Introduction to course

Introduction to course#

This course shows in practice how to create a scientific image figure. It deliberately focuses on only the most critical items that affect whether a figure is legible and truthful. The condensed checklist below shows the covered items.

For the complete set of recommendations, see the full set of checklists.

In

Fig. 9 Condensed checklist.#

Who this course is for#

This course is for anyone who wants to process scientific images for visualization in image figures (e.g., scientific articles, posters, presentations).

This course assumes no prior knowledge of image processing. It uses the freely accessible, open-source tools Fiji and Inkscape.

This is a hands-on course providing examples that students can implement themselves. The principles and instructions can also be applied to your own images.

Note

The content is created with microscopy images, specifically light microscopy in mind. The core principles are applicable to other scientific image domains.

Course learning objectives#

The overall course objective is to walk through creating one complete image figure from raw data, including the figure legend and the methods used to create and process the images.

After the course, the learner will be able to produce an image figure from an original microscopy image that truthfully and effectively reports the scientific result that the figure represents (see below).

In

Fig. 10 Example of an image figure with key annotations provided, such as a scale bar and content including the shown image colors. An enlarged crop is shown next to the overview to focus the viewer on a key result. The origin of the crop is annotated in the overview. Additional annotations, such as white arrowheads, are used to further guide the viewer.#

Overview of teaching units#

The units follow the image processing workflow step by step: adjusting visibility and formatting in Fiji, preparing the figure in Inkscape, and finally describing the methods used for image processing and analysis.

Unit

Learning objective

Time

1.

Unit 1: Visibility

Load an example image, adjust colors, brightness, and contrast, and create a merged image, in order to make the information in the image clearly visible to a wide audience and to accurately represent the underlying image content.

45 min

2.

Unit 2: Format and annotations

Format an image using cropping and rotation, and add key annotations such as scale bars, to clearly and accurately present scientific results.

25 min

3.

Unit 3: Figure and availability

Create a complete scientific image figure using vector graphics software, adding legible annotations, figure legends, and method descriptions, and make the underlying image data available in a public repository, so that the data and image processing are fully documented and communicated.

15 min

4.

Unit 4: Quantification

Perform a basic quantitative image analysis using an existing tool, document the software, tool versions, and analysis settings used, and identify what needs to be shared (code, settings files, example data) so that custom or existing analyses are transparent and reproducible by others.

10 min

Total time for the course is about 1–2 hours. Each teaching unit is self-contained and can be executed individually. The individual tasks performed in each unit can also be followed using the condensed checklist (see Fig. 9).

For more content on image processing ethics and image interpolation, see: Further information.

Materials#

For this tutorial you are going to need:

The course shown here contains all necessary image data as a download.

Note

We selected Fiji and Inkscape because they are free, open source, and familiar to the authors. The underlying principles can be applied to other tools.

We recommend using the latest versions of these programs.

Further material: Image publication#

Cheatsheets for creating image figures:

Schmied C. and Jambor HK. Effective image visualization for publications – a workflow using open access tools and concepts [version 2]. F1000Research 2021, 9:1373 (doi: 10.12688/f1000research.27140.2)

Images for the examples were published here:

Wolff C. et al. Morphological profiling data resource enables prediction of chemical compound properties. iScience 2025 (doi: 10.1016/j.isci.2025.112445)

Input, processed, and result images can also be downloaded from a Zenodo repository.

Further material: Image acquisition#

This course assumes that well-acquired images are already available. Image acquisition presents its own challenges and care must be taken to acquire images at appropriate resolution and with proper intensity sampling.

For image acquisition to be reproducible, the materials and methods also need to be documented properly, and each image should be supported by all necessary metadata.

For further information on image acquisition, we have listed some key papers here:

  • Brown CM. Fluorescence microscopy–avoiding the pitfalls. J Cell Sci. 2007 May 15;120(Pt 10):1703-5. doi: 10.1242/jcs.03433

  • Pawley J. The 39 steps: a cautionary tale of quantitative 3-D fluorescence microscopy. Biotechniques. 2000 May;28(5):884-6, 888. doi: 10.2144/00285bt01

  • North AJ. Seeing is believing? A beginners’ guide to practical pitfalls in image acquisition. J Cell Biol. 2006 Jan 2;172(1):9-18. doi: 10.1083/jcb.200507103

  • Waters JC. Accuracy and precision in quantitative fluorescence microscopy. J Cell Biol. 2009 Jun 29;185(7):1135-48. doi: 10.1083/jcb.200903097

Information for reporting:

  • Montero Llopis, P., Senft, R.A., Ross-Elliott, T.J. et al. Best practices and tools for reporting reproducible fluorescence microscopy methods. Nat Methods 18, 1463–1476 (2021). https://doi.org/10.1038/s41592-021-01156-w

Further material: Ethical image processing#

This course gives recommendations on how to perform image processing for good scientific image visualization. We do not directly discuss intentional or misleading image manipulation (e.g., fraudulent duplication or reuse of image data). For considerations on ethical image processing, we have created the section: Ethical image processing.

Further reading on this topic:

  • Cromey DW. Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images. Sci Eng Ethics. 2010 Dec;16(4):639-67. doi: 10.1007/s11948-010-9201-y

  • Cromey DW. Digital images are data: and should be treated as such. Methods Mol Biol. 2013;931:1-27. doi: 10.1007/978-1-62703-056-4_1

  • Bik EM, Casadevall A, Fang FC. 2016. The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications. mBio 7. doi: 10.1128/mbio.00809-16

Help#

If you need help, write to the team on image.sc: https://forum.image.sc/tag/quarep.

You can also reach out to the creator of this tutorial: schmiedc