The document discusses the importance of fair data and model management in scientific research, particularly in systems and synthetic biology, emphasizing the need for effective data handling, documentation, and reproducibility. It outlines the FAIR principles (Findable, Accessible, Interoperable, Reusable) and presents strategies for automating data management processes, ensuring proper documentation, and maintaining collaboration across disciplines. Additionally, it highlights community efforts and resources aimed at improving data stewardship and the sharing of research outputs.

1. Being FAIR:
FAIR data and model
management
Professor Carole Goble, carole.goble@manchester.ac.uk
The University of Manchester, UK
The FAIRDOM Association Coordinator
ELIXIR-UK Head of Node
Co-lead ELIXIR Interoperability Platform
SSBSS 2017, July 17 2017, Cambridge, UK
4th International Synthetic & Systems Biology Summer School

2. Data-driven and predictive biology
Data, Software, Models, SOPs….MATTER
Not a by-product.
It’s the fuel.
The assets.
modellers
experimentalists

3. Why Data Management
http://fair-dom.org
https://www.youtube.com/wat
ch?v=N2zK3sAtr-4
https://www.youtube.com/watch?v=PWutnWBfUSw

4. SystemsApproach: Context + more than Data
models, data, SOPs, samples, strains, publications….
multiple, interrelated assets. multiple, dispersed repositories
Multiple omics: genomics, transcriptomics
proteomics, metabolomics, fluxomics,
reactomics
Images, molecular biology, reaction kinetics…
SOPs, sample and strain metadata…
Models: Metabolic, gene network, kinetic…
Scripts and workflows
The relationships between…
Tracking: versions, provenance, parameters…
Citation and credit…
Standards
fairsharing.org

5. More than simple supplementary materials
16 datafiles (kinetic, flux inhibition, runout)
19 models (kinetics, validation)
13 SOPs
3 studies (model analysis, construction,
validation)
24 assays/analyses (simulations, model
characterisations)
Penkler, G., du Toit, F., Adams, W., Rautenbach, M.,
Palm, D. C., van Niekerk, D. D. and Snoep, J. L. (2015),
Construction and validation of a detailed kinetic model
of glycolysis in Plasmodium falciparum. FEBS J, 282:
1481–1511. doi:10.1111/febs.13237

6. SyntheticApproach - Automation
• Automate data management
– Spreadsheets, instruments, LIMS…
– Replication, comparison
• Support automation
– Tracking successful products from plasmids
– Informing robots
– Incorporate into pipelines and workflows
– Mediate through samples
– Standards
[Courtesy: Andrew Millar]

7. Systems Approach…Collaboration
teams, disciplines, partners
What methods are been used to determine
enzyme activity?
What SOP was used for this
sample?
Where is the validation data for this model?
Is there any group generating kinetic data?
Is this data available?
Track versions of my model
Whats the relationship between the data and
model?
Which data belong to
which publications?

8. modellers
experimentalists

9. End to end Management
Project Boot up, Run andWashup
• Capture
• Track
• Organise & Link
• Curate
• Report
• Exchange
• Retain
• Integrate
• Reuse other systems
• Support data-driven processes
CREATING
DATA
PROCESSING
DATA
ANALYSING
DATA
PRESERVING
DATA
ACCESS
TO DATA
RE-USING
DATA

10. The FAIR Guiding Principles for scientific data management and stewardship
https://www.nature.com/articles/sdata201618 (2016)

11. The greater good….
Access to public funded research, Reproducible results
Value and cite all research outputs
https://www.nature.com/articles/sdata201618 (2016)

12. UK Funder Data Policies
http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies
Compliance and Policy
Data Management Plans

13. https://wellcomeopenresearch.org/ Nature Scientific Data
Data (and software) as a first class citizen
Data (and software) Citation
Scholarly Communications Providers

14. The Personal good….
• reviewers want additional work
• statistician wants more runs
• analysis needs to be repeated
• post-doc leaves,
• student arrives
• new/revised datasets
• updated/new versions of
algorithms/codes
• sample was contaminated
• better kit - longer simulations
• new partners, new projects
Personal & Lab
Productivity
Sharing
Reproducibility

15. Catalogues
Standards: identifiers, metadata
Stores
Policy,
Identifiers,
Authorised
Access &
Licensing

16. Standards are not always used....
Formats MetadataMetadata reporting
guidelines
Ontologies
*top three most popular
The evolution of standards and data management practices in systems biology (2015). Stanford et al,
Molecular Systems Biology, 11(12):851

