updating interface

000_preview.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:90bcb110dcc8d1b54b2ec4ab071a3059e5b235959fb3c6fafb41a2f7c047c1cd
-size 6180

README.md

@@ -53,6 +53,8 @@ print("Timeseries:", None if X is None else X.shape, "  Connectivity:", None if
 
 ## Use with `datasets` (viewer‑ready, no scripts)
 
+Note: modern `datasets` (>= 3.x) does not execute local Python dataset scripts. Use `data_files=` with Parquet/JSONL as shown below.
+
 You can explore a tiny, fast preview split directly via the `datasets` library. The preview embeds a small 8×8 top‑left
 slice of the correlation matrix so the Hugging Face viewer renders rows/columns quickly. Paths to the full on‑disk
 arrays are included for downstream loading.
@@ -60,28 +62,31 @@ arrays are included for downstream loading.
 ```python
 from datasets import load_dataset
 
-# Tiny viewer-ready preview (embeds small 8×8 matrices). One split named "train".
-ds = load_dataset(
-    "pakkinlau/multi-modal-derived-brain-network",
-    data_files="manifests/preview.parquet",
-    split="train",
-)
+# Root-level Viewer splits (recommended on the Hub):
+#   train.parquet — tiny preview with embedded 8×8 matrices
+#   validation.parquet — metadata-only dev slice
+
+ds = load_dataset("pakkinlau/multi-modal-derived-brain-network", data_files="train.parquet", split="train")
 row = ds[0]
 print(row["parcellation"], row["subject"])      # e.g., 'AAL116', 'sub-control3351'
 print(row["corr_shape"], row["ts_shape"])      # e.g., [116, 116], [116]
 corr8 = row["correlation_matrix"]               # 8×8 nested list (for display)
 
 # Light dev slice (metadata+paths only). Stream to avoid downloads in CI.
-dev = load_dataset(
-    "pakkinlau/multi-modal-derived-brain-network",
-    data_files="manifests/dev.parquet",
-    split="train",
-    streaming=True,
-)
+dev = load_dataset("pakkinlau/multi-modal-derived-brain-network", data_files="validation.parquet", split="train", streaming=True)
 for ex in dev.take(3):
     _ = (ex["parcellation"], ex["subject"], ex["corr_path"])  # no embedded arrays
 ```
 
+You can also use the manifest entrypoints under `manifests/`:
+
+```python
+from datasets import load_dataset
+
+preview = load_dataset("pakkinlau/multi-modal-derived-brain-network", data_files="manifests/preview.parquet", split="train")
+dev = load_dataset("pakkinlau/multi-modal-derived-brain-network", data_files="manifests/dev.parquet", split="train", streaming=True)
+```
+
 To access the full arrays, load from the returned `corr_path` / `ts_path` using SciPy or `mat73` with variable name fallbacks:
 
 ```python
@@ -135,11 +140,39 @@ manifests/
   dev.parquet            # Parquet version (fast viewer)
 ```
 
+### Data files
+
+- Root (used by the Viewer):
+  - `train.parquet` — tiny viewer‑ready preview with embedded 8×8 correlation matrices
+  - `validation.parquet` — dev metadata‑only slice (no embedded arrays)
+- Manifests (secondary entrypoints):
+  - `manifests/preview.parquet` — same content as `train.parquet` (if duplicated)
+  - `manifests/dev.parquet` — same as `validation.parquet` (if duplicated)
+
 ### Integrity & Checksums
 
 Rows in the preview/dev manifests include `*_sha256` and `*_bytes` for both `corr_path` and `ts_path`, derived from `manifests/manifest.jsonl`.
 You can verify a local copy by recomputing SHA‑256 and matching the values.
 
+Example (verify a correlation .mat):
+
+```python
+import hashlib
+from pathlib import Path
+
+def sha256(path: Path, buf=131072):
+    h = hashlib.sha256()
+    with open(path, 'rb') as f:
+        while True:
+            b = f.read(buf)
+            if not b:
+                break
+            h.update(b)
+    return h.hexdigest()
+
+# compare with row['corr_sha256']
+```
+
 ### Scripts (optional)
 
 - `scripts/enrich_manifests.py`: Enrich preview/dev JSONL with shapes (from sidecars), embedded 8×8 tiles (from `preview/`), and checksums (from `manifests/manifest.jsonl`).

dataset_infos.json

@@ -23,8 +23,8 @@
     "builder_name": "parquet",
     "config_name": "default",
     "version": {"version_str": "1.0.0", "major": 1, "minor": 0, "patch": 0},
-    "splits": {"train": {"name": "train", "num_examples": 5}},
-    "data_files": {"train": ["train.parquet"]},
+    "splits": {"train": {"name": "train", "num_examples": 5}, "validation": {"name": "validation", "num_examples": 20}},
+    "data_files": {"train": ["train.parquet"], "validation": ["validation.parquet"]},
     "download_checksums": {},
     "download_size": 0,
     "dataset_size": 0,