  360{\deg} videos in recent years have experienced booming development.
Compared to traditional videos, 360{\deg} videos are featured with uncertain
user behaviors, bringing opportunities as well as challenges. Datasets are
necessary for researchers and developers to explore new ideas and conduct
reproducible analyses for fair comparisons among different solutions. However,
existing related datasets mostly focused on users' field of view (FoV),
ignoring the more important eye gaze information, not to mention the integrated
extraction and analysis of both FoV and eye gaze. Besides, users' behavior
patterns are highly related to videos, yet most existing datasets only
contained videos with subjective and qualitative classification from video
genres, which lack quantitative analysis and fail to characterize the intrinsic
properties of a video scene. To this end, we first propose a quantitative
taxonomy for 360{\deg} videos that contains three objective technical metrics.
Based on this taxonomy, we collect a dataset containing users' head and gaze
behaviors simultaneously, which outperforms existing datasets with rich
dimensions, large scale, strong diversity, and high frequency. Then we conduct
a pilot study on user's behaviors and get some interesting findings such as
user's head direction will follow his/her gaze direction with the most possible
time interval. A case of application in tile-based 360{\deg} video streaming
based on our dataset is later conducted, demonstrating a great performance
improvement of existing works by leveraging our provided gaze information. Our
dataset is available at https://cuhksz-inml.github.io/head_gaze_dataset/
