---
title: SDRBench
emoji: 📦
colorFrom: blue
colorTo: indigo
sdk: static
pinned: false
---

# SDRBench — Scientific Data Reduction Benchmark

[SDRBench](https://sdrbench.github.io/) provides reference scientific datasets for benchmarking
lossy and lossless data reduction (compression) techniques: climate, weather, cosmology,
molecular dynamics, quantum chemistry, turbulence, combustion, X-ray imaging and fusion
simulations from DOE laboratories and their partners.

This organization hosts an **unmodified mirror** of the SDRBench datasets, which are
originally distributed by Argonne National Laboratory through Globus. Every file is
byte-for-byte identical to the one in the original SDRBench archive (sha256 verified), and
the mirror is kept in sync automatically.

```bash
pip install sdrbench
```

```python
import sdrbench
sdrbench.list()                      # available datasets
nyx = sdrbench.dataset("nyx")
nyx.fields                           # field names
x = nyx["temperature"]               # numpy array, C order
```

- Website and documentation: https://sdrbench.github.io/
- Python package and sync pipeline: https://github.com/szcompressor/sdrbench
- Maintained by the SDRBench team (Argonne National Laboratory and collaborators)

**Citation.** Please cite SDRBench and the data provider named on each dataset card:

```bibtex
@inproceedings{zhao2020sdrbench,
  title     = {{SDRBench}: Scientific Data Reduction Benchmark for Lossy Compressors},
  author    = {Zhao, Kai and Di, Sheng and Liang, Xin and Li, Sihuan and Tao, Dingwen and Chen, Zizhong and Cappello, Franck},
  booktitle = {2020 IEEE International Conference on Big Data (Big Data)},
  pages     = {2716--2724},
  year      = {2020}
}
```