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FAIRsFAIR and implementing the FAIR principles
- a Finnish perspective
Jessica Parland-von Essen & Josefine Nordling 15.4.2020
https://www.fairdata.fi/koulutus/koulutuksen-tallenteet/
Turning FAIR into Reality
Final Report and Action Plan from the European Commission
Expert Group on FAIR Data (2018)
Step 1: Define – concepts for FAIR DigitalObjects and the
ecosystem
Rec. 1: Define FAIR for implementation
Rec. 2: Implement a model for FAIR Digital Objects
Rec. 3: Develop components of a FAIR ecosystem
Step 2: Implement – culture, technology and skills for FAIR
practice
Rec. 4: Develop interoperability frameworks for FAIR sharing within
disciplines and for interdisciplinary research
Rec. 7: Support semantic technologies …
2
https://doi.org/doi:10.2777/1524
Creating a FAIR Ecosystem
3
FAIR Ecosystem Components
4 http://doi.org/10.5281/zenodo.3565428
TFiR https://doi.org/doi:10.2777/1524
INFRAEOSC-5 Task Forces
ESFRI Clusters
‘Horizontals’EOSC
Governance Board
FAIR (5c)
(5a)
INFRAEOSC-5 Cross Project Collaboration
Board
Regional Nodes / Thematic
Projects (5b)
Other FAIR initiatives
EOSC
Executive Board
Practice and
policy
Training and
education
Certification
Facilitating FAIR in EOSC
https://fairsfair.eu/
D3.3 FAIR Policy Enhancement Recommendations
Based on D3.1 FAIR Policy Landscape Analysis https://doi.org/10.5281/zenodo.3558173, some examples:
• Building on the work of other initiatives (FAIRsharing, EOSCpilot, RDA), agree on a common set of
FAIR policy elements and work with stakeholders to employ them to describe their policies.The
emphasis should be on describing those policy elements that may be considered ‘rules’ rather than
simply suggested good practice to support machine-actionability.
• Standardised exceptions for not sharing data should be developed and promoted in associated
policy guidance.
• Provide mechanisms to enable searching for data by license type in repositories.
• Support researchers to assess the potential risks, benefits and associated costs to enable the
sharing of FAIR data as they draft their DMP.
• RDM support should place an emphasis on selecting which data to make and keep FAIR as well as
advising on where data should be deposited7
https://doi.org/10.5281/zenodo.3686901
D3.2 FAIR Data PracticeAnalysis
• Landscape review
oESFRI clusters
• Examples of good practices
oGood community metadata
oData management planning templates
oMandatory DMP training
oPublished DMPs in catalogue
oTools for assessing data quality and FAIRness
8
https://doi.org/10.5281/zenodo.3581353
National Archives of Finland. Photo: Helsinki City Museum
https://finna.fi/Record/hkm.HKMS000005:00000zfk
9
https://doi.org/10.5281/zenodo.3581353
Towards enabling FAIR data
FAIR compliance in services?
• M2.7 Assessment Report on 'FAIRness of Services'
ohttps://doi.org/10.5281/zenodo.3688762
• There are many frameworks to assess FAIRness of datasets
oRDA maturity model
oFAIR assist
oSee further examples in report
• CoreTrust Seal, Rules of participation and FitSM examples of tools for assessing services,
but they don’t address alignment with the FAIR principles per se
• Sufficient metadata, PID management and API are elements in providing FAIR support
• A service can enable, respect or reduce the FAIRness of a digital object
11
D4.1 Recommendations on Requirements for FAIR Datasets in
Certified Repositories
• Trusted Digital Repositories are an important element of the FAIR
Ecosystem, as they offer long-term stewardship to keep the data objects
FAIR
• The CoreTrustSeal offers a certification process for assessing repositories
• Most of the FAIR data principles are either explicitly or implicitly covered by
the CoreTrustSeal requirements
• Assessment during different stages of the RDM lifecycle. Examples
oFor the researcher manual evaluation https://satifyd.dans.knaw.nl/
oFor the repository FsF metrics to support CTS evaluation
12
https://doi.org/10.5281/zenodo.3678716
13
The goal is an API that exposes FAIR data
– read more about the project
https://fairsfair.eu/application-results-open-call-data-repositories
How a repository can support the FAIR data principles
D2.3 Set of FAIR data repositories features
• Metadata for digital objects:
o The repository should provide metadata in different formats, which
can be harvested by different search engines (I)
