Create README.md

README.md

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+---
+language:
+- en
+license: mit
+tags:
+- Biology
+- Bioinformatics
+- Virus
+- Genomics
+- Proteomics
+- Nucleotide
+- Protein
+- Foundation Model
+- LucaVirus
+- LucaVirus-Mask
+- AI4Bio
+- AI4Science
+- Nucleotide-Protein
+task_categories:
+- feature-extraction
+size_categories:
+- 10M<n<100M
+---
+
+# Dataset Card for LucaVirus-OpenVirus-Gene
+
+## 1. Dataset Summary
+
+**LucaVirus-OpenVirus-Gene** is a large-scale genomic dataset consisting exclusively of viral nucleotide sequences. It is a specialized subset of the **OpenVirus** corpus, curated specifically for the pre-training of the **LucaVirus-Gene** foundation model.
+
+By focusing purely on viral genomes, this dataset provides a high-density corpus of 10.4 million sequences, enabling models to capture the intricate evolutionary patterns, regulatory motifs, and genomic architectures of DNA and RNA viruses.
+
+## 2. Dataset Statistics
+
+The dataset focuses solely on nucleotide sequences (genomes, genes, and fragments):
+
+| Feature | Count / Description |
+| :--- | :--- |
+| **Total Sequences** | 10.4 Million |
+| **Sequence Type** | Nucleotide (DNA/RNA) |
+| **`obj_type` Identifier** | `gene` (Exclusive) |
+| **Primary Use** | Pre-training for LucaVirus-Gene |
+
+## 3. Data Structure & Format
+
+### 3.1 File Organization
+The dataset is provided as a compressed **`.tar`** archive. Upon extraction, the data is partitioned into three standard machine-learning subsets:
+
+```text
+LucaVirus-OpenVirus-Gene/
+├── train/          # Training set (primary corpus for genomic pre-training)
+├── dev/            # Validation set (for model selection and tuning)
+└── test/           # Test set (for final evaluation and benchmarking)
+```
+
+Each directory (`train`, `dev`, `test`) contains one or more **CSV files** with headers.
+
+### 3.2 CSV Schema
+All CSV files follow a consistent four-column schema:
+
+| Column Name | Description | Details                                                                                                |
+| :--- | :--- |:-------------------------------------------------------------------------------------------------------|
+| **`obj_id`** | Sample ID | Unique identifier for each viral sequence.                                                             |
+| **`obj_type`** | Sequence Type | Set to `gene` for all entries in this dataset (Nucleotide).                                            |
+| **`obj_seq`** | Sequence Content | Raw nucleotide string (A, T(U), C, G, N).                                                              |
+| **`obj_label`** | Label | Metadata, taxonomic info, or functional labels associated with the genome (Annotation, Bio Knowledge). |
+
+
+## 4. Intended Use
+
+- **Genomic Foundation Modeling**: Building models like **LucaVirus-Gene** that specialize in the "language of genomes."
+- **Viral Evolution Studies**: Analyzing conserved nucleotide patterns across divergent viral lineages.
+- **Regulatory Element Discovery**: Identifying viral gene boundaries, promoters, and other non-coding functional motifs.
+
+## 5. Usage Example
+
+You can extract the archive and load the genomic data using the following Python snippet:
+
+```python
+import tarfile
+import pandas as pd
+import os
+
+# 1. Extract the genomic dataset
+with tarfile.open("LucaVirus-OpenVirus-Gene.tar.gz", "r:gz") as tar:
+    tar.extractall(path="./LucaVirus-OpenVirus-Gene")
+
+with tarfile.open("LucaVirus-OpenVirus-Gene/dataset.tar.gz", "r:gz") as tar:
+    tar.extractall(path="./LucaVirus-OpenVirus-Gene/dataset")
+    
+# 2. Load a sample from the training set
+train_path = "./LucaVirus-OpenVirus-Gene/dataset/v1.0/train"
+csv_files = [f for f in os.listdir(train_path) if f.endswith('.csv')]
+
+if csv_files:
+    # Load the first CSV file
+    df = pd.read_csv(os.path.join(train_path, csv_files[0]))
+    
+    # Verify the sequence type
+    print(f"Loaded {len(df)} genomic sequences.")
+    print(df[['obj_id', 'obj_seq']].head())
+```
+
+## 6. Related Resources
+
+This dataset is a core component of the **LucaGroup** biological modeling ecosystem.
+- **Full Corpus (Gene + Prot)**: [LucaVirus-OpenVirus-Gene-Prot](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Gene-Prot)
+- **Protein Subset**: [LucaVirus-OpenVirus-Prot](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Prot)
+- **Models**: Visit the [LucaVirus Collection](https://huggingface.co/collections/LucaGroup/lucavirus).
+- 
+## 7. Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@article{lucavirus2025,
+  title={Predicting the Evolutionary and Functional Landscapes of Viruses with a Unified Nucleotide-Protein Language Model: LucaVirus.},
+  author={Pan, Yuan-Fei* and He, Yong*. et al.},
+  journal={bioRxiv},
+  year={2025},
+  url={https://www.biorxiv.org/content/early/2025/06/20/2025.06.14.659722}
+}
+```
+
+## 8. License
+
+This dataset is released under the **MIT License**.
+
+## 9. Contact
+
+*For further information, please visit the [LucaGroup GitHub](https://github.com/LucaOne), email to: [YongHe: sanyuan.hy@alibaba-inc.com, heyongcsat@gmail.com], or contact the team via the Hugging Face organization profile.*
+