Applying FAIR principles to data is becoming more common but applying them to training materials is lagging somewhat. It has been proposed [ 1] that these principles should be applied to training resources and not just research data. Basics of image analysis are covered well by the community with numerous resources available (though not always easily accessible) but higher-level resources, especially in the AI/ML or coding space, are rarer.
With this in mind we took existing resources [ 2, 3, 4, 5] that have been developed as single large PDF documents and converted them to online resources that conformed to the FAIR principles. One was a 400-plus page training manual (and its associated training image sets) on using the Fiji distribution of ImageJ, which has evolved to the behemoth that it is today. The other being a more recent, and smaller, training manual on using open-source tools for AI and ML image analysis.
These resources were already available to download in PDF format, achieving the accessibility criteria of FAIR, but were not in a format that could be edited or accessed easily. To date, the documents have been downloaded 1,000s of times, and used as hard copy print outs for more than 40 face-to-face workshops. As the original source of these manuals are word documents held within a local storage system, editing and adding new content in a collaborative manner cannot be easily done. This can be especially difficult for international collaboration.
Using github.io as a platform we translated the existing documentation across to a fully online resource that met the FAIR principles of being findable, accessible, interoperable and reusable. The final version is now available for use by, and contribution from, users across the globe.