Card: list the training data (datasets:, base_model:) and add a Training data section

Follow-up to braindecode PR #1493: `from_pretrained` reads `datasets:` into `model.card_data`. Ids: MOABB names, NEMAR nm/on ids, OpenNeuro ds ids; evidence per checkpoint.

README.md

@@ -7,6 +7,10 @@ tags:
   - sleep
   - foundation-model
   - braindecode
+datasets:
+- SSC
+- MESA
+- MrOS
 ---
 
 # SleepFM — pretrained encoder
@@ -40,6 +44,27 @@ time. Loading either way gives bit-identical outputs.
 Note that the released encoder is contrastive and carries **no classification head**:
 `final_layer` is randomly initialised and must be fine-tuned.
 
+<!-- braindecode:training-data -->
+## Training data
+
+The data that shaped these weights. The ids are the `datasets:` field of this card,
+which braindecode reads into `model.card_data.datasets` when you call
+`from_pretrained` (MOABB class names for MOABB datasets, OpenNeuro / NEMAR
+accessions, Hub ids otherwise). `nm`/`on` ids are datasets on [NEMAR](https://nemar.org)
+(`on` = NEMAR's copy of the OpenNeuro dataset with the same number). Rows marked
+`private:` are data the authors did not release; they are not in `datasets:`.
+
+| `datasets:` id | Dataset | Stage | Modality |
+|---|---|---|---|
+| `SSC` | [Stanford Sleep Clinic PSG cohort (Stanford Sleep Dataset)](https://sleepdata.org/datasets/ssc) | pretraining | PSG |
+| `private:BioSerenity` | [BioSerenity / SleepMed proprietary PSG collection (18,869 overnight recordings)](https://doi.org/10.1038/s41591-025-04133-4) | pretraining | PSG |
+| `MESA` | [Multi-Ethnic Study of Atherosclerosis (sleep ancillary, NSRR)](https://sleepdata.org/datasets/mesa) | pretraining | PSG |
+| `MrOS` | [Outcomes of Sleep Disorders in Older Men (MrOS Sleep, NSRR)](https://sleepdata.org/datasets/mros) | pretraining | PSG |
+
+- Leave-one-out multimodal contrastive pretraining on the 'pretrain' split of the four cohorts (>585,000 h, ~65,000 participants); SHHS explicitly held out. Upstream model_base/config.json: mode=leave_one_out, epochs=1.
+- Sources: Thapa et al., Nature Medicine 2026, doi:10.1038/s41591-025-04133-4, Results 'Dataset and SleepFM architecture'; https://github.com/zou-group/sleepfm-clinical README (main); braindecode SleepFM docstring (W/bd/braindecode/models/sleepfm.py).
+
+
 ## Usage
 
 ```python