| --- |
| 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} |
| } |
| ``` |
|
|
