整合美学研究图像数据集，一键查找匹配需求的高质量资源。
DODA: A Database of Datasets for Aesthetics Research
- 构建统一网页平台，集中展示美学研究常用图像数据集。
- 提供数据集规模、标注类型、图像属性等关键信息，支持快速筛选。
- 助力科研协作与复用，推动美学领域开放科学发展。
随着实证美学与计算美学领域的快速发展，大量用于美学评价的图像数据集应运而生。然而，这些数据集在标注标准、图像质量、内容分布等方面差异显著，难以高效匹配研究需求。目前缺乏统一的公开检索系统，研究人员多通过论文或 OSF、GitHub、Dropbox 等平台分散共享数据集链接，常需逐个下载才能获取图像质量与内容细节，耗时费力。为此，本文提出美学研究数据集数据库（DODA），一个直观的 Web 应用程序，集成当前重要的美学研究图像数据集。DODA 提供各数据集的基本信息（如规模、分辨率、标注类型、标注者数量等），并对其中多数数据集预计算并展示定量图像属性。我们讨论了基于 DODA 的数据集选择标准，并展示了数据复用的优势。该方法有助于促进实证与计算美学领域的协作与开放科学实践。
原文摘要 · Abstract (English)
With rapid growth in the fields of empirical and computational aesthetics we have seen a vast increase in large image datasets annotated for aesthetics. As the image databases differ widely in many respects (e.g., different standards for annotation), it can be tedious to find the dataset that fits one's research needs best. The absence of a centralized open-science search system causes additional problems. Currently, researchers typically share dataset links in papers or on diverse platforms like OSF, GitHub or Dropbox. Manually searching for details like image quality and content often requires downloading all datasets. Therefore, we present the Database Of Datasets for Aesthetics (DODA), an intuitive Web application in which researchers can browse all important datasets for aesthetics research. DODA provides general information about these datasets (size, resolution, type of annotation, number of annotators, etc.) and for many of them also precomputed quantitative image properties. We discuss relevant criteria for selecting a suitable dataset with DODA and illustrate the benefits of reusing datasets. Our approach facilitates collaboration across the fields of empirical and computational aesthetics. Keywords: empirical aesthetics, computational aesthetics, machine learning, image annotation, quantitative image properties, Open Science
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