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README.md

@@ -1,21 +1,50 @@
 ---
-dataset_info:
-  features:
-  - name: data_type
-    dtype: string
-  - name: data
-    dtype: string
-  - name: filename
-    dtype: string
-  splits:
-  - name: Apartment_release_clean_seq131_M1292
-    num_bytes: 135442390
-    num_examples: 13
-  download_size: 57744554
-  dataset_size: 135442390
-configs:
-- config_name: default
-  data_files:
-  - split: Apartment_release_clean_seq131_M1292
-    path: data/Apartment_release_clean_seq131_M1292-*
+language:
+- en
+license:
+- mit
 ---
+
+# ADT Dataset
+
+## Dataset Description
+This dataset contains Aria Digital Twin (ADT) sequences with various sensor data and annotations.
+
+## Usage Example
+```python
+from datasets import load_dataset
+import pandas as pd
+import json
+import io
+
+def deserialize_csv(csv_string):
+    return pd.read_csv(io.StringIO(csv_string))
+
+def deserialize_json(json_string):
+    return json.loads(json_string)
+
+# Load the dataset
+dataset = load_dataset("ariakang/ADT-test")
+sequence = dataset["{sequence_name}"]
+
+# Get list of available files
+files = list(zip(sequence["filename"], sequence["data_type"]))
+print("Available files:", files)
+
+# Load specific data
+for i, (filename, data_type, data) in enumerate(zip(
+    sequence["filename"], sequence["data_type"], sequence["data"]
+)):
+    if data_type == "csv":
+        df = deserialize_csv(data)
+        print(f"Loaded CSV {filename}: {len(df)} rows")
+    elif data_type in ["json", "jsonl"]:
+        json_data = deserialize_json(data)
+        print(f"Loaded JSON {filename}")
+    elif data_type == "vrs_info":
+        vrs_info = deserialize_json(data)
+        print(f"VRS files: {[f['filename'] for f in vrs_info]}")
+```
+
+## VRS Files
+VRS files are stored in: sequences/{sequence_name}/vrs_files/