17. … model reuse and reproducibility tricky…
Stanford et alThe evolution of standards and data management practices in systems biology,
Molecular Systems Biology (2015) 11: 851 DOI 10.15252/msb.20156053

18. Catalogues, Storage and Publishing
Active Data, Published Data
local/project LIMS, data management,
analytics. active data.
global, public, central subject-specific databases
published data.
ACT LOCALTHINK GLOBAL

19. Cloud services
figshare
zenodo
Amazon Web Services
Google Cloud
Azure, EBI Embassy Cloud
Own cloud
FAIRDOMHub
mendeley data
Cloud Data Services
Cloud hosting services
OpenAIRE

20. Catalogues, Storage and Publishing
Active Data, Published Data
Stanford et al The evolution of standards and data
management practices in systems biology, Molecular
Systems Biology (2015) 11: 851 DOI 10.15252/msb.20156053

21. Catalogues, Storage and Publishing
Active Data, Published Data
Stanford et al The evolution of standards and data
management practices in systems biology, Molecular
Systems Biology (2015) 11: 851 DOI 10.15252/msb.20156053
Type specific archives
Fragmented silos

22. Catalogues, Storage and Publishing
Active Data, Published Data
Stanford et al The evolution of standards and data
management practices in systems biology, Molecular
Systems Biology (2015) 11: 851 DOI 10.15252/msb.20156053
Type specific archives
Fragmented silos
Experimental context
All together

23. Catalogues, Storage and Publishing
Active Data, Published Data
Central Repository Centric
Research Infrastructure for FAIR
Data for Life Sciences in Europe
Top down, 21 National Nodes +
EMBL-EBI
Project Centric
FAIR Research Data Management for
Data, SOPs, Models for Systems and
Synthetic Biology Projects
Grass roots Association of Institutions
and members funded by 4 EU countries.
http://www.fair-dom.org http://www.elixir-europe.org

24. modellers
experimentalists

25. FAIRDOM Consortium
Since 2008….
ERANets and ERACoFunds
National Programmes
National Centres
EU Research Infrastructures Sponsors:

26. Built by Project PALs
post docs, postgrads and techs

27. FAIRDOM
FAIRDOM Software Platform+Tools
A Central Public Hub
for Projects
Customised Project
Installations
Project Stewardship
Consultancy Services
Community
Activities
80 Projects 30+ Installations

28. http://fair-dom.org/knowledgehub/data-management-checklist/
https://dmponline.dcc.ac.uk/
http://dmp.fairdata.solutions/ (very early alpha)

29. FAIR Checklists
Making Data Findable (documentation and metadata management)
• What documentation and metadata will accompany the data (assist its
discoverability)? (Details on methodology, definitions, procedures, SOPs,
vocabularies, units, dependencies, etc)
• What information is needed for the data to be read and interpreted in the
future?
• What naming conventions will be used?
• How will you approach versioning your data?
• How will you capture / create this documentation and metadata?
• How do you ensure the completeness of the captured data?
Making Data Accessible
Specify which data will be made openly available taking into consideration
• What ethics and legal compliance issues do you have if any? Do you need
consent for data preservation and sharing? Do you have to protect certain
data? Is any data sensitive?
• Do you think you might have Intellectual Property Rights issues? Have you
considered ownership of the data, licensing, restrictions on use?
• Do you think you will need to embargo any data?
• How will you make the data available? (consider the platforms you will use:
databases, repositories, etc)
• What methods or software tools are needed to access the data? shoudl you
include documentation detailing how to access use/access the software that is
needed for accessing the data? Is it possible to include this software with the
data (e.g. source code, docker etc)
• If there are any restrictions on accessibility, how will you provide access?
Making Data Interoperable
• What standards (metadata vocabularies, formats,
checklists) or methodologies will you use?
• How do you address data and model quality?What
validation steps do you foresee?
• Will you use standardised vocabulary for all data types
to allow inter-disciplinary interoperability?
• Where you can not used standardised vocabulary for all
types of data, can you map to more commonly used
ontologies?
Making Data Re-usable
• How will you licence your data to permit the widest re-
use possible?
• When will the data be made available for re-use? Does
this include an embargo period? (if so, why?)
• Which data will be available for re-use during/after the
project? If not, why?
• What are your data quality assurance processes?
• How long do you expect your data to remain re-usable?

