A volunteer and collaborative effort to bring information about shared microscopy facilities to the University of Arizona and the community.

Additional Resources

The half-day Introduction to Digital Images in Science workshop is a great way to get started in understanding how to correctly work with scientific image data. If additional questions come up after the workshop, feel free to reach out to the workshop team with your concerns.

To learn more:

University of Arizona resources:

What's in a picture? The temptation of image manipulation - The outstanding Journal of Cell Biology (2004) article co-authored by the journal's Managing Editor, Dr. Michael Rossner, and Editor, Dr. Kenneth Yamada. "Here we present some general guidelines for the proper handling of digital image data and provide some specific examples to illustrate pitfalls and inappropriate practices."

Due Diligence - The American Society for Biochemistry and Molecular Biology has an excellent online series of blog posts that cover topics relevant to correctly submitting publication figures to a journal. To read the series in chronological order, start from the bottom of the list.

The JCB's guidelines to beautiful, high-quality figures - A series of ten blog posts from the Journal of Cell Biology. These guidelines and policies have been adopted by a number of journals and publishers. (Rockefeller University Press)

Data Visualization - Nature Methods has a collection of 35 short articles grouped into several categories related to data visualization and design for the presentation of data. While this series is not as directly relevant to working with digital images, most research publications also include graphical data and you may find this useful.

Dr. John Russ' website - Dr. Russ wrote The Image Processing Handbook and has taught thousands of people about properly working with image data. There are a number of useful PDF downloads on this page related to Photoshop, JPEG, Image Ethics, and how the human visual system perceives the world.

11 ways to avert a data-storage disaster - A 2019 Nature article with the tag line "Hard-drive failures are inevitable, but data loss doesn’t have to be". C|Net also has an excellent article on backups from IT pro David Gerwirtz.

Writing a microscopy Materials & Methods - From a microscopy core at Harvard. Some modifications will be necessary, but this is an excellent start. Please remember to acknowledge the core facility.

Microscopy & Imaging Resources online - Webpages and PDF handouts written and curated by Doug Cromey and hosted on this website. Includes a short essay on digital image ethics (with links to Doug's two published papers on the topic), as well as the printable materials page with useful PDF handouts on microscopy, digital imaging (Photoshop), and some instrument/software specific tutorials. Of particular interest:

iBiology has a collection of very well done YouTube videos on microscopy and image analysis.

Image Analysis software - There are options at the UA for access to commercial image analysis software. If you'd like to do your own analysis, we recommend the FIJI version of ImageJ because this free, open-source software comes with a large number of life sciences plugins pre-installed, FIJI automatically updates itself, and it includes the BioFormats plugins, allowing you to open more than 100 proprietary (vendor specific) image file formats. there are a number of FIJI tutorials available. If you want to build an image analysis "pipeline" (a protocol that is used over and over), you might be interested in options like CellProfiler. Online options for analysis pipelines include CyVerse (NSF-funded national cyber infrastructure initiative, the PI of this grant is from the UA) or APEER (an online image analysis initiative sponsored by Carl Zeiss Inc). For questions and support (especially for ImageJ, CellProfiler or other open-source image analysis software), visit the Scientific Community Image Forum: a discussion forum for scientific image software.

Acquiring data for image analysis - Many types of measurements are fairly simple to perform using image analysis, but if the image data being analyzed has artifacts (recognized or un-noticed) the numbers could be misleading or flat-out wrong. Some basic image analysis principles (FIJI) and a blog post on quantifying microscope images (CellProfiler) are useful guides to collecting good quality data. There are many caveats to the quantitation of fluorescence intensities, to get an idea of the kinds of things that must be well controlled we recommend The 39 Steps: A Cautionary Tale of Quantitative 3-D Fluorescence Microscopy.