1. FAIR Computational Workflows
Professor Carole Goble
The University of Manchester UK
EU Research Infrastructures: ELIXIR, IBISBA, EOSC-Life
Centre of Excellence: BioExcel
Software Sustainability Institute UK
FAIRDOM Consortium
carole.goble@manchester.ac.uk
GCB 2021, 7th September 2021
https://gcb2021.de/

2. P44, Implementation of a scalable
SARS-CoV-2 NGS data processing,
variant calling and correlation analysis
pipeline with snakemake
[Norma J. Wendel]
GCB2021
Many examples of
multi-step processing and analytics
(and all those ML pipelines!)
Systematic linking together of
multiple tools and software
packages using computational
infrastructure
Some using
Computational Workflow Systems
Computational Workflows for Data intensive Bioscience
prepare, analyze, and share increasing volumes of complex data
Bioimage analysis with deep learning for
everyone: Visual programming in JIPipe
[Ruman Gerst, Jan-Philipp Praetorius]
APEER: A cloud-based digital microscopy
platform to create image processing
workflows.
[Bernhard Fichti]

3. Computational Workflows for Data intensive Bioscience
prepare, analyze, and share increasing volumes of complex data
CryoEM Image Analysis
Metagenomic Pipelines
Drug Discovery
Protein Ligand MD
Simulation
Genome Annotation
High Throughput Sequencing
[Fabrice Allain
JOBIM2021]
[Romain Dallet
JOBIM2021]
[Adam Hospital]
[Rob Finn]
[Carlos Oscar Sorzano Sanchez]

4. 20 years+
Computational workflows
decades in the making…finally coming of age….
doi: 10.1093/gigascience/giaa140
Nature 573, 149-150 (2019)
https://doi.org/10.1038/d41586-019-02619-z

5. What are Data intensive Computational Workflows?
Systematic linking together multiple tools and software packages
inputs
outputs
tools, CLI,
containers,
workflows
Scale up
Access to computational infrastructure
and datasets, tool interoperability,
processing portability and
optimisation, data wrangling.
Specification
description
Software
Execution
WfMS
Engine
Workflow
Scale out
Flexible workflow composition to
construct & run executable control
and data flows using
heterogeneous software packages,
codes, tools, other workflows made
by other people.

6. SARS-CoV-2 allelic-variant surveillance
Automated monitoring of structured data
from the European COVID-19 Data Portal and
national SAR-CoV-2 sequencing datasets.
Scalable - access to a global distributed
compute network
• Improved data quality
• Uniformly analysed data for downstream
analysis & visualisation
• Submission of data to public archives
• https://covid19.galaxyproject.org
https://elixir-europe.org/news/covid-19-variants-galaxy
https://doi.org/10.1101/2021.03.25.437046
Suite of
workflows

7. Distributed analysis , Pulsar network
Managed online hosted Workflow as a Service Platform
Direct use by end users - 32K users
Experts build workflows that others can use with their own data
Researchers build and reuse workflows that are shared
End users also use it to access and interact with a tool
Workflow and Tool histories and reporting
Björn Grüning
U of Freiburg

8. Those workflows in the WorkflowHub Registry
curated collection Find, publish and cite workflows and
collections. Reuse, recycle, repurpose.

9. Sharing Accelerates Science
9
A digital space for
EMERGEN, the
French plan for SARS-
CoV-2 genomic
surveillance and
research
Adapting and Reusing the
ELIXIR Galaxy Workflows
Tried and tested
transparent methods.
Jacques van Helden

10. Inter-twingled Workflow System Landscape
Scripting
environments
Interactive Electronic
Research Notebooks
Repositories Registries
Inter-twingling
Mix and Matching
Interactive &
exploratory
analysis
Production, automated,
workflow-integrated
software
Workflow
Management
Systems & execution
platforms
https://s.apache.org/existing-workflow-systems
298 Systems
General and Specialised

11. https://snakemake.github.io/
Workflows are rules:
Graph of jobs for automatic parallelisation,
DIY package & containerisation
installation, auto-documentation
From frameworks to web based analysis platforms, hybrid cloud deployment
Communities tend to cluster round a few systems.
Take up of a WfMS typically depends on the “plugged-in” availability of data type
specific codes, skills level of the workflow developers, and popularity.
Online portals users build and reuse
workflows around publicly available or
user-uploaded data and pre-wrapped,
pre-installed tools.

