Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language: en
+pretty_name: "Koopman DA Benchmark Dataset"
+size_categories:
+- n<1K
+tags:
+- numpy
+- hdf5
+---
+
+# Koopman Data Assimilation Dataset
+
+Dataset accompanying the benchmark for Data Assimilation (DA) tasks with Koopman-inspired models.
+
+Code repository: [github.com/Wenxuan52/KoopmanDABench](https://github.com/Wenxuan52/KoopmanDABench)
+
+## What Is Hosted Where
+
+The project is split across two locations:
+
+- **GitHub repo** — source code, plus a few small files under `data/` (e.g. data-generation scripts, small reference files, configs).
+- **This Hugging Face dataset** — the large pre-computed `.npy` / `.h5` files that are too big to track in Git.
+
+On a new machine, cloning the GitHub repo and then downloading this dataset into the same `data/` folder gives you the complete setup. The two sets of files do not overlap.
+
+## Final `data/` Layout
+
+After both `git clone` and `hf download`, the `data/` folder should look like:
+
+```
+data/
+├── kol_generate_data/            # from GitHub
+├── ERA5_High/
+│   ├── process_weatherbench.py   # from GitHub
+│   ├── test.py                   # from GitHub
+│   └── raw_data/                 # from Hugging Face
+├── ERA5/                         # from Hugging Face
+├── dam/                          # from Hugging Face
+├── cylinder/                     # from Hugging Face
+└── kol/                          # from Hugging Face
+```
+
+---
+
+## Quick Start on a New Machine
+
+### 1. Clone the code from GitHub
+
+```bash
+git clone https://github.com/<your-username>/irp-test-framework.git
+cd irp-test-framework
+```
+
+This already populates `data/` with the small files tracked in Git (e.g. generation scripts).
+
+### 2. Set up the Python environment
+
+```bash
+conda create -n koopmanda python=3.10 -y
+conda activate koopmanda
+pip install -r requirements.txt        # or: pip install -e .
+pip install -U huggingface_hub
+```
+
+### 3. Pull the large data files from Hugging Face into `data/`
+
+Authenticate once (only needed if this dataset is private; skip for public datasets):
+
+```bash
+hf auth login
+```
+
+Then download into the existing `data/` folder — files from GitHub are not affected:
+
+```bash
+hf download <your-username>/<your-dataset-name> \
+    --repo-type dataset \
+    --local-dir ./data
+```
+
+Equivalent Python call:
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id="<your-username>/<your-dataset-name>",
+    repo_type="dataset",
+    local_dir="./data",
+)
+```
+
+The project should now run exactly as it did on the original machine.
+
+## License
+
+Released under the MIT License.