30. Community Actions
http://www.fair-dom.org
Samples Club Developers Club

31. Stewardship Support
500K needed*, a new career needing a career path
*European Open Science Cloud Report

32. FAIRDOM Platform
Free and Open Source
Front end
Project(s) Hub
Back end
Onsite storage & analytics
On site
Tracking, data analytic pipelines,
Extract,Transform and Load direct from the
instruments, large data management
LIMS, auto-archiving
Web-based portal
Project controlled spaces
Metadata catalogue &Yellow pages
Results repository, dissemination and collaboration
Tool gateway
Built using Built using

33. Back end
Instrument Data Management, LIMS, ELN
Samples
Protocols
Experiment
Description
Raw Data
Analysis
Scripts
Results
Laboratory Notebook &
Inventory Manager
ELN
LIMS-like
linking data to biological materials
• samples+protocols management
• data management
• experimental description
Big Data analytics on distributed compute resources

34. • Project controlled protected spaces
– Working space, show space for results
– Supp. materials space for publications
– Yellow pages and collaboration
– Upload or link to data
• One place catalogue
– Regardless of physical store
– Organised is ISA with shared metadata
– Standards-compliant
• Linked with other systems
– Project on-site (secure) repositories
– Public deposition archives
– Integrated with JWSOnline modelling tools
Front End Hub: A Commons
one place to Find, Access and organise assets
“Using FAIRDOMHub my own
lab colleagues saw what I was
doing and called to
collaborate!”

35. 859 people
80 projects
198 institutions
FAIRDOMHub.org Public Commons
self managed workspaces, controlled sharing, shared metadata
yellow pages

36. More than simple supplementary materials
16 datafiles (kinetic, flux inhibition, runout)
19 models (kinetics, validation)
13 SOPs
3 studies (model analysis, construction,
validation)
24 assays/analyses (simulations, model
characterisations)
Penkler, G., du Toit, F., Adams, W., Rautenbach, M.,
Palm, D. C., van Niekerk, D. D. and Snoep, J. L. (2015),
Construction and validation of a detailed kinetic model
of glycolysis in Plasmodium falciparum. FEBS J, 282:
1481–1511. doi:10.1111/febs.13237

37. Investigation
Study Analysis
Data
Model
SOP(Assay)
https://fairdomhub.org/investigations/56

38. Catalogue across repositories regardless of location
federated stores retaining context to support decision making and reuse
bridging local and global
In House Stores
External Databases
Publishing services
Secure Stores
Model Resources
Upload or
Reference

39. Protected spaces, sharing sensitivities
Open science applies to you but not me…
not available, not citable.
Licenses
Negotiated access
Embargos
Permission controls
Staged sharing

40. Act LocalThink Global Cloud Service
.org
Local retention
In flight management,
Private sharing
Customisation
Centres, large projects
National projects
Local skills for admin support
Post-project retention
One stop showcase
Self-managed sharing
Supplementary materials
Off-the-shelf features
Hosted on behalf of users
Delegated admin support
Long term repository
• Trusted
repository
• Guaranteed
until 2029
• Long term
maintenance
• Sustainability
• 1TB per
project stored
centrally.
• Much more
catalogued.

41. Hub common space, one place
to organise and report your assets
.org
Nucl. Acids Res. (2016) doi: 10.1093/nar/gkw1032
70+ Projects
30+ Installations
Public & cloud
Subject and Datatype archives

42. Typical Data Flows
HTP data
processing
management
exchange
deposition
publishing
reporting
ORGANISATION
COMMUNICATION
samples
analytics
models, SOPs
processed
data
DISSEMINATION
Less data, more metadata, potentially wider access
processed
data

43. Publishing…snapshot and assign DOIs
Credits and Citations
G. Penkler, F. Du Toit, W. Adams, M.
Rautenbach, D. C. Palm, D. D. Van
Niekerk, & J. L. Snoep. (2014).
Glucose metabolism in Plasmodium
falciparum trophozoites. FAIRDOMHub.
http://doi.org/10.15490/seek.1.investigati
on.56
Snapshot to fix state with
particular versions
Assign a DOI
Entry has citation metadata
Use in journals and in metrics
systems
Active entry continues to
evolve
Fenner et al, A Data Citation Roadmap for Scholarly Data Repositories
doi: https://doi.org/10.1101/097196