12. Abstraction property of Computational Workflow Systems
Separation of the workflow specification from its execution & tools
Encoded method less dependent on
implementations
• Sustained as digital environment evolves
- extend investments
• Map to diverse platforms - exploit latest
advances in platforms
• Reused and re-purposed - lower
experimental design cost & share
methods across discipline boundaries

13. Ten Handy Properties of Computational Workflow Systems
Abstraction & Composition
Using the best codes written by 3rd parties
Handle heterogeneity
Shield complexity & incompatibility
Sharable reusable, re-mixable methods
Automation
Repetitive reproducible pipelines
Simulation sweeps
Manage data and control flow
Optimised monitoring & recovery
Automated deployment
Scalability & Infrastructure Access
Accessing infrastructures, datasets and tools
Optimised computation and data handling
Parallelisation
Secure sensitive data access & management
Interoperating datasets & permission handling
Reporting & Accreditation
Portability
Sharing & Adaptability
Provenance logging & data lineage
Auto-documentation
Result comparison
Dependency handling
Containerisation & packaging
Moving between on premise & cloud
Shared method, publishable know-how
BYOD / parameters
Different implementations
Changes in execution infrastructure

14. WORKFLOW
APPLICATION USER
Yes it’s work, Labour saving -> Labour shifting
Production platforms & pipelines, Collective Labour
TOOL
DEVELOPER
WORKFLOW
USER
SYS ADMIN WORKFLOW
DEVELOPER
& CUSTODIAN
COMPUTATIONAL
USER
Workflow System as a Platform Workflow System as a Service
Labour
Reach
need
infrastructure
& services
need tools to be
wrapped &
maintained
need workflows to be
developed, tested,
run & maintained
need to find and understand
workflows, with explanations to
use properly and safely.

15. The long tail
Common Workflow Language workflows
CWL Viewer, https://view.commonwl.org
Github and git repositories of
~2500 unique workflows (~26,700 including versions)
Top 10 repos
70% of workflows
Top 20 repos
80% of workflows

16. https://lifescience-ri.eu/
An open collaborative
space for digital
biology in Europe
Environment for hosting
& processing data

17. Workflows are an entry point to the
tools and datasets
functions for production quality
FAIR data processing
access to secure data processing
Figure Credit: Romain Dallet
Galaxy Genome Annotation (GGA) environment in the cloud
A data and method commons
A portable environment of interoperable tools
RIs publish data, methods & services for
management, storage and reuse

18. The EOSC-Life Workflow Collaboratory
People, workflows, services and standards for FAIR Workflows.

19. Reflection: Computational Workflows
Reproducibility
Replication
Regulation
Labour saving
Productivity
Reliability
Knowledge sharing
Adaption
Scholarly Objects
Democratisation
of computational
analysis & methods
Framings for the EOSC-Life FAIR Workflow Collaboratory

20. The EOSC-Life FAIR Workflow Collaboratory
FAIR
Workflows

21. FAIR Principles
Findable, Accessible, Interoperable, Reusable
A set of guiding principles to enhance the value
of all digital resources and their reuse by people
and by machines
Assumption – data (& software) are first class
objects that will be shared
Accelerate science - find and reuse and interlink
data (and tools, workflows, machine
learning….)
A community journey to common guidelines
Consumers and producers all benefit.

