Jambor, Helena

Tran, Tuan Minh

English

The Australian National University

Reproducible presentation of imagesAuthors Helena Jambor (0000-0003-3397-1842), TU Dresden/Germany; Tuan Minh Tran, NTU, SingaporeWhy is reproducibility important for image data?Digital images are just data and therefore easily manipulated.All digital images are just numbers and thus easy to changes. At increasing frequency, images are submitted for publications that have unintentional and intentional mistakes. It is currently estimated that 4% of published papers have problematic images (Bik, 2016). Images are: photographs, micrographs, EM images, movies. What are image manipulations and how can I avoid them?Easy steps to avoid image manipulations:1. Keep raw original and only adjust copies.2. Compare only images that are acquired and processed identically.3. Don’t use lossy compression to save images.4. Report method of sample preparation, image acquisition, and image processing.5. Acceptable manipulations: • Linear adjustment of brightness/contrast  for entire image. • Cropping to a region of interest (ROI)6. Be careful: software filters that improve image quality and nonlinear adjustments, e.g. gamma settings7. Non-acceptable manipulation: • Specific features in images are enhanced, obscured, moved, removed, or introduced.• Combinations of multiple images into one!✔ ✗.png .tiff .ome .pdf .jpg .pptWhy do I need to bother about image legibility?Images must be universally understandable to all audiences, now and in future. What do I need to consider when doing quantitative imaging? Images that are analyzed quantitatively need special care during the acquisition process. Image reproducibility means that all audiences can understand and potentially replicate the image. Audiences include scientists around the globe, across disciplines, the public, and future readers. How can I prepare legible images? Steps towards legible images:1. Choose magnificationThe magnification of images must fit your research question. All parts of the images that answer this question need to be visible. Focus on tissue                  Focus on cells        Focus on subcellular 2. Decide whether to use colorColor can convey information, focus attention, or help distinguish. If none of these apply: do not use color. Also, greyscale has much better contrast than multiple colors. Choose wisely! 3. Make colors accessible to everybodySome people are color-blind, some people can’t distinguish similar shades. 4. Add labels for key features if necessary5. Add title and scale informationWhich tools are available for image processing?Image repositories: clouds and public databases.Images in papers are compressed and can’t be re-analyzed quantitatively. To permit a later reuse, e.g. with improved software, store the raw images in public databases. (see also Ellenberg et al 2018)Store also information on: sample type & preparation method, basic annotation, acquisition method.Deposit your imageshttps://zenodo.org/ free online repositories that provides DOI. Upload limit 50GB per dataset. Supported by CERN, EU https://figshare.com/ free online repository to store & share data, provides DOI, upload limit: 5GB per file.Further readingCromey, D.W. 2010. Sci Eng Ethics. Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images, doi.org/10.1007/s11948-010-9201-yRossner M., Yamada K. 2004. JCB. What’s in a picture? the temptation of image manipulation. 10.1083/jcb.200406019Rossner M. 2006. The scientist. How to guard against image fraud. Ellenberg, J et al. 2018. Nature methods. A call for public archives for biological image data. doi.org/10.1038/s41592-018-0195-8Bik et al. 2019. mBIO. The prevalence of inappropriate image duplication in biomedical research publications.Training: UAB Center for Ethics and Values in the Scienceshttps://ori.hhs.gov/education/products/RIandImages/default.htmlJournal guidelines: http://www.aje.com/en/arc/avoiding-image-fraud-7-rules-editing-images/Training also regularly offered by neubias (network of european bioimage analysts)Normal color vision           Color blind vision (Deuteranopia)Not color-blind saveColor-blind saveImage processing must be transparent and interpretable by a larger community. Efforts to image with open microscopy environment (OME), and process images with open processing algorithms is key, e.g. FIJI.Only use dyes where signal is linearly correlated with material. Many dies are not linear and can’t be used for quantification. Only with loss-less file fomats. Reversion from compressed to rich format is not possible, see en.wikipedia.org/wiki/Image_file_formatsBe careful with exposure time/filter/lamps No change during an experiment and use the dynamic range of your camera when imaging. See: Claire Brown doi: 10.1242/jcs.022079Magnification, binning Pick lens + image resolution you need to answer biological question. Too long imaging can cause bleaching/toxicity. eLIFE Ambassadors 2019 CC-BY 4.0 International license.

Reproducible presentation of images

https://openresearch-repository.anu.edu.au/bitstream/1885/155723/1/Image_reproducibility_Jambor v1.pdf

Reproducible presentation of images

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