Abstract
Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in 3 fields; plant sciences, cell biology, and physiology (n = 580 papers). Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.
Highlights
Images are often used to share scientific data, providing the visual evidence needed to turn concepts and hypotheses into observable findings
Cell biology was one of the most visually intensive fields, with publications containing an average of approximately 0.8 photographs per page [1]
While there are many resources on fraudulent image manipulation and technical requirements for image acquisition and publishing [2,3,4], data examining the quality of reporting and ease of interpretation for image-based figures are scarce
Summary
Images are often used to share scientific data, providing the visual evidence needed to turn concepts and hypotheses into observable findings. Plant sciences papers included approximately 0.5 photographs per page [1]. While there are many resources on fraudulent image manipulation and technical requirements for image acquisition and publishing [2,3,4], data examining the quality of reporting and ease of interpretation for image-based figures are scarce. When scientists and journals share papers on social media, posts often include figures to attract interest. The PubMed search engine caters to scientists’ desire to see the data by presenting thumbnail images of all figures in the paper just below the abstract [8]. Readers can click on each image to examine the figure, without ever accessing the paper or seeing the introduction or methods. EMBO’s Source Data tool (RRID:SCR_015018) allows scientists and publishers to share or explore figures, as well as the underlying data, in a findable and machine readable fashion [9]
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