44. 18/07/2017 44
An “evolving manuscript” would begin with a pre-
publication, pre-peer review “beta 0.9” version of an
article, followed by the approved published article itself, [
… ] “version 1.0”.
Subsequently, scientists would update this paper with
details of further work as the area of research develops.
Versions 2.0 and 3.0 might allow for the “accretion of
confirmation [and] reputation”.
Ottoline Leyser […] assessment criteria in science revolve
around the individual. “People have stopped thinking
about the scientific enterprise”.
http://www.timeshighereducation.co.uk/news/evolving-manuscripts-the-future-of-scientific-communication/2020200.article

45. Retention: Moses
from the ERANET SysMO Programme
Project ended in 2010
Publication in 2014/2015
Using data from 2012
[Maxim Zakhartsev]

46. [Adapted from Ursula Klingmüller, Martin Böhm]
Excemplify
Antibody
Database
FAIR collaboration
from the ERANet ERASysAPP

47. 47
Programme
Overarching research theme (The Digital Salmon)
Project
Research grant (DigiSal, GenoSysFat)
Investigation
A particular biological process, phenomenon or thing
(typically corresponds to [plans for] one or more closely related
papers)
Study
Experiment whose design reflects a specific biological research
question
Assay
Standardized measurement or diagnostic experiment using a
specific protocol
(applied to material from a study)
Jon Olav Vik,
Norwegian University of Life Science
Integration with Norway’s national
einfrastructure for Life Science (NeLS)

48. Specialist
databases
Local
Biochem4j
ICE
Global
Brenda,
wikipathways,
Biomodels
ICE
Public
Deposition
Databases
Public
Catalogues
Tracking in
Specialist Systems
Institutional
Catalogue &
Repository

49. Specialist
databases
Local
Biochem4j
ICE
Global
Brenda,
wikipathways,
Biomodels
ICE
Public
Deposition
Databases
Public
Catalogues
Institutional
Catalogue &
Repository
Tracking in
Specialist Systems

50. Ubiquitous Spreadsheet
• Unifying processes
• Common spreadsheet models
– Consistency and quality of
collaboration
– Common identifier meanings
– Metadata collection
Tracking in
Specialist Systems

51. http://www.fairdomhub.org
https://sandbox1.fairdomhub.org
• empty box for safe playing
• copy the investigation that is there
• add your name to the guest list so we don’t double
up - http://tinyurl.com/sandboxlist
Try out for yourself…

52. The first steps?
• Metadata design
• Samples
– The link between everything
• The ubiquitous spreadsheet
– Templates and exchange…
– Unifying processes
– Carrying best practice
Image from FAIRSharing.org

53. Use and reuse
standard identifiers
General standards
Site specific
Community standards
e.g. SynBioChem ICE Strain convention
A URL preferably to identifiers.org that resolves to the
description of the host strain in NCBI taxonomy
e.g. e-Coli DH5α http://identifiers.org/taxonomy/668369
location independent resolvable
identifiers (URIs) decoupling the
identification of records from
their physical locations

54. Investigation:
Glucose metabolism in P.
falciparum trophozoites
Study:
Model construction
Study:
Model validation
Assay: LDH
Assay: PK
Assay: ENO
Assay: PGM
Assay: PGK
Assay: GAPDH
Assay: TPI
Assay: ALD
Assay: PFK
Assay: PGI
Assay: HK
Assay: GLCtr
Assay: PYRtr
Assay: LACtr
Assay: G3PDH
Assay: GLYtr
Assay: ATPase
Data: GLCtr
Model: GLCtr
Data: HK
Model: HK
Steady state
Incubation
penkler1
Validation data
penkler2
Validation data
...
...
SOP: GLCtr
SOP: HK
...
SOP: Validation
Assay: Culturing
Assay: Lysate prep.
SOP: Culturing
SOP: Lysate prep.
Design an ISA (Investigation, Study,
Assay/Analysis) structure.
Devising this
makes you
think…..

