ArticleScientific data2026
A multi-level visual representation dataset for large-scale non-financial information disclosure.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.