dataset_info.json

Provides metadata about the dataset.

Describes:

Dataset description and license

Feature types (string, int32, float32) and their meaning

Train/test split sizes (num_examples)

Size category (n<1K)

📌 Use case:
This file allows frameworks like 🤗 Datasets library to automatically parse schema, splits, and metadata when loading the dataset.

dataset_info.json

@@ -0,0 +1,53 @@
+{
+  "description": "The AI Battery Optimizer Dataset is a synthetic smartphone battery usage dataset created for research in energy optimization and ML-based app usage prediction.",
+  "citation": "@dataset{teamneuralbattery2025,\n  author = {Aishwarya Singh and Lavanya Arora and Shreya Kathuria and Navya Jain},\n  title = {AI Battery Optimizer Dataset},\n  year = {2025},\n  publisher = {Hugging Face},\n  license = {CC-BY-4.0}\n}",
+  "homepage": "https://huggingface.co/datasets/teamneuralbattery/AI-Battery-Optimizer",
+  "license": "CC-BY-4.0",
+  "features": {
+    "timestamp": {
+      "dtype": "string",
+      "description": "UTC timestamp of the log entry"
+    },
+    "battery_percentage": {
+      "dtype": "int32",
+      "description": "Battery percentage remaining"
+    },
+    "power_usage_mw": {
+      "dtype": "int32",
+      "description": "Power usage in milliwatts"
+    },
+    "time_remaining_min": {
+      "dtype": "int32",
+      "description": "Estimated time remaining in minutes"
+    },
+    "predicted_app": {
+      "dtype": "string",
+      "description": "Predicted app to be opened next"
+    },
+    "confidence": {
+      "dtype": "float32",
+      "description": "Confidence score of the prediction (0\u20131)"
+    },
+    "brightness": {
+      "dtype": "int32",
+      "description": "Screen brightness percentage"
+    },
+    "fps": {
+      "dtype": "int32",
+      "description": "Frame rate setting (30 or 60 FPS)"
+    }
+  },
+  "splits": {
+    "train": {
+      "num_examples": 40,
+      "name": "train"
+    },
+    "test": {
+      "num_examples": 10,
+      "name": "test"
+    }
+  },
+  "size_categories": [
+    "n<1K"
+  ]
+}