55. Use FAIRData and Metadata Standards
help to improve understanding and exchange….
Credit: Nicolas Le Novère, Babraham Institute, UK, adapted.
represents
genetic designs
- standardized
vocabulary of
schematic glyphs
- standardized
digital format.
ICE, SBOLStack,
iGEM
CIMR Core Information for Metabolomics Reporting
MIABE Minimal Information About a Bioactive Entity
MIACA Minimal Information About a Cellular Assay
MIAME Minimum Information About a Microarray Experiment
MIAME/Nutr MIAME / Nutrigenomics
MIAME/Plant MIAME / Plant transcriptomics
MIAME/Tox MIAME /Toxicogenomics
MIAPA Minimum Information About a Phylogenetic Analysis
MIAPAR Minimum Information About a Protein Affinity Reagent
MIAPE Minimum Information About a Proteomics Experiment
MIARE Minimum Information About a RNAi Experiment
MIASE Minimum Information About a Simulation Experiment

56. Where do I go for standards
information?
Linking models….
• connecting (experimental/simulation) data to models
• connecting the single standards?
• interfacing between the different scales?
https://fairsharing.org/collection/FAIRDOM

57. How do I design the metadata?
Metadata ramps
Metadata
Registration and Use

58. Metadata ramps: spreadsheet templates
Tooling for annotations and checklist templates for different types of assay data.
Embed ontologies into
Excel templates
Excel spreadsheets enriched
with ontology annotations
Upload, extract metadata and register
http://www.rightfield.org.uk

59. Ramping up Samples
Spreadsheets! A new framework for Syn and Sys Bio
Samples are Inputs and Outputs….
compliant

60. Sounds
hard….
what can
I do?
12 steps to being FAIR
plan to be born FAIR
1. plan data management lifecycle: plan, cost and implement pathways and
storage including what you will archive, what you will throw away, how you
will collect metadata and how you will curate throughout
2. use standard identifiers and identifier standards
3. use metadata standards with data provenance
4. catalogue / register data with metadata
5. have access and sharing policies with licenses
6. use data (assets) management platforms and tools that work together
7. deposit into public archives
8. have a sustainability / end project plan
9. resource and support, and that also means people too
10. embed data management into work practices and do some training
11. give credit
12. check if you have sensitive data issues

61. What can you do?
• Make a Data Management Plan (check the checklist).
• Get an account on the FAIRDOMHub or install your own.
• Define and share your SOPs.
• Who is your group’s data steward?
• How are they getting credit?
• Know your local data management policies and resources.
• Get some training.
• Educate your supervisors, institutions and peers.
• Build some metadata ramps

62. The Data Steward
function, profession, cultural shift
• 500,000 needed in Europe*
• Specialist skills
• Career pathways
• Recognition
Curation and management
• Supported, Resourced
• Recognised, Rewarded
Sharing policy and practice embedded
* Realising the Open European Science Cloud (2016)

63. Jon OlavVik,
Norwegian University of Life Science
Maksim Zakhartsev
University Hohenheim, Stuttgart,
Germany
Alexey Kolodkin
Siberian Branch
Russian Academy of Sciences
Tomasz Zieliński,
SynthSys Centre
University Edinburgh, UK
Martin Peters, Martin Scharm
Systems Biology Bioinformatics
University of Rostock, Germany

64. Reading List
• Wolstencroft et al (2016). “FAIRDOMHub: a repository and collaboration environment
for sharing systems biology research”. NucleicAcids Research, 45(D1): D404-D407.
DOI: 10.1093/nar/gkw1032
• Rice and Southal,The Data Librarian's Handbook, Wiley Publishing, 2016
• Stanford et alThe evolution of standards and data management practices in systems
biology, Molecular Systems Biology (2015) 11: 851 DOI 10.15252/msb.20156053
• Wilkinson et alThe FAIR Guiding Principles for scientific data management and
stewardship, https://www.nature.com/articles/sdata201618 (2016)
• McMurry, Juty, et al. (2017) Identifiers for the 21st century: How to design, provision,
and reuse persistent identifiers to maximize utility and impact of life science data.
PLoS Biol 15(6): e2001414. https://doi.org/10.1371/journal.pbio.2001414
• Fenner et al, A Data Citation Roadmap for Scholarly Data Repositories doi:
https://doi.org/10.1101/097196
• Realising the Open European ScienceCloud
https://ec.europa.eu/research/openscience/pdf/realising_the_european_open_science
_cloud_2016.pdf

65. Website list
• FAIRDOM http://www.fair-dom.org
• FAIRDOMHub http://www.fairdomhub.org
• Rightfield http://www.rightfield.org.uk
• FAIRSharing http://www.fairsharing.org
• ELIXIR http://www.elixir-europe.org
• Software Carpentry https://software-carpentry.org/
• DataCarpentry http://www.datacarpentry.org/
• Sandbox https://sandbox1.fairdomhub.org
• empty box for safe playing
• copy the investigation that is there
• add your name to the guest list so we don’t double up -
http://tinyurl.com/sandboxlist