22. The FAIR Principles look
RDA FAIR Data Maturity Model. Specification and Guidelines https://zenodo.org/record/3909563#.YORYkUzTX19
https://www.go-fair.org/fair-principles/
Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and
stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18

23. tl;dr FAIR Principles for Data https://www.go-fair.org/fair-principles/
Enhance automation
Persistent human readable and machine-actionable metadata
Linked metadata and community standards
Persistent identifiers
Clear licensing and access rules
Protocols for machine accessibility
Register / Index
Assumption: operate in an ecosystem at scale and in legacy settings.
Fairly AI Ready

24. FAIR for Software
Software is a digital object but research software is not (just) data
https://www.rd-alliance.org/groups/fair-4-researchsoftware-fair4rs-wg
FAIR for Research Software (FAIR4RS) working group
Lamprecht et al., 2019
FAIR4RS First Draft of FAIR4RS principles
CodeMeta
https://github.com/codemeta/codemeta/
Katz, et al PATTERNS 2, 2021

25. FAIR Principles for Workflows
Hybrid Processual Digital Objects
Method “Data” Objects
Workflows as
FAIR Software
FAIR+R and FAIR++
The principles revised
Workflows as
FAIR Digital Objects
Data-like method objects
The principles adapted
Workflows as
FAIR Data Instruments
FAIRification of the dataflow
The data principles supported
C. Goble, S. Cohen-Boulakia, S.
Soiland-Reyes, D. Garijo, Y. Gil, M.R.
Crusoe, K. Peters & D. Schober. FAIR
computational workflows. Data
Intelligence 2(2020), 108–121.
doi: 10.1162/dint_a_000
Workflow Objects
Software Objects

26. FAIR Principles for Workflows
Image credit: BioExcel Centre of Excellence
Composition & Portability
different
components,
codes,
languages,
third parties

27. FAIR Interoperability and Reusability = Composability
R: Reusable - can be understood, modified,
built upon or incorporated into other software
I: Software interoperates with other software through
community standard APIs and community standard meta(data)
Software include qualified references to other objects
Richly described
Well documented
Licensed
Sample input parameters and test data
Checker workflows
Track versions
Programmatic access to (meta)data
Libraries of canonical workflow blocks
Make tools workflow-ready
Wrap tools
FAIR4RS Proposed Principles for FAIR Software
Design for FAIR Data
Design for Reuse
Community Review
Community Curation
Certification
Best Practice
Licence combinations
Access permissions
Local -> Global identifiers

28. Findable & Accessable
register workflows with assigned PID + metadata in a searchable resource.
https://workflowhub.eu
Publishing Services
Journals
Digital Objects of Scholarship
published, cited, exchanged, reviewed, validated & reused in
new and different ways
• Versioned identifiers
• DOI assignment (https://doi.org/10.48546/workflowhub.workflow.29.2)
• Collections, Canonical workflow libraries
scripts
Repos
Containers Deploys
Tools
Agnostic and generous with many WfMSs
with different degrees of support
• Workflows in native places
• Metadata standards framework, handles
associated objects and links between objects.
• Perpetual development by an open community

29. licensing
authors
& credit
analytics
access
search
versions & status
other
workflows
Biggest challenge?
Metadata of course!
Work with WfMS to auto-extract
metadata + provide metadata services

30. More than just a list
3
Spaces, Teams, People
Linking up providers and users
Building visibility & reputation
Reciprocity to close the
“Find – Get– Use – Credit” loop
Citations
Knowledge Graphs linking out to
OpenAIRE, DataCite etc

31. Customised FAIRDOM-SEEK
https://fair-dom.org, https://fairdom-seek.org, https://fairdomhub.org
Digital asset management platform for Project Hubs
organising, cataloguing, sharing and publishing multiple kinds of research
objects held in multiple repositories for multi-partner projects.
Wolfgang
Müller
Martin
Golebiewski
Ulrike
Wittig
Olga
Krebs
Xiaoming
Hu

