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

@@ -9,6 +9,8 @@ tags:
 - bbl
 - self-supervised
 - foundation-model
+datasets:
+- CamCAN
 ---
 
 # BBL (pretrained, CamCAN)
@@ -31,6 +33,24 @@ The linear head (`final_layer`, `n_outputs=2` in `config.json`) is not part of t
 release: it is PyTorch's default init under `torch.manual_seed(0)`. Passing `n_outputs` to
 `from_pretrained` replaces it. Fine-tune or linear-probe before use.
 
+<!-- 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 |
+|---|---|---|---|
+| `CamCAN` | [Cambridge Centre for Ageing and Neuroscience (Cam-CAN) MEG data, Shafto et al. 2014 / Taylor et al. 2017](https://cam-can.mrc-cbu.cam.ac.uk/dataset/) | pretraining | MEG |
+
+- self-supervised band/phase/amplitude pretext tasks; 583 Cam-CAN subject rows trained (train split; rows with non-zero Adam state)
+- Sources: https://github.com/neural-processing-lab/the-brains-bitter-lesson/blob/3965a2be11677d9c2b9cecb88109a0af6bf19d1c/README.md; checkpoint hyper_parameters (inspected 2026-11-06, meg-ports-20261106/bbl/weights/CHECKPOINT.md and evidence/parity.log); arXiv:2406.04328 Sec. 4 Datasets.
+
+
 ## Intended input
 
 - MEG, `(batch, n_chans, 125)`: CamCAN's 306 Elekta `MEG*` channels in FIF order (the