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Creating Clear and Informative Image-based Figures for Scientific Publications

Helena Jambor, Alberto Antonietti, Bradly Alicea, Tracy L. Audisio, Susann Auer, Vivek Bhardwaj, Steven J. Burgess, Iuliia Ferling, Małgorzata Anna Gazda, Luke H. Hoeppner, Vinodh Ilangovan, Hung Lo, Mischa Olson, Salem Yousef Mohamed, Sarvenaz Sarabipour, Aalok Varma, Kaivalya Walavalkar, Erin M. Wissink, Tracey L. Weissgerber

Posted on: 8 December 2020

Preprint posted on 8 October 2020

Article now published in PLOS Biology at http://dx.doi.org/10.1371/journal.pbio.3001161

Recommendations for image-based figures in scientific publications.

Selected by Mariana De Niz

Categories: cell biology, ecology, physiology, plant biology

Background

Images are often used to share scientific data, providing the visual evidence needed to turn concepts and hypotheses into observable findings. While many resources exist regarding guidelines on fraudulent image manipulation and technical requirements for image acquisition and publishing, data examining the quality of reporting and ease of interpretation for image-based figures are scarce.Moreover, recent evidence suggests that important methodological details about image acquisition are often missing. Although general recommendations emphasize that authors should design figures for their audience rather than themselves, and that figures should be self-explanatory, figures are often difficult to interpret, and therefore represent a problem for accessibility and importantly, for reproducibility. In their work, Jambor et al (1) (as part of the eLife Ambassadors program of 2018 and 2019) examined the quality of reporting and accessibility of image-based figures among papers published in top journals in plant sciences, cell biology and physiology. Based on their results, they provide targeted recommendations about how informative image-based figures can be created in a manner that makes them both accessible and reproducible.

 

Figure 1. Example on suggested recommendations for image annotation (Ref.1).

Key findings and developments

Findings regarding image-related information across three fields: plant science, physiology and cell biology.

The authors focused on papers from the top 15 journals within three large research fields, namely plant sciences, cell biology and physiology. These were the key findings:

  1. Scale bars: 50% of papers approximately, had complete scale bars for each image provided, while the remaining 30% and 20% had incomplete or fully missing information, respectively.
  2. Insets: The majority of papers in all three fields clearly and accurately marked the location of all insets, however, one-fifth of papers appeared to have marked the location of at least one inset incorrectly, while in approximately one-fifth, clear inset markings were missing for some or all insets.
  3. Accessibility to readers with colour blindness: Although papers without any colorblind accessible figures were uncommon, almost half of cell biology papers and 1/4th of physiology and plant science papers contained some images that were inaccessible to readers with deuteranopia. Further, up to a third of papers contained color annotations that were not visible to someone with deuteranopia.
  4. Figure legends: The majority of physiology and cell biology papers provided a full description of the objects shown in the figure. About half of the papers also failed to adequately explain insets. About 66% of all papers clearly stated the meaning of all image labels, and the vast majority explained what each colour represented.
  5. Based on all criteria defined in this study, the authors found that only 16% of physiology papers, 12% of cell biology papers, and 2% of plant science papers, met the criteria for all image-based figures in any given paper.

Based on the above findings, the authors provide a set of recommendations for creating clear and informative image-based figures for scientific publications.

 

Recommendations for creating clear and informative image-based figures for scientific publications.

  1. Choose a scale or magnification that fits the research question. Namely, that it allows to see the features described and needed to answer the question- be this anywhere in the range between whole body level and sub-cellular level. When both low and high magnifications are necessary for one image, insets should be used to show a small portion of the image at higher magnification, and the inset location should be accurately marked in the low magnification image. Also, insets should be explained in the figure legends.
  2. Include a clearly labeled scale bar. The authors offer various suggestions of what a clearly labeled scale bar should include: every image type should have a scale bar; these scale bars and labels should be clearly visible; the dimensions should be stated with the scale bar.
  3. Use colours wisely. In a scientific context, adapting colours is possible and may enhance readers’ understanding. In many instances, scientists can choose between displaying greyscale or colour images. Greyscale is often used to display fine details, or when visibility is compromised.
  4. Choose a colourblind accessible colour palette. Colourblind-safe colour palettes for fluorescence and other images should be used – there are programs simulating how a specific combination of colours would look to readers with deuteranopia (who cannot distinguish red and green), and tritanopia (who cannot distinguish green and blue). The authors recommend the use of free tools for this, such as Color Oracle. The authors suggest that cyan and magenta, or green and magenta are combinations visible to readers with normal vision, deuteranopia and tritanopia. In addition to choosing an adequate colour palette, information can be displayed in separate channels in addition to the merged image. Individual channels may be shown in greyscale to make it easier for readers to perceive fine details.
  5. Figure design. In multi-pannel figures, careful planning is needed to convey a clear message, while ensuring that all panels fit together and follow a logical order. The authors provide in their work a planning table to help with figure design. They highlight as an important aspect, the direction of information depending on their array of columns/rows, and how readers approach this information (i.e. top to bottom/left to right). The authors suggest QuickFigures as a tool that helps create multipanel figures for microscopy-based images.
  6. Figure annotation. The image should have the right amount of annotations, without it being too little, too abstract, or too long such that it covers the image and/or results in a label difficult to interpret. In such case, explanations of the labels should be placed in the figure legend. Additionally, abbreviations should be used cautiously, and should be explained in the figure legend even if they are defined within the manuscript. Also, colours and stains should be explained enough to allow readers unfamiliar with the stain to interpret the image. Finally, ensure that the annotations (in addition to the figures) are accessible to colour-blind readers.
  7. Prepare figure legends. Important information needed to interpret the images should be present in the legend. This includes species and tissue type/object shown in the figure, explanations of all labels, annotations and colours, and markings/legend entries denoting insets. Moreover, relevant details not marked in the figure should be explained in the legend.