32. FAIR Workflow are FAIR Software
lifecycle support for living objects
Indicators of Status
Workflow
monitoring
Register versions
Version PIDs
Support Github actions
Track authors and contributions
Incremental metadata and
supplementary materials
Track & lift out sub-
workflows
R1.2: (Meta)data and software are associated
with detailed provenance

33. Tool Registry Service API
Accessible
metadata and workflows retrievable by their PID using a standardized
communication protocol
GitHub page: https://github.com/ga4gh/tool-registry-service-schemas

34. FAIR Metadata for Machines and Humans
WfMS neutral canonical descriptions
https://www.commonwl.org
Canonical description of the workflow
Linked to containerised tools
• Aid collaboration & knowledge transfer
• Standardise expression of workflow
• Describe engine neutral portable, reusable workflows
• Reduce vendor / project lock-in
• Enable workflow comparisons
• “Abstract” CWL

35. Design by canonical, modularised workflow blocks
Build a library of tested and validated CWL blocks
CWL:
• Canonical descriptions
• Recycle descriptions and sub-workflows
• Platform independent pipeline exchange and comparison
Rob Finn
Folker Meyer
AWE
MEGAHIT
Assembly
pipeline
[with thanks to Rob Finn]

36. Extensible Metadata Framework
that caters for all those processual FAIR criteria
Common metadata
about the workflow,
tools & parameters
Canonical workflow
description of the
steps of the workflow
Type the input and outputs
of the steps
Run Provenance / Histories / Tests
Format for packaging a
workflow, its metadata and
companion objects (links to
containers, data etc) for
exchange, archiving,
reporting, citing.
FAIR Digital Object
All Open Communities

37. Bioschemas lightweight metadata
Extensible and Linked metadata in service of the Life Science Community
Open community reusing industry de facto standard
Computation workflow profile
Formal
parameter
profile
https://bioschemas.org
Opinionated use of schema.org, the web
resource mark-up used by search engines,
knowledge graphs and increasingly science
as a whole.
Computational tool

38. RO-Crate Digital Objects
Lightweight way of packaging everything together regardless where or what it is
https://www.researchobject.org/ro-crate/
Format for packaging up scattered resources and self
describing that package and its parts
- integrated view + context
- metadata and PIDs reference digital and real things
- datasets, workflows, services, software & people, places etc.
Web-native, off the
shelf - machine and
human readable,
search engine &
developer friendly.
Infrastructure
independent &
self-describing
PIDs, JSON-LD,
Schema.org,
archive formats
Extensible and open-
ended to cope with
diversity and legacy
“Duck typing”
using profiles +
added schema.org
and domain
ontologies

39. RO-Crate Profile Variants
https://www.researchobject.org/ro-crate/profiles
Galaxy-
Workflow-
RO-Crate
Workflow-RO-Crate
Workflow-
Testing-
RO-Crate
Workflow-
Run-
RO-Crate
BioComputeObject
-RO-Crate
IEEE P2791-2020

40. BioComputeObject - Regulation
why and how to use a workflow IEEE P2791-2020
robust, safe exchange & reuse of HTS
computational analytical workflows
http://biocomputeobject.org
Alterovitz, Dean II, Goble, Crusoe, Soiland-Reyes et al “Enabling Precision Medicine via standard
communication of NGS provenance, analysis, and results” PLOS Biology 2018m,
https://doi.org/10.1371/journal.pbio.3000099
https://biocompute-objects.github.io/bco-ro-crate/
“Sidecar” third party metadata files
inside the RO-Crate
FAIR has to operate in a legacy
ecosystem
format

41. Reproduciblity – Repeatability
Provenance & Preservation
Workflow-Run-RO-Crate
Some heavy lifting …
when is it FAIR enough?
R1.2: (Meta)data and software are associated with
detailed provenance - not just the workflow but the
run record associated with the data it produced ….

