Update dataset card

README.md

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+---
+license: other
+license_name: sdrbench
+license_link: https://sdrbench.github.io/
+pretty_name: "SDRBench: QMCPACK"
+tags:
+- scientific-data
+- lossy-compression
+- sdrbench
+- hpc
+- sz3
+---
+
+# SDRBench — QMCPACK
+
+Many-body ab initio Quantum Monte Carlo (electronic structure of atoms, molecules and solids): einspline orbitals, single precision, little-endian. The archive holds the original layout (115, 69, 69, 288) with the orbital index fastest; variant 288x115x69x69 gives the preconditioned layout (288 orbitals x 115 x 69 x 69).
+
+This repository is an **unmodified mirror** of the QMCPACK dataset from
+[SDRBench](https://sdrbench.github.io/datasets.html), the Scientific Data Reduction Benchmark. The originals are hosted by
+Argonne National Laboratory on Globus; every archive was unpacked and its files uploaded
+byte-for-byte (sha256 verified). `metadata/` holds the original SDRBench property and template
+files. The mirror is kept in sync automatically by
+[szcompressor/sdrbench](https://github.com/szcompressor/sdrbench).
+
+- **Data source:** QMCPACK performance test; contact Ye Luo (yeluo@anl.gov)
+- **Total size:** 630.7 MB
+- **Format:** raw little-endian binary, C order (slowest dimension first)
+
+## Usage
+
+```bash
+pip install "sdrbench[sz3]"     # sdrbench + pysz (SZ3)
+```
+
+```python
+import numpy as np
+import sdrbench
+from pysz import sz, szConfig, szErrorBoundMode
+
+ds = sdrbench.dataset("qmcpack")         # default variant: 115x69x69x288
+print(ds.fields)
+x = ds["einspline"]                     # numpy array, dtype <f4, shape (115, 69, 69, 288)
+
+# compress with SZ3 (pysz) at a 1e-3 value-range-relative error bound
+conf = szConfig()
+conf.errorBoundMode = szErrorBoundMode.REL
+conf.relErrorBound = 1e-3
+compressed, ratio = sz.compress(np.ascontiguousarray(x), conf)
+y, _ = sz.decompress(compressed, x.dtype.type, x.shape)
+max_err, psnr, nrmse = sz.verify(np.asarray(x), y)
+print(f"ratio {ratio:.1f}x, PSNR {psnr:.1f} dB, max error {max_err:.3g}")
+```
+
+Other variants: `sdrbench.dataset("qmcpack", "288x115x69x69")`.
+
+Files are downloaded on first use and cached; if Hugging Face is unreachable the package falls
+back to the original archive on Globus.
+
+Without the package, any file can be read with `huggingface_hub` and numpy:
+
+```python
+from huggingface_hub import hf_hub_download
+import numpy as np
+p = hf_hub_download("sdrbench/qmcpack", "115x69x69x288/einspline_115_69_69_288.f32", repo_type="dataset")
+x = np.fromfile(p, dtype="<f4").reshape((115, 69, 69, 288))
+```
+
+## Contents
+
+
+### `115x69x69x288` (default)
+
+3 files, 630.7 MB, from [SDRBENCH-QMCPack.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/QMCPACK/SDRBENCH-QMCPack.tar.gz).
+
+| Field | dtype | Shape (C order) | File |
+|---|---|---|---|
+| `einspline` | `<f4` | 115 x 69 x 69 x 288 | `115x69x69x288/einspline_115_69_69_288.f32` |
+
+Other files: `115x69x69x288/SDRBENCH-QMCPACK-115x69x69x288-property.txt`, `115x69x69x288/template_data.txt`
+
+### `288x115x69x69`
+
+Preconditioned layout (orbital, z, y, x) recommended by the QMCPACK users and used in the SDRBench command examples (einspline_288_115_69_69.pre.f32). Not stored separately: computed on load by transposing the original file.
+
+Derived layout: no extra files; computed on load from `115x69x69x288/`.
+
+| Field | dtype | Shape (C order) | File |
+|---|---|---|---|
+| `einspline` | `<f4` | 288 x 115 x 69 x 69 | `115x69x69x288/einspline_115_69_69_288.f32` |
+
+## Citation
+
+If you use this dataset, please cite SDRBench:
+
+```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}
+}
+```
+
+## License and terms
+
+The data are distributed under the same terms as on the [SDRBench website](https://sdrbench.github.io/datasets.html).
+Rights remain with the original data providers listed above.