Create README.md

README.md

@@ -1,12 +1,58 @@
 ---
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: "sequence.csv"
-license: mit
+pretty_name: "Latent DNA Diffusion Sequences (hg19)"
+tags:
+- genomics
+- biology
+- generative-ai
+- diffusion-models
+- dna
+- huggingscience
+- science
+license: "other" 
 task_categories:
-- text-generation
+- text-generation # Generating DNA sequences is analogous to text generation
 size_categories:
-- 1K<n<10K
----
+- 10M<n<100M
+---
+
+# Dataset Card for Latent DNA Diffusion
+
+## Dataset Description
+
+This dataset contains a collection of human DNA sequences, processed for the purpose of training generative models like the one described in the **Latent DNA Diffusion** project.  
+
+- **Source**: Human reference genome assembly **hg19 (GRCh37)**  
+- **Sequence length**: 256 base pairs  
+- **Format**: HDF5 file containing uniformly processed sequences  
+
+The primary purpose of this dataset is to serve as a training corpus for models that can learn the underlying patterns of the human genome and generate novel, realistic DNA sequences.  
+
+### Applications
+- Data augmentation for genomics  
+- Studying gene regulation  
+- Generating synthetic genomic data to preserve patient privacy  
+
+---
+
+## How to Use
+
+The data is stored in a single **HDF5** file.  
+
+Example usage in Python:
+
+```python
+import h5py
+from huggingface_hub import hf_hub_download
+
+# Download the HDF5 file from Hugging Face Hub
+file_path = hf_hub_download(
+    repo_id="Zehui127127/latent-dna-diffusion",
+    filename="human_hg19_256.hdf5",
+    repo_type="dataset"
+)
+
+# Open and explore the file
+with h5py.File(file_path, 'r') as f:
+    print("Available keys:", list(f.keys()))
+    sequences = f['sequences'][:]  # Example key
+    print("Shape of dataset:", sequences.shape)