Write the dataset card of v1

README.md

@@ -1,3 +1,155 @@
 ---
 license: cc-by-4.0
+pretty_name: samplingdb reference sets
+tags:
+- sampling
+- mcmc
+- benchmark
+- bayesian-inference
+- molecular-dynamics
+- statistical-physics
+size_categories:
+- 100K<n<1M
 ---
+
+# samplingdb reference sets
+
+Reference samples for the targets of [samplingdb](https://github.com/h2o64/samplingdb), a PyTorch
+library of unnormalised densities for benchmarking samplers. A sampler's output is scored against
+these sets. Each one comes from a long MCMC run on the target's own density, and was checked for
+convergence and independence before it was published here.
+
+You do not need to download anything by hand. samplingdb fetches a set the first time it is used,
+checks its sha256, and keeps it in its cache.
+
+## Quick start
+
+```sh
+pip install git+https://github.com/h2o64/samplingdb
+```
+
+```python
+from samplingdb import build
+
+target = build('lj55')
+reference = target.test_samples(n=10000)   # downloads the set once (66 MB), then reads the cache
+metrics = target.compute_metrics(samples)  # samples: your sampler's output, same shape
+```
+
+To fetch sets ahead of time, for example before a job on machines with no internet access:
+
+```python
+import samplingdb
+
+samplingdb.download_references(['lj55', 'aldp'])   # by catalog name
+samplingdb.download_references()                   # every published set, 3.1 GB
+```
+
+The cache is `~/.cache/samplingdb`, or the directory `SAMPLINGDB_CACHE` names. It is laid out like
+this repo, so a copy of the repo is a ready cache:
+
+```sh
+hf download h2o64/samplingdb --repo-type dataset --revision v1 --local-dir "$SAMPLINGDB_CACHE"
+```
+
+Without samplingdb, every file is a plain NumPy array:
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download('h2o64/samplingdb', 'pt/dw4_6b739c6d.npy', repo_type='dataset', revision='v1')
+x = np.load(path)   # (n_samples, dim)
+```
+
+## Files
+
+Each row of an array is one sample, flattened, in the coordinates the samplingdb target's
+`log_prob` takes:
+
+| Folder | Targets | Coordinates |
+| --- | --- | --- |
+| `pt/` | DW-4, LJ-13, LJ-55 | particle positions; samplingdb removes the centre of mass when it reads them |
+| `swapmc/` | ternary Kob-Andersen glass | particle positions in the periodic box, species in canonical order |
+| `md/` | peptides | atom positions in nanometres, in the atom order of the released PDB |
+| `stan/` | Bayesian posteriors | the unconstrained parameters, in samplingdb's order |
+
+A file is named `<folder>/<target name>_<spec_id>.npy`. The `spec_id` is a hash of the constants
+that define the density, so a set can never be read against a different density than the one it
+was drawn from. Next to every array, `<same name>_manifest.json` records the target, its `spec_id`,
+the full generation protocol with its diagnostics, and the array's sha256.
+
+| File | `build` name | Samples | dim | dtype | Size | Independence |
+| --- | --- | --- | --- | --- | --- | --- |
+| `pt/dw4_6b739c6d.npy` | `dw4` | 102400 | 8 | float32 | 3.3 MB | 91% effective |
+| `pt/lj13_6a0a72c4.npy` | `lj13` | 102400 | 39 | float32 | 16.0 MB | 98% effective |
+| `pt/lj55_dc3f6b7e.npy` | `lj55` | 100352 | 165 | float32 | 66.2 MB | 100% effective |
+| `stan/seeds_f9a50f20.npy` | `seeds` | 100000 | 26 | float64 | 20.8 MB | bulk ESS 96% of the draws |
+| `stan/brownian_45794097.npy` | `brownian` | 100000 | 32 | float64 | 25.6 MB | bulk ESS 92% of the draws |
+| `stan/lgcp_cea6d9a6.npy` | `lgcp` | 100000 | 1600 | float64 | 1280.0 MB | bulk ESS 90% of the draws |
+| `stan/lgcp-whitened_30e98363.npy` | `lgcp-whitened` | 100000 | 1600 | float64 | 1280.0 MB | bulk ESS 98% of the draws |
+| `stan/breast-cancer_543f5f94.npy` | `breast-cancer` | 100000 | 31 | float64 | 24.8 MB | bulk ESS 102% of the draws |
+| `stan/german-credit_3cfdf175.npy` | `german-credit` | 100000 | 25 | float64 | 20.0 MB | bulk ESS 110% of the draws |
+| `stan/ionosphere_d00109c3.npy` | `ionosphere` | 100000 | 35 | float64 | 28.0 MB | bulk ESS 96% of the draws |
+| `stan/sonar_52aa10ef.npy` | `sonar` | 100000 | 61 | float64 | 48.8 MB | bulk ESS 108% of the draws |
