Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward A Deep Understanding of What Makes a Scientific Visualization
  Memorable   We report results from a preliminary study exploring the memorability of
spatial scientific visualizations, the goal of which is to understand the
visual features that contribute to memorability. The evaluation metrics include
three objective measures (entropy, feature congestion, the number of edges),
four subjective ratings (clutter, the number of distinct colors, familiarity,
and realism), and two sentiment ratings (interestingness and happiness). We
curate 1142 scientific visualization (SciVis) images from the original 2231
images in published IEEE SciVis papers from 2008 to 2017 and compute
memorability scores of 228 SciVis images from data collected on Amazon
Mechanical Turk (MTurk). Results showed that the memorability of SciVis images
is mostly correlated with clutter and the number of distinct colors. We further
investigate the differences between scientific visualization and infographics
as a means to understand memorability differences by data attributes.

15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th