Abstract
Image databases are central to empirical aesthetics, enabling tests of how image statistics relate to observers’ appreciation. However, many existing databases have two key limitations: (1) they conflate low-level visual features with high-level semantic content, making it difficult to separate visual from cognitive influences on aesthetic judgments; and (2) they are imbalanced, overrepresenting highly appreciated images. To address these issues, we present the Minimum Semantic Content (MSC) database, a large, systematically curated resource for computational aesthetics. It comprises 10,426 natural scenes with reduced, homogenized semantic content, minimizing cognitive and emotional confounds. Each received 100 individual aesthetic ratings from naïve observers, drawn from a pool of approximately 10,000 participants, via crowdsourcing. The database includes both “beautified” and “uglified” versions, generated with a manipulation technique that promotes uniform coverage across the aesthetic spectrum. This broader distribution mitigates bias and overfitting in models. Validation also shows improved robustness in computational models overall. This database enables researchers to study how perceptual features shape aesthetic judgments, using stimuli with very limited semantic and contextual confounds.
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Data availability
The MSC database is openly available via the Open Science Framework (OSF)25, and can be accessed at https://doi.org/10.17605/OSF.IO/ZGSVJ. The OSF project includes two components: (1) “The MSC Database” and (2) “Validation Software”. The first component contains the complete image database as well as a comprehensive.csv file with the raw rating results. This arrangement allows users to easily determine the type and provenance of each image and provides access to detailed metadata and aesthetic ratings, supporting a wide range of analyses and applications. All images included in the MSC database are either in the public domain or released under licenses permitting reuse and redistribution.
Code availability
All QIP metrics were computed using the Python-based QIP toolbox of Redies et al.43, while all subsequent analyses and visualizations of these metrics were performed using custom R57 scripts, which are made publicly available. The second component of the OSF repository project, titled Validation Software, contains:
• All numerical tables and R code57, executed in RStudio58, for reproducing the statistical analyses and generating the figures in the main manuscript and Supplementary Material (MSC statistical validation and complete analysis of QIP metrics.zip). The code required to generate all corresponding figures (MSC_analysis_results_and_figures.zip).
• Automatically generated textual descriptions (captions) for images in the MSC Database produced using the GPT-4o59 vision-language model (MSC dataset captions.zip).
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Acknowledgements
C.A.P. and X.O. were supported by the Ministerio de Ciencia e Innovación, Gobierno de España MCIN/AEI/10.13039/501100011033: grants PID2020-118254RB-I00 and TED2021-132513B-I00, by the Agencia de Gestió d’Ajuts Univesitaris i de Recerca (AGAUR) through grant 2021-SGR-01470, and CERCA Programme / Generalitat de Catalunya. B.R. is supported by Grant PID2022-143257NB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A Way of Making Europa”. O.P. was funded by a Maria Zambrano Fellowship for the attraction of international talent for the requalification of the Spanish university system—NextGeneration EU (ALRC). A.J. was funded by the FI fellowship AGAUR 2022 FI-SDUR 00248 (Secretaria d’Universitats i Recerca, Generalitat de Catalunya, and Fons Social Europeu). This work was supported by the Departament de Recerca i Universitats (SGR 2025).
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C.A.P., O.P. and B.R. conceived the study. C.A.P., O.P. and A.J. developed the codebase and conducted the experiments. O.P., C.A.P. and A.J. performed the statistical analyses. C.A.P. and O.P. wrote the initial draft of the manuscript. Project supervision was carried out by C.A.P. and X.O. The manuscript was revised by O.P., C.A.P, B.R. and X.O. Data curation was performed by C.A.P. and O.P.
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Penacchio, O., Javed, A., Raducanu, B. et al. The Minimum Semantic Content (MSC) Dataset: A Large, Balanced Resource for Computational Aesthetics Research. Sci Data (2026). https://doi.org/10.1038/s41597-026-06816-0
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DOI: https://doi.org/10.1038/s41597-026-06816-0
