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README.md

@@ -1,4 +1,21 @@
 
+```yaml
+language: 
+- english
+pretty_name: "Sound-of-Water 50"
+tags:
+- audio-visual learning
+- physical property estimation
+- pouring water
+license: "MIT"
+task_categories:
+- physical property estimation
+- fine-grained audio classification
+- liquid mass estimation
+- container shape estimation
+```
+
+
 <!-- # <img src="./assets/pouring-water-logo5.png" alt="Logo" width="40">  -->
 # 🚰 The Sound of Water: Inferring Physical Properties from Pouring Liquids
 
@@ -43,6 +60,7 @@ This dataset is associated with the paper "The Sound of Water: Inferring Physica
   - [🎬 YouTube samples](#-youtube-samples)
   - [📜 Citation](#-citation)
   - [🙏 Acknowledgements](#-acknowledgements)
+  - [🙅🏻 Potential Biases](#-potential-biases)
 
 
 ## 📚 Dataset Overview
@@ -240,3 +258,7 @@ We also want to highlight closely related work that could be of interest:
 * [Human sensitivity to acoustic information from vessel filling](https://psycnet.apa.org/record/2000-13210-019). Journal of Experimental Psychology (2020).
 * [See the Glass Half Full: Reasoning About Liquid Containers, Their Volume and Content](https://arxiv.org/abs/1701.02718). ICCV (2017).
 * [CREPE: A Convolutional Representation for Pitch Estimation](https://arxiv.org/abs/1802.06182). ICASSP (2018).
+
+## 🙅🏻 Potential Biases
+
+The dataset is recorded on a standard mobile phone from the authors themselves. It is recorded in a  indoor setting. As far as possible, we have tried to not include any personal information in the videos. Thus, it is unlikely to include harmdul biases. Plus, the scale of the dataset is small and is not likely to be used for training large models.