A multi-level visual representation dataset for large-scale non-financial information disclosure

Although corporate sustainability reports increasingly employ visual rhetoric to influence stakeholder perceptions, quantitative tools for objectively measuring these strategies remain limited. Here we present the Non-Financial Information Disclosure Visual Representations Index (NFIVI) dataset, a dynamic resource covering Chinese listed companies. While the current release (2006-2024) encompasses a comprehensive collection of these reports, the dataset is updated annually, with data volume steadily increasing as new reports are processed. Utilizing a pipeline integrating layout analysis and computer vision, we decompose reports into three fundamental elements: text, image, and color. This dataset introduces two indices to objectively quantify visual composition and structure: the Feature-Correlation Index (NFIVI_FC), measuring stylistic consistency through multidimensional feature coherence, and the Information Entropy Index (NFIVI_EI), assessing visual complexity based on color diversity. Alongside 18 granular indicators spanning the text, image, and color dimensions at both page and document levels, these indices operationalize abstract design concepts into computable metrics. This resource enables large-scale quantitative research into corporate impression management and supports the development of automated auditing tools for non-financial disclosures.