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README.md

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----
-license: apache-2.0
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+---
+
+pretty_name: MSNet Datasets
+language: en
+tags:
+  - proteomics
+  - mass-spectrometry
+  - deep-learning
+  - bioinformatics
+  - peptide-identification
+  - license mit
+  - size_categories 500M<n
+
+---
+
+# MSNet Datasets
+
+## Dataset Description
+
+MSNet Datasets is a large-scale, standardized, and AI-ready collection of mass spectrometry (MS) data designed for machine learning applications in computational proteomics.
+
+Instead of hosting raw data directly, this Hugging Face dataset serves as an **entry point and interface**, providing standardized access to externally hosted MSNet resources.
+
+The dataset addresses key limitations of existing public repositories, such as heterogeneous metadata, inconsistent processing pipelines, and lack of benchmarking standards, by offering a unified and reproducible data representation.
+
+---
+
+## Background
+
+Deep learning has become integral to modern proteomics, supporting tasks such as:
+
+* Fragment ion intensity prediction
+* Retention time (RT) prediction
+* Peptide–spectrum match (PSM) rescoring
+* De novo peptide sequencing
+
+Despite the abundance of publicly available MS data, most repositories primarily store raw files with inconsistent metadata and processing standards, making them difficult to use directly in machine learning workflows.
+
+---
+
+## Motivation
+
+Current proteomics datasets often suffer from:
+
+* Incomplete or inconsistent metadata
+* Heterogeneous preprocessing pipelines
+* Limited diversity in experimental conditions
+* Lack of standardized benchmarks
+
+MSNet Datasets provides a unified, curated, and ML-ready interface to facilitate reproducible research and fair model evaluation.
+
+---
+
+## Data Sources
+
+The dataset is curated from:
+
+* Public proteomics datasets (ProteomeXchange)
+* Large-scale projects (e.g., π-HuB)
+
+A total of **114 large-scale datasets** are included, covering diverse biological contexts, instrument platforms, and fragmentation strategies.
+
+---
+
+## Data Processing
+
+All datasets are systematically reprocessed using a reproducible workflow:
+
+* Metadata standardized using SDRF
+* Uniform reanalysis of raw MS data
+* Peptide-spectrum match (PSM) generation
+* Peak annotation including:
+
+  * b⁺, b²⁺, y⁺, y²⁺ ions
+  * With and without neutral losses
+  * Multiple mass tolerance settings applied during annotation
+* Harmonization into a unified structure
+
+---
+
+## Data Format
+
+Processed data are stored in **Parquet format**, enabling:
+
+* Efficient storage and compression
+* Fast I/O for large-scale data
+* Compatibility with PyTorch, TensorFlow, and other ML frameworks
+
+---
+
+## Dataset Structure
+
+Each entry corresponds to a peptide-spectrum match (PSM) and includes:
+
+| Column          | Description                      |
+| --------------- | -------------------------------- |
+| spectrum_id     | Spectrum identifier              |
+| mz_array        | m/z values                       |
+| intensity_array | Intensity values                 |
+| precursor_mz    | Precursor m/z                    |
+| charge          | Charge state                     |
+| peptide         | Peptide sequence                 |
+| modifications   | Post-translational modifications |
+| rt              | Retention time                   |
+| instrument      | Instrument type                  |
+| fragmentation   | Fragmentation method             |
+
+---
+
+## Access
+
+⚠️ **Note:** Hugging Face does **not host the raw data**.
+
+Instead, data can be accessed through the following official resources:
+
+* **Web portal:** https://msnet.ncpsb.org.cn
+* **Dataset hub:** https://quantms.org/datasets
+
+---
+
+## Official Loader
+
+We provide an official data loader for seamless integration:
+
+👉 https://github.com/PHOENIXcenter/MSNet
+
+Supports:
+
+* PyTorch
+* TensorFlow
+
+---
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# This dataset provides metadata / interface only
+dataset = load_dataset("your-username/msnet")
+
+print(dataset)
+```
+
+For full data access, please use the official loader.
+
+---
+
+## Use Cases
+
+* Training deep learning models for proteomics
+* PSM rescoring and confidence estimation
+* De novo peptide sequencing
+* Retention time and intensity prediction
+* Benchmarking computational methods
+
+---
+
+## Limitations
+
+* Data is hosted externally (not on Hugging Face)
+* PTM coverage is continuously expanding
+* Some modalities (e.g., DIA, XL-MS) are not fully integrated
+
+---
+
+## Citation
+
+If you use MSNet Datasets, please cite the corresponding π-MSNet publication.
+
+---
+
+## License
+
+MIT License
+
+---