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Image . 2025
License: CC BY
Data sources: Datacite
ZENODO
Image . 2025
License: CC BY
Data sources: Datacite
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Dataset for "The persistence of paiting styles"

Authors: Munnangi, Reetikaa Reddy; Giunti, Barbara;

Dataset for "The persistence of paiting styles"

Abstract

Database of images of paintings analyzed in the paper "The persistence of painting styles", by Barbara Giunti and Reetikaa Reddy Munnangi. The code for running the experiments is available at https://github.com/BarbaraGiunti/persistence_paintings. The images in the dataset come from WikiArt or were generated by ChatGPT. Barbara Giunti and Reetikaa Reddy Munnangi do not claim ownership of these images. The images are collected here with the sole scope of ensuring the reproducibility of the experiments presented in the paper "The persistence of painting styles". The paper can be found here: https://arxiv.org/abs/2511.16695, while a short video with the highlights of the work can be found here: https://www.youtube.com/watch?v=sJSnidrEabM.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average