Upload README.md

README.md

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-license: cc-by-4.0
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-
-# CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization
-
-Here we introduce the protein dataset used in our benchmark evaluation, with comprehensive 3D information and fine-grained localization annotations.
-
-Each file includes the following data:
-
-- `AlphaFold_pdb.tar`: The original PDB data predicted by AlphaFold2 with full protein documentation.
-- `coordinate.tar`: The extracted C-alpha coordinates from AlphaFold2.
-- `localization.tar`: The separate detaild and 20-class fine-grained localization annotations from Uniprot and HPA; The union localization annotations from Union and HPA; Amino acids sequence and 3Di tokens.
+# CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization
+
+### 📊 Dataset Specifications
+
+This repository contains the complete dataset of the paper: **"CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization"** *Accepted by **ICLR 2026***.
+
+Here we introduce the protein dataset used in our **CAPSUL** benchmark evaluation, with comprehensive 3D information and fine-grained localization annotations. Data is derived from AlphaFold2, UniProt, and the Human Protein Atlas (HPA).
+
+### 📂 Data Structure
+
+```
+CAPSUL/
+├── AlphaFold_pdb.tar      # Original AlphaFold2 PDB files
+├── coordinate.tar         # Extracted C-alpha (Cα) coordinates
+└── localization/          # Annotations and sequence features
+    ├── union.csv               # 20-class annotations (UniProt + HPA), used for evaluation in the paper
+    ├── uniprot_detail.csv      # Original raw annotations (UniProt)
+    ├── hpa_detail.csv          # Original raw annotations (HPA)
+    ├── uniprot_20.csv          # 20-class annotations (UniProt)
+    └── hpa_20.csv              # 20-class annotations (HPA)
+```
+
+*Note:* The implementation code is available at https://github.com/getbetter-hyccc/CAPSUL
+
+### 🔍 Detailed Component Breakdown
+
+- **`AlphaFold_pdb.tar`**: Contains the full original PDB files predicted by AlphaFold2. These include atomic coordinates, pLDDT scores, and complete protein metadata.
+- **`coordinate.tar`**: To facilitate faster training for geometric models, we provide pre-extracted C-alpha (Cα) coordinates. This reduces I/O overhead by focusing on the protein backbone trajectory.
+- **`localization/`**: Includes both the original raw labels and our standardized 20-class fine-grained labels. These annotations are specifically curated to evaluate model performance across diverse cellular compartments, whose processing details can be found in our paper.
+