  With the rapid development and widespread application of VR/AR technology,
maximizing the quality of immersive panoramic video services that match users'
personal preferences and habits has become a long-standing challenge.
Understanding the saliency region where users focus, based on data collected
with HMDs, can promote multimedia encoding, transmission, and quality
assessment. At the same time, large-scale datasets are essential for
researchers and developers to explore short/long-term user behavior patterns
and train AI models related to panoramic videos. However, existing panoramic
video datasets often include low-frequency user head or eye movement data
through short-term videos only, lacking sufficient data for analyzing users'
Field of View (FoV) and generating video saliency regions.
  Driven by these practical factors, in this paper, we present a head and eye
tracking dataset involving 50 users (25 males and 25 females) watching 15
panoramic videos. The dataset provides details on the viewport and gaze
attention locations of users. Besides, we present some statistics samples
extracted from the dataset. For example, the deviation between head and eye
movements challenges the widely held assumption that gaze attention decreases
from the center of the FoV following a Gaussian distribution. Our analysis
reveals a consistent downward offset in gaze fixations relative to the FoV in
experimental settings involving multiple users and videos. That's why we name
the dataset Panonut, a saliency weighting shaped like a donut. Finally, we also
provide a script that generates saliency distributions based on given head or
eye coordinates and pre-generated saliency distribution map sets of each video
from the collected eye tracking data.
  The dataset is available on website: https://dianvrlab.github.io/Panonut360/.
