Write the dataset card of v1

README.md

@@ -10,6 +10,75 @@ tags:
 - statistical-physics
 size_categories:
 - 100K<n<1M
+configs:
+- config_name: dw4
+  data_files:
+  - split: test
+    path: viewer/dw4_6b739c6d.parquet
+- config_name: lj13
+  data_files:
+  - split: test
+    path: viewer/lj13_6a0a72c4.parquet
+- config_name: lj55
+  data_files:
+  - split: test
+    path: viewer/lj55_dc3f6b7e.parquet
+- config_name: seeds
+  data_files:
+  - split: test
+    path: viewer/seeds_f9a50f20.parquet
+- config_name: brownian
+  data_files:
+  - split: test
+    path: viewer/brownian_45794097.parquet
+- config_name: lgcp
+  data_files:
+  - split: test
+    path: viewer/lgcp_cea6d9a6.parquet
+- config_name: lgcp-whitened
+  data_files:
+  - split: test
+    path: viewer/lgcp-whitened_30e98363.parquet
+- config_name: breast-cancer
+  data_files:
+  - split: test
+    path: viewer/breast-cancer_543f5f94.parquet
+- config_name: german-credit
+  data_files:
+  - split: test
+    path: viewer/german-credit_3cfdf175.parquet
+- config_name: ionosphere
+  data_files:
+  - split: test
+    path: viewer/ionosphere_d00109c3.parquet
+- config_name: sonar
+  data_files:
+  - split: test
+    path: viewer/sonar_52aa10ef.parquet
+- config_name: trialanine
+  data_files:
+  - split: test
+    path: viewer/aaa-T310_e0bfbee3.parquet
+- config_name: hexaalanine
+  data_files:
+  - split: test
+    path: viewer/aaaaaa-T310_08d2d916.parquet
+- config_name: aldp
+  data_files:
+  - split: test
+    path: viewer/ace-a-nme-T300_54968005.parquet
+- config_name: ala4
+  data_files:
+  - split: test
+    path: viewer/ace-aaa-nme-T300_cda31701.parquet
+- config_name: ka-ternary-T0.32
+  data_files:
+  - split: test
+    path: viewer/ka-ternary-N44-T0.32_d50e8a3f.parquet
+- config_name: ka-ternary-T1.0
+  data_files:
+  - split: test
+    path: viewer/ka-ternary-N44-T1.0_c0f9b3b8.parquet
 ---
 
 # samplingdb reference sets
@@ -20,7 +89,8 @@ these sets. Each one comes from a long MCMC run on the target's own density, and
 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.
+checks its sha256, and keeps it in its cache. To look at a set first, open the dataset viewer above
+and pick the target in the subset menu.
 
 ## Quick start
 
@@ -62,6 +132,27 @@ path = hf_hub_download('h2o64/samplingdb', 'pt/dw4_6b739c6d.npy', repo_type='dat
 x = np.load(path)   # (n_samples, dim)
 ```
 
+## Browsing, and loading with `datasets`
+
+Each set also has a Parquet copy in `viewer/`, which is what the dataset viewer shows: one subset
+per target, named as in `samplingdb.build`, with one row per sample. The column `x` is the sample
+and `log_prob` is the samplingdb target's unnormalised log density at it, whose histogram the viewer
+draws above the column. For the particle systems, the peptides and the glass, `log_prob` is minus
+the energy in units of kT, so its histogram is the energy distribution, mirrored.
+
+The same files load with the `datasets` library, under the split `test`:
+
+```python
+from datasets import load_dataset
+
+lj55 = load_dataset('h2o64/samplingdb', 'lj55', split='test')
+rows = lj55.with_format('numpy')[:]
+x, log_prob = rows['x'], rows['log_prob']   # (100352, 165) and (100352,)
+```
+
+The `.npy` files are what samplingdb reads and what the tag pins; the Parquet copies hold the same
+rows.
+
 ## Files
 
 Each row of an array is one sample, flattened, in the coordinates the samplingdb target's