AI 中文总结
研究人员因美学数据集差异大、缺乏集中搜索系统而难选合适数据集，为此开发了DODA Web应用，提供数据集信息与属性，助力跨领域合作。
AI 中文摘要
随着经验美学与计算美学领域的快速发展，已出现大量经美学标注的大型图像数据集。由于这些图像数据库在诸多方面差异巨大（如标注标准不同），找到最符合自身研究需求的数据集可能十分繁琐。缺乏集中式开放科学搜索系统会引发更多问题，目前研究人员通常在论文或OSF、GitHub、Dropbox等不同平台分享数据集链接，手动搜索图像质量、内容等细节往往需要下载所有数据集。因此，我们提出了美学研究数据集数据库（Database Of Datasets for Aesthetics，DODA），这是一款直观的Web应用，研究人员可在其中浏览所有重要的美学研究数据集。DODA提供这些数据集的一般信息（规模、分辨率、标注类型、标注者数量等），还为其中许多数据集提供预先计算的定量图像属性。我们讨论了利用DODA选择合适数据集的相关标准，并说明了复用数据集的益处，该方法促进了经验美学与计算美学领域的跨领域合作。
英文摘要
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
