Canonical Workflow for Machine Learning Tasks

There is a huge gap between(1)the state of workflow technology on the one hand and the practices in the many labs working with data driven methods on the other and(2)the awareness of the FAIR principles and the lack of changes in practices during the last 5 years.The CWFR concept has been defined which is meant to combine these two intentions,increasing the use of workflow technology and improving FAIR compliance.In the study described in this paper we indicate how this could be applied to machine learning which is now used by almost all research disciplines with the well-known effects of a huge lack of repeatability and reproducibility.Researchers will only change practices if they can work efficiently and are not loaded with additional tasks.A comprehensive CWFR framework would be an umbrella for all steps that need to be carried out to do machine learning on selected data collections and immediately create a comprehensive and FAIR compliant documentation.The researcher is guided by such a framework and information once entered can easily be shared and reused.The many iterations normally required in machine learning can be dealt with efficiently using CWFR methods.Libraries of components that can be easily orchestrated using FAIR Digital Objects as a common entity to document all actions and to exchange information between steps without the researcher needing to understand anything about PIDs and FDO details is probably the way to increase efficiency in repeating research workflows.As the Galaxy project indicates,the availability of supporting tools will be important to let researchers use these methods.Other as the Galaxy framework suggests,however,it would be necessary to include allsteps necessary for doing amachine learning task including those that require human interaction andtodocument all phases with the help of structured FDOs.

Canonical Workflow for Experimental Research

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.