your goal is to **benchmark a model on RMIS**, this dataset repository is only one part of the workflow. A typical setup is:
+
+1. Load a subset from this Hugging Face dataset repository.
+2. Extract signal representations with your model.
+3. Evaluate the representations with the RMIS benchmark pipeline from the GitHub repository.
+
+In other words, this repository provides the **benchmark data**, while the RMIS codebase provides the **standardized evaluation**.
+
+## Acknowledgements
+
+RMIS is built from multiple public industrial signal datasets. We thank the original dataset creators for making these resources available.
+
+If you believe that any content in this repository infringes your rights, please contact us and we will address the issue promptly.
+
+## Citation
+
+If you find RMIS useful, please cite the following paper.
+
+```bibtex
+@article{fan2025fisher,
+  title={FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation},
+  author={Fan, Pingyi and Jiang, Anbai and Zhang, Shuwei and Lv, Zhiqiang and Han, Bing and Zheng, Xinhu and Liang, Wenrui and Li, Junjie and Zhang, Wei-Qiang and Qian, Yanmin and Chen, Xie and Lu, Cheng and Liu, Jia},
+  journal={arXiv preprint arXiv:2507.16696},
+  year={2025}
+}
+```