42. FAIR Digital Objects
RO-Crate, a step towards FAIR Digital Object Middleware
“Each FAIR digital object type has
its own metadata requirements,
and may have its own repositories
and registries”
FAIR Digital Objects for Science: From Data Pieces to Actionable
Knowledge Units: https://doi.org/10.3390/publications8020021
https://fairdo.org
https://fairdo.org/wg/fdo-cwfr/

43. Our Workflow Metadata Underware is ready!
Archiving
General
Executing
Testing & Monitoring
WfMS
A2. metadata are accessible, even when the workflow is no longer
available
Metadata preservation beyond any one service in RO-Crate archive,
republished in a long-term archive
R1. workflows are richly described with a plurality of accurate and
relevant attributes, R1.3 domain-relevant community standards
Automating metadata by on-boarding WfMS and FAIR services.
Metadata so that a workflow is read-reproducible as a method description
R. The software is usable (it can be executed) and reusable (it can be
understood, modified, built upon, or incorporated into other software).
Services and standards containers, testing & monitoring, execution,
GA4GH TRS API

44. Reflection: FAIR takes a village
its a JOINT responsibility and opportunity!
In order for data to be
FAIR, you need services
that enable FAIR
Be a good plug-in tool and data citizen
enable programmatic access to datasets
make clean tool interface
avoid usage restrictions
use open community data standards and formats
simplify installation
code for portability, parallelisation & reproducibility
manage versions
register! document!
Be a good workflow maker......and user
use and make FAIR identifiers for data
license data outputs
use open community data standards and formats
validate parameters
use a WfMS that tracks data provenance
consider secure data processing
manage versions
design tests and test data
credit tool and sub-workflow makers
choose FAIR data services
register! document! build libraries!
use well documented FAIR
enabling and FAIR workflows
credit the makers!

45. Reflection: FAIR takes a village
its a JOINT responsibility and FAIR ≠ FREE
Advocate standards & practice
Sustain and manage infrastructure
Credit and incentives
Maturity models & metrics
Certification and canonical libraries
In order for data to be
FAIR, you need services
that enable FAIR
Training,
Stewardship &
Sustainability
Workflows are an entry point to the tools
and datasets of EOSC-Life and functions
for FAIR data.

46. Summary: FAIR Computational Workflows
EOSC-Life Workflow Collaboratory
Production workhorses, transparent, reproducible
processing & democratised access to data, infrastructure and
complex processing.
Hybrid Digital Objects of scholarship that should be FAIR
themselves and support FAIR Data.
FAIR assumed to operate in an ecosystem at scale and in
legacy settings.
FAIR takes a village where everyone shoulders responsibility
not just data and service providers

47. Acknowledgements
The WorkflowHub Club, Bioschemas Community, RO-Crate
Community, CWL Community, Galaxy Europe, EOSC-Life
and ELIXIR Tools Platform.
Special Thanks
Stian Soiland-Reyes (U Manchester / U Amsterdam)
Paul Brack, Stuart Owen, Finn Bacall, Alan Williams (U Manchester)
Björn Grüning (U Freiburg)
Frederik Coppens (VIB)
Sarah Jones (GEANT)
Herve Menager (Pasteur Institute)
Sarah Cohen-Boulakia (U Paris Sacly)
Dan Katz (U Illinois Urbana-Champaign)
Simone Leo (CRS4)
Laura Rodriguez-Navas (BSC)
José Mª Fernández (BSC)
EOSC-Life https://www.eosc-life.eu/
ELIXIR http://elixir-europe.org
RO-Crate https://www.researchobject.org/ro-crate/
WorkflowHub https://workflowhub.eu/ and workflowhub.org
Galaxy Europe https://galaxyproject.eu/
Bioschemas https://bioschemas.org
Common Workflow Language https://www.commonwl.org/
FAIRDOM https://fair-dom.org
WorkflowsRI https://workflowsri.org/

48. Extras
50