Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating a Null Model of Scientific Image Reuse to Support Research
  Integrity Investigations   When there is a suspicious figure reuse case in science, research integrity
investigators often find it difficult to rebut authors claiming that "it
happened by chance". In other words, when there is a "collision" of image
features, it is difficult to justify whether it appears rarely or not. In this
article, we provide a method to predict the rarity of an image feature by
statistically estimating the chance of it randomly occurring across all
scientific imagery. Our method is based on high-dimensional density estimation
of ORB features using 7+ million images in the PubMed Open Access Subset
dataset. We show that this method can lead to meaningful feedback during
research integrity investigations by providing a null hypothesis for scientific
image reuse and thus a p-value during deliberations. We apply the model to a
sample of increasingly complex imagery and confirm that it produces
decreasingly smaller p-values as expected. We discuss applications to research
integrity investigations as well as future work.

5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg