Creating Clear and Informative Image-based Figures for Scientific Publications

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 three fields; plant sciences, cell biology and physiology. 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.

Reproducible image handling and analysis

“…In Fig 1, we demonstrate how one can end up in uneven contrast enhancement depending on how one processes image data (Fig 1A, original image; adjusting contrast Fig 1B, altogether; Fig 1C, individually). Design and layout suggestions for clear and effective figures can be found elsewhere (Schmied & Jambor, 2020; preprint: Jambor et al , 2020) and references therein.…”

Effective image visualization for publications – a workflow using open access tools and concepts

“…For generating a "poor" image example, we processed the raw microscope images minimally, only converting the bit depth from 16-bit to 8-bit and retained default color schemes. We did not add annotations, performed no image cropping, rotation, or specific brightness contrast adjustments as these often lack in poorly visualized images 4 . We thus simulated images as they are typically "processed" in the majority of current publications 4 .…”

“…We did not add annotations, performed no image cropping, rotation, or specific brightness contrast adjustments as these often lack in poorly visualized images 4 . We thus simulated images as they are typically "processed" in the majority of current publications 4 . To perform a qualitative assessment, we tested image visibility to color blind (deuteranopia) audiences using the color blindness simulator (RRID: SCR_018400; 25 ).…”

“…Problematic is also that methods often insufficiently report on image acquisition and processing 3 . Last, images frequently have low legibility, as only 10-20% of published images provide all key information (annotation of color/inset/scale/specimen) 4 . In the long run, problematic images may undermine the trust in scientific data and, when published in emerging image archives, reduce the value of such repositories 5,6 .…”

Training early career researchers to use meta-research to improve science: A participant-guided “learn by doing” approach

“…The participant-guided, “learn by doing” approach provided hands on experience in using meta-research to understand and solve problems with scientific research. The teams’ research examined the legibility and interpretability of images in scientific publications [ 11 ]. Participants systematically assessed image-based figures among papers published in the top 15 journals in three fields; plant sciences, cell biology and physiology.…”