DOI: 10.4121/22154798
title: An image dataset for studying time of day perception in paintings

description: We have compiled a dataset of 194 images for studying time of day perception in paintings. The images were sourced from the Materials in Painting (materialsinpaintings.tudelft.nl) and National Gallery (nationalgallery.org.uk/paintings) datasets and depict outdoor scenes under varying illumination conditions, ranging from dawn to night. The paintings included in the dataset span the 17th to 20th centuries. The dataset is divided into two groups, each used as stimuli for two online rating experiments. Group 1 comprises 104 images, while Group 2 contains 90 images with metadata indicating the depicted time of day. The metadata includes information about the artist, time period of creation, and other relevant information. All images are licensed under Creative Commons Zero (CC0) except for those from the National Gallery dataset, which are subject to Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0. However, users can still access these images directly from the National Gallery dataset using the metadata provided. Our dataset is designed to facilitate research on time of day perception in paintings and improve our understanding of the factors that may influence this perception.<br><strong>File Format</strong>Excel (.xlsx) and ZIP (.zip) files<br><strong>Content Overview</strong><strong>Excel Sheets</strong><strong>(A) List_of_paintings including metadata</strong><strong>(A-1) List_of_paintings_for_Group 1.xlsx</strong>This file contains the list of paintings in Group 1 with their corresponding file names and metadata. The metadata includes information about the artist, the time period in which the painting was created, the location of the collection where the painting is held, and any relevant content of the painting that might be related to the depiction of time of day.<strong>(A-2) List_of_paintings_for_Group 2.xlsx</strong>This file contains the list of paintings in Group 2 with their corresponding file names and metadata. The metadata includes information about the artist, the time period in which the painting was created, the location of the collection where the painting is held, and any relevant content of the painting that might be related to the depiction of time of day.<br><strong>Images</strong><strong>(B) Images of paintings</strong><strong>(B-1) Images of paintings for Group 1.zip</strong>The images in this file belong to Group 1, as denoted by the file name, and comprise a set of paintings available for download in JPG digital format. Please note that images originally sourced from the National Gallery dataset are not included here, and users should download them directly from the National Gallery website at nationalgallery.org.uk/paintings.<strong>(B-2) Images of paintings for Group 2.zip</strong>This file includes a set of images of paintings that are part of Group 2, as indicated by its name. These images can be downloaded in a JPG digital format.<br><strong>Usage Notes</strong>This dataset is intended for academic research purposes.Please cite the dataset as follows: Yu, C. (2023). An image dataset for studying time of day perception in paintings. 4TU. ResearchData, https://doi.org/10.4121/22154798.<strong> </strong><strong>Funding</strong>H2020 Marie Skłodowska-Curie Actions (H2020-MSCA-ITN-2017) “DyViTo: Dynamics in Vision and Touch,” project number 765121.The Netherlands Organization for Scientific Research, VIDI project “Visual communication of material properties,” project number 276.54.001.<strong> </strong><strong>Contact Information</strong>Cehao Yu, C.Yu-2@tudelft.nl<strong> </strong><strong>References</strong>Yu, C., Van Zuijlen, M. J. P., Spoiala, C., Pont, S. C., Wijntjes, M. W. A., & Hurlbert, A. (2023). Time-of-day perception in paintings. Journal of Vision, 0(0):08633, 1–27, https://doi.org/10.1167/jov.0.0.08633.Yu, C., Van Zuijlen, M. J., Spoiala, C., Pont, S. C., Wijntjes, M., & Hurlbert, A. (2023, August 24). Time of day perception in paintings. PsyArXiv Preprints. https://doi.org/10.31234/osf.io/7xyrn.