Basic Workshop
Advanced Workshop

The shown course models are the two standard courses.
Modular changes and combinations are possible according to your needs

Why education in scientific image handling, processing and analysis is important!

The immense publication volume in life sciences is constantly increasing. New methods including diverse imaging techniques and the further technical innovation and advance e.g. in modern microscopy contribute to faster data acquisition in progressively less time.

Nevertheless, drawing final conclusions from acquired data is still the responsibility of the individual scientists and working groups. In the light of this increasing acquisition speed and amount of data in today’s scientific environment it is indispensable to be aware of background knowledge regarding data processing and comply with scientific standards to obtain non-altered and meaningful data sets as bases of our interpretations. Moreover, every scientist should adhere to the respective scientific ethics and good laboratory practice to sustain the significance and credibility of scientific data in the future.

Most young students in life sciences right at the beginning of their scientific careers often are not fully aware of the possibilities, limits and problems of digitized imaging data. Therefore, it is essential to teach especially young academics in an early stage of their career a comprehensive knowledge about image handling, basic processing and analysis. This should combine an efficient workflow with high scientific quality in the future.

Moreover, one essential part in scientific publication is to present data in form of figures. Therefore, a least biased choice of representative images from an experiment is already the first critical step (besides proper experimental design and image acquisition). Thereafter, images are often subject to editing. The reason/intention for the image editing should already be questioned. Is it applied to improve visibility of features, to direct the observers view to specific regions, to get rid of “ugly background” or dirt from the sample preparation or to underscore points for or against a hypothesis?

Mostly, image editing (as well as image analysis) is done based on good intentions but influenced by natural bias (e.g. our visual system [1]) and often a lack of sound knowledge in image processing techniques. Not seldom, this leads to alterations in the image data which very quickly might be considered as misrepresentation of data. The Office of Research Integrity (ORI) figured out that from all cases they opened in 2007-2008 for investigation against potential scientific misconduct 68% included image data manipulation [2].
Of consideration is the fact that images we take in science ARE precious data and need to be handled as such [3].

In the last years the awareness in this field increased and the number of journals explicitly stating strict image data related guidelines and enforcing compliance is (too) slowly increasing.
Additionally, several whitepapers with a common tenor of suggested guidelines for image processing of scientific images are available [4], [5], [6], [7], [8].

Besides those guidelines the education of young scientists in image processing, analysis, handling of scientific images and publication ethics is essential. BioVoxxel is dedicated to communicate skills in good scientific practice regarding digital images to the scientific community to help preventing unintentional image data alterations.

References

[1] Seeing the Scientific Image

[2] Science journals crack down on image manipulation

[3] Digital Images Are Data: And Should Be Treated as Such

[4] Guidelines for Best Practices in Image Processing

[5] Avoiding Twisted Pixels: Ethical Guidelines for the Appropriate Use and Manipulation of Scientific Digital Images

[6] Digital Image Ethics – University of Arizona (by Douglas Cromey)

[7] What’s in a picture? The temptation of image manipulation

[8] Manipulation and Misconduct in the Handling of Image Data

[9] Seeing the Big Picture – Scientific Image Integrity under Inspection

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