Canonical Workflow for Experimental Research.

Abstract The overall expectation of introducing Canonical Workflow for Experimental Research and FAIR digital objects (FDOs) can be summarised as reducing the gap between workflow technology and research practices to make experimental work more efficient and improve FAIRness without adding administrative load on the researchers. In this document, we will describe, with the help of an example, how CWFR could work in detail and improve research procedures. We have chosen the example of “experiments with human subjects” which stretches from planning an experiment to storing the collected data in a repository. While we focus on experiments with human subjects, we are convinced that CWFR can be applied to many other data generation processes based on experiments. The main challenge is to identify repeating patterns in existing research practices that can be abstracted to create CWFR. In this document, we will include detailed examples from different disciplines to demonstrate that CWFR can be implemented without violating specific disciplinary or methodological requirements. We do not claim to be comprehensive in all aspects, since these examples are meant to prove the concept of CWFR.