Canonical Workflows to Make Data FAIR

Abstract

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, 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.

The Specimen Data Refinery: A Canonical Workflow Framework and FAIR Digital Object Approach to Speeding up Digital Mobilisation of Natural History Collections

“…Workflow technologies from the ELIXIR Research Infrastructure [19], including Galaxy [20], Common Workflow Language [21], Research Object Crates (RO-Crates) [22,23] and WorkflowHub [24], and selected tools are integrated in a cloud-based workflow platform for natural history specimens-the 'Specimen Data Refinery' [1] that will become one of the main services to be offered by the planned DiSSCo research infrastructure [5]. The tools themselves, implemented with findable, accessible, interoperable, and reusable (FAIR) characteristics [25] are packaged into canonical workflow component libraries [26], rendering them reusable, and interoperable with one another. FAIR Digital Objects are adopted as the common input/output pattern, fully compatible with digital objects at the core of DiSSCo data management [27].…”

“…New components can be created by wrapping existing programs, with in-built dependency management and automated conversion to executable containers. As such, Galaxy and CWL offer possibilities for a rich canonical workflow component landscape with a workflow management regime that can be both easily FAIR compliant and efficient internally [26]. The WorkflowHub, which facilitates CWL and enables workflows to be registered, shared and published, is mutually coupled with Galaxy so that workflows can be discovered in the Hub and immediately executed in a public-use Galaxy instance.…”

“…Galaxy/CWL environments offer the possibility to integrate generic digital object methods [43,44,45] for the interactions between workflow components, thus making them able to meet the need and ease the burden of compiling FAIR compliant data throughout the research lifecycle [26].…”

“…Supporting 'FAIRness' internally and acting as glue between the steps of canonical workflows, FDOs record and can represent the state of a workflow, its inputs and outputs, and the component steps performed in a comprehensive manner [26]. Each FDO is anchored by a globally unique and resolvable, persistent identifier (PID) (such as a DOI ® , for example) that clearly refers to one digital entity.…”

“…They complement one another to support implementation of the FAIR principles, especially the interoperable and reusable principles by making workflows self-documenting. This renders automated whole processes (or fragments thereof) for digitizing and extending natural history specimens' data as FAIR without adding additional load to the researchers that stand to benefit most from that [26]. Each FDO type originates from different Research Infrastructures (ELIXIR, DiSSCo) with different implementation frameworks.…”