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UID:submissions.supercomputing.org_SC21_sess342_misc277@linklings.com
SUMMARY:WORKS21:  Invited Talk – FAIR Computational Workflows
DESCRIPTION:Workshop\n\nWORKS21:  Invited Talk – FAIR Computational Workfl
 ows\n\nGoble\n\nThe FAIR principles (Findable, Accessible, Interoperable, 
 Reusable) have laid a foundation for sharing and publishing digital assets
 , starting with data and now extending to all digital objects including so
 ftware. The use of computational workflows has accelerated in the past few
  years driven by the need for repetitive and scalable data processing, acc
 ess to and exchange of processing know-how, and the desire for more reprod
 ucible (or at least transparent) and quality assured processing methods. C
 OVID-19 pandemic has highlighted the value of workflows. Over 290 workflow
  systems are currently available, although a much smaller number are widel
 y adopted. As first class, publishable research objects, it seems natural 
 to apply FAIR principles to workflows. The FAIR data principles themselves
  originate from a desire to support automated data processing, by emphasiz
 ing machine accessibility of data and metadata. As workflows have a dual r
 ole as software and explicit method description, their FAIR properties dra
 w from both data and software principles for descriptive metadata, softwar
 e metrics, and versioning. However, workflows create unique challenges suc
 h as representing a complex lifecycle from specification to execution via 
 a workflow system, through to the data created at the completion of the wo
 rkflow. As workflows are chiefly concerned with the processing and creatio
 n of data they have an important role to play in ensuring and supporting d
 ata FAIRification.\n\nThe work on defining and improving the FAIRness of w
 orkflows has already started. A whole ecosystem of tools, guidelines and b
 est practices are under development to reduce the time needed to adapt, re
 use and extend existing scientific workflows. For example, a fundamental t
 enet of FAIR is the universal availability of machine processable metadata
 . The European EOSC-Life Cluster has developed a metadata framework for FA
 IR workflows based on schema.org, RO-Crate and Common Workflow Language (C
 WL), and uses the GA4GH TRS API for a standardised communication protocol 
 to support Accessibility. It has developed and runs the WorkflowHub regist
 ry which uses both the framework and the protocol to support workflow Find
 ability. EOSC-Life have made great efforts to on-board community workflow 
 platforms such as Galaxy, snakemake, nextflow and CWL to carry and use FAI
 R metadata for discovery and reuse. As FAIR software needs to be usable an
 d not just reusable, EOSC-Life has also developed services for, e.g. workf
 low testing (LifeMonitor), execution and benchmarking.\n\nThe Interoperabi
 lity principle is the hardest to unpack for both data and software. For wo
 rkflows, interoperability follows two threads: (i) supporting workflow sys
 tem interoperability through workflow descriptions independent of the unde
 rlying system (e.g. CWL and WDL) and (ii) workflow component composability
 . Workflows are ideally composed of modular building blocks and these and 
 the workflows themselves are expected to be reused, refactored, recycled a
 nd remixed. Thus, FAIR applies "all the way down": at the specification an
 d execution level, and for the whole workflow and each of its components. 
 Composability also relates to reuse – that is, adapting, a workflow or its
  component “can be understood, modified, built upon or incorporated into o
 ther workflow”. Reuse challenges also include being able to capture and th
 en move workflow components, dependencies, and application environments in
  such a way as not to affect the resulting execution of the workflow. Inte
 roperability and Reusability present important obligations on software dev
 elopers to ensure that tools and datasets are workflow ready data with cle
 an I/O programmatic interfaces, no usage restrictions, use of community da
 ta standards, and that they are simple to install and designed for portabi
 lity. Workflow developers can be both data-FAIR, by using and making ident
 ifiers, licensing data outputs, tracking data provenance and so on, and wo
 rkflow-FAIR by managing versions, providing test data, and sharing librari
 es of composable and reusable workflow “blocks”. Communities are working o
 n reviewing, validating and certifying canonical workflows.\n\nWhile there
  are emerging tools for addressing different aspects of FAIR workflows, ma
 ny challenges remain for describing, annotating, and exposing scientific w
 orkflows so that they can be found, understood and reused by other scienti
 sts. Further work is required to understand use cases for reuse and enable
  reuse in the same or different environments. The FAIR principles for work
 flows need to be community-agreed before metrics can be considered to dete
 rmine whether a workflow is FAIR, whether a workflow repository or registr
 y is FAIR, and whether it is possible to automatically review whether a wo
 rkflow’s dataflow is FAIR. Community activism, perhaps led by the platform
 s and registries coming together in a community group like WorkflowsRI, is
  needed to define principles, policies and best practices for FAIR workflo
 ws and to standardize metadata representation and collection processes. In
  this talk I will present current work on FAIR principles, practices and s
 ervices for computational workflows, using developments in the European EO
 SC-Life Workflow Collaboratory and the Bioexcel Centre of Excellence.\n\nT
 ag: Online Only, Cloud and Distributed Computing, Scientific Computing, Wo
 rkflows\n\nRegistration Category: Workshop Reg Pass
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