+| `md/aaa-T310_e0bfbee3.npy` | `trialanine` | 100000 | 99 | float32 | 39.6 MB | 92% effective |
+| `md/aaaaaa-T310_08d2d916.npy` | `hexaalanine` | 100005 | 189 | float32 | 75.6 MB | 98% effective |
+| `md/ace-a-nme-T300_54968005.npy` | `aldp` | 100000 | 66 | float32 | 26.4 MB | 96% effective |
+| `md/ace-aaa-nme-T300_cda31701.npy` | `ala4` | 100000 | 126 | float32 | 50.4 MB | 94% effective |
+| `swapmc/ka-ternary-N44-T0.32_d50e8a3f.npy` | `ka-ternary-T0.32` | 105600 | 88 | float32 | 37.2 MB | 20 relaxation times apart |
+| `swapmc/ka-ternary-N44-T1.0_c0f9b3b8.npy` | `ka-ternary-T1.0` | 102400 | 88 | float32 | 36.0 MB | 20 relaxation times apart |
+
+Independence is the effective sample size as a fraction of the rows: measured on the stored chains
+for the tempered runs, and Stan's smallest bulk ESS for the posteriors, which exceeds 100% where
+successive NUTS draws are anticorrelated. For the glass it is the spacing between stored
+configurations of one chain, in structural relaxation times.
+
+The rows are interleaved across chains, so the first `n` rows of a file span every chain of the run
+that produced it, and `test_samples(n=...)` can read a prefix.
+
+## How the sets were made
+
+Every set was generated by a script in the `gen/` folder of the samplingdb repository, which
+documents each protocol and its acceptance checks in `docs/reference-sets.md`.
+
+| Folder | Sampler | Checked before writing |
+| --- | --- | --- |
+| `pt/` | non-reversible parallel tempering with HMC moves | every chain makes 10 round trips and every pair of adjacent levels accepts at least 5% of its swaps; the merged set is at least 90% independent, with no drift from its start |
+| `md/` | replica-exchange molecular dynamics with solute tempering, BAOAB at 1 fs | every replica makes a round trip and every pair of adjacent levels accepts at least 5% of its swaps; the merged set is at least 90% independent on the pair distances and backbone torsions, with no drift from its start |
+| `swapmc/` | swap Monte Carlo: 100 relaxation times of equilibration, then one configuration every 20 | consecutive configurations of a chain have decorrelated, and the specific heat agrees between the two halves of the run |
+| `stan/` | Stan's NUTS, on models written in samplingdb's own coordinates | Stan's log density equals samplingdb's to 1e-9; R-hat below 1.01, bulk ESS above 90% of the draws, no divergences |
+
+## Not published yet
+
+Two catalogued targets have no set in this release. A later release will add them under a new tag.
+
+- `ka-binary-T0.2`, the binary Kob-Andersen glass of Cheng et al. 2026. Its parallel-tempering
+  replicas that crystallise stay trapped at intermediate temperatures, and the set generated so far
+  is not stationary.
+- `chignolin`. Its folding equilibrium is expensive to sample, and the planned 10 000-structure
+  set has not been run yet.
+
+## Versions
+
+samplingdb pins one tag of this repo and the sha256 of every file it reads. A release adds files
+under a new tag and never replaces a published one, so every samplingdb version keeps reading the
+exact bytes it was tested against. A density change gets a new `spec_id`, and therefore a new file.
+
+| Tag | Contents |
+| --- | --- |
+| `v1` | the 17 sets above |
+
+## License and attribution
+
+The samples are released under CC BY 4.0. They are draws from densities that others defined, so
+please also credit the sources of the densities you use:
+
+- DW-4, LJ-13 and LJ-55: the potentials of Köhler et al. 2020, as implemented in bgflow.
+- The logistic regressions: the Ionosphere, Sonar, Statlog (German Credit) and Breast Cancer
+  Wisconsin (Diagnostic) datasets of the UCI Machine Learning Repository, under CC BY 4.0. Seeds:
+  the data of Crowder 1978. Brownian: the model of inference_gym. LGCP: the Finnish pines data of
+  Møller et al. 1998.
+- The peptides: the systems of the Sequential Boltzmann Generators data (Tan et al. 2025), with the
+  Amber ff99SB-ILDN and ff14SB force fields and OBC implicit solvent, evaluated with OpenMM.
+- The ternary glass: the swap-friendly Kob-Andersen mixture of Jung et al. 2023, after Kob and
+  Andersen 1995.