Canonical Workflows to Make Data FAlR

The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices,yet the incentives for researchers to change their practices are presently weak.In addition,data-driven science has been slow to embrace workflow technology despite clear evidence of recurring practices.To overcome these challenges,the Canonical Workflow Frameworks for Research(CWFR)initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments thereof.This standardised approach,with FAIR Digital Objects as anchors,will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from it.This paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year or so,highlights several projects that hold promise for the CWFR approaches,including Galaxy,Jupyter Notebook,and RO Crate,and concludes with an assessment of the state of the field and the challenges ahead.

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.