What I like about this preprint

I think this work is extremely useful as a very complete guideline to all researchers in various areas beyond the three chosen in this study. I think indeed, following these recommendations carefully will improve a lot the readability and reproducibility of imaging-based work. In my experience, imaging-based labs are familiar with the vast majority of these recommendations. I think having these recommendations is a good baseline for all labs (including those whose expertise is not imaging) to standardize figure generation when it comes to inclusion of images. Moreover, the work touches on the rationale for each of the recommendations they give.

References

  1. Jambor et al, Creating clear and informative image-based figures for scientific publications, bioRxiv, 2020.

 

 

 

doi: https://doi.org/10.1242/prelights.26207

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Author's response

Tracey Weissgerber shared

Open questions 

1.I found your work extremely useful. What can be done, based on the recommendations you provide here, to make journals adopt these, as guidelines for publication? This would make it a requirement to satisfy these basic points for eg. microscopy-based images. Perhaps this could be a guide also for reviewers so that this is corrected at this stage of the publication process?

Journal policies are important to signal expectations, however the available evidence suggests that policy changes alone are not very effective in improving the quality of reporting. Journal policies asking authors to report Research Resource Identifiers (RRIDs) for key biological resources, for example, led to a 1% improvement in reporting (doi: 10.12688/f1000research.6555.2, Table 2). A study of articles submitted to Nature Publishing Group journals revealed that mandating authors to complete a checklist upon revision led to a 16% improvement in reporting of the Landis 4 criteria (blinding, randomization, sample size calculations, and exclusions) (doi: 10.1007/s11192-016-1964-8).

Editors seeking to introduce policy changes need a clear plan for how the journal will implement the new policies. This is especially important when policies seek to change standard practice. Reviewers are unlikely to be aware of the new policy; even if they are aware, they may be hesitant to question widely accepted practices. An alternate approach is to train journal staff editors or staff to check for common problems. This adds an additional burden for volunteer editors and may not be feasible on a large scale. Automated screening tools that identify common problems could theoretically screen preprints or submitted manuscripts on a large scale. Tools could potentially be developed to screen for some of the problems identified in this manuscript, although other problems that we identified may be too complex for automated tools to detect. The Automated Screening Working Group is using a collection of automated tools to screen COVID-19 preprints posted on bioRxiv and medRxix (https://scicrunch.org/ASWG/about/COVIDPreprint) for factors such as blinding, randomization, reporting of sex/gender, author acknowledged limitations, and the use of bar graphs to present continuous data. Reports are posted publically using hypothes.is and tweeted out via @SciScoreReports. Approximately 10,000 preprints have been screened so far, illustrating that this approach would be feasible if screening tools were available.

2.How can this information reach a wider audience? For instance, could/should this be taught already at Universities training future scientists (just as writing, basic bioinformatics, and statistics are subjects commonly taught)?

Actions that readers can to take to disseminate this information include learning to identify and fix the problems described in the preprint, adopting better practices in their own research, an encouraging their colleagues to do the same. This might include commenting on ways to improve image-based figures when you review papers or organizing a training session for your lab or department. Scientists can also talk to editors about changing journal policies and procedures to improve the quality of images published in their journals.

We strongly encourage training about good visualization practices, including techniques for creating clear and informative image-based figures. Training would be beneficial for undergraduate students, graduate students, postdocs, PIs, technicians and clinician scientists. These skills may also be beneficial for high school students. We’ve included teaching slides that instructors can use in the OSF repository. Instructors should also plan activities that allow students to practice what they’ve learned. This might include an “Improve this figure” session, where instructors present students with figures from papers in their field and students discuss strategies for improving the visualizations. Instructors might also ask students to design a figure for their own research, or to identify and fix problems with an image-based figure in a publication from their lab.

3.Following on the question before, how would you implement these recommendations as a requirement for image repositories (albeit they are not as common as they should probably be)?

Image repositories are relatively new, offering a valuable opportunity to experiment with policies and procedures designed to increase the quality and value of deposited images and meta-data. Repositories need to reach consensus about essential things that scientists must report, vs. important things that authors should be encouraged to report. An important first step may be to determine which factors are essential to evaluate the quality of the data, assess the risk of bias in the experimental design, and facilitate re-use of images. Reporting templates, drop down menus that allow authors to select common options, and writing guides may be valuable resources for helping authors to efficiently provide necessary information. Finally, it’s important to note that different requirements may be necessary depending on the image type. Our study focused on factors that were common to many different types of images (photographs, microscopy, electron microscopy, clinical imaging techniques like ultrasound or MRI); hence we did not examine factors relevant to specific types of images.

4.On a slightly different topic, what recommendations would you add for videos included in publications, in addition to the requirements you have discussed in detail for static images?

Our study did not examine videos, so we don’t have data on common practices. Videos can be used to illustrate both methods and results (data). Many of the factors described for static images apply to data videos, while some also apply to methods videos. Examples include ensuring that scale information is visible and using overlay text/annotations to highlight important features that are difficult to see or interpret. Markings or annotations used to highlight features of the video are accessible to colorblind viewers. Timestamps provide visible documentation of when the video was recorded. If the video needs to be slowed down or sped up to allow readers to see changes over time, the adjusted speed should be documented. Eliminating background clutter when filming reduces distractions for the viewer. Camera angles and distance are also very important – viewers should be able to see important features clearly. “Legends” and meta-data explaining what is shown help viewers to interpret the video, as do auditory or overlay text explanations included in the video. If the video includes sound, providing closed captioning for hearing impaired viewers improves accessibility.

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