o Metadata should be provided as RDF, including JSON-LD. Based on
these machines can provide human-friendly
presentations/visualisations by resolving the URIs and retrieving the
human-readable labels (I)
o Providing metadata at the level of files, variables, attributes, individual
cells, granularity to be decided by the repository (I)
o Gather provenance metadata on digital objects and files upon upload
(IR)
o Provide masks and ways to quickly upload metadata (I)
o Demand fine-grained metadata from data providers (FI)
o Implement community standards (FI)
o Automatic ontology suggestions and lookup (IF)
o Landing pages should be machine-interpretable or implement content
negotiation, have metadata in different formats (FI)
o HTTP header should contain technical metadata about the DO (FI)
14
https://doi.org/10.5281/zenodo.3631528
• Machine-readable and interpretable metadata about
repository itself (I)
• Expose (Meta) Data Model (in machine-readable form) (I)
• PID policies
• PID for each digital object or file (I)
• Use global persistent identifiers (I)
• The target of PID should be inferable by machines
from PID metadata
itself, employ PID information types or Linked Data
type (I)
• Data object and file requirements
• Connect compute infrastructures and data
repositories
(to avoid commuting data) (I)
• Subsetting of data (I)
• Technical support for predefined file formats
(including complex data
formats like netCDF), with a preference for open file
formats (FI)
• Machine-readable license (R)
• The repository should provide a search interface or be linked
to aggregating services that enable findability (F)
What FAIRsFAIR metrics could be like
15
FsF-F1-01D Universally Unique Identifier
FsF-F1-02D Persistent Identifier
FsF-F2-01M Descriptive Metadata
FsF-F3-01M Inclusion of Data Identifier in Metadata
FsF-F4-01M Searchable Metadata
FsF-A1-01M Data Access Level
FsF-A2-01M Metadata Preservation
FsF-I1-01M Semantic Representation of Metadata
FsF-I3-01M Qualified References to Related Entities
FsF-R1-01M Community-Driven Metadata
FsF-R1-02M Data Content Description
FsF-R1.1-01M Data Usage Licence
FsF-R1.3-01D Standard File Format
https://doi.org/10.5281/zenodo.3678716
Pillars of FAIRness
17
FINDABLE
ACCESSIBLE
INTEROPERABLE
RE-USABLE
Mons, Barend & Neylon, Cameron
& Velterop, Jan & Dumontier, Michel
& Bonino da Silva Santos, Luiz Olavo & Wilkinson, Mark.
(2017). Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding
principles for the European Open Science Cloud.
Information Services & Use. 37. 1-8.
https://doi.org/doi:10.3233/ISU-170824
Data
catalog
Resolver
PID
Data file
License
Configuration
file
Read me
Persistent identifiers are
necessary for research
and a key element in FAIR data
Persistent identifiers and research data in Finland
• URN, DOI and handle are common PIDs
for research data
• The DataCite Finland Consortium offers
access to minting DOI
• CSC is also a member in the ePIC
Consortium (handle identifiers)
• CSC PID Policy
ohttps://research.csc.fi/pid-policy
PID info
https://wiki.eduuni.fi/display/C
scPidVerkosto/PID-verkosto
Open Science WG
https://wiki.eduuni.fi/x/GYv6B
PID Forum
https://www.pidforum.org/
EOSC PID Policy
https://doi.org/10.5281/zenodo.35
74203
D2.1 Report on FAIR requirements for persistence and
interoperability 2019
• FAIR technologies and methods
o Semantic interoperability
o Semantic artefacts
o PIDs and PID services
• FAIR in the context of the data life cycle
o Repositories
o Evolving datasets and data citation
• ESFRI RI’s
o Metadata, persistent identifiers, semantic artefacts in different domain infrastrucutres
20
http://doi.org/10.5281/zenodo.3557381
Semantic Interoperability
19/04/202021
Interoperability is the ability of computer systems to
transmit data with unambiguous, shared meaning.
Semantic interoperability is a requirement to enable
machine computable logic, inferencing, knowledge
discovery, and data federation between information
systems. Semantic interoperability is achieved when
the information transferred has, in its communicated
form, all of the meaning required for the receiving
system to interpret it correctly, even when the
algorithms used by the receiving system are
unknown to the sending system. Syntactic
interoperability is a prerequisite to semantic
interoperability.
CASRAI. https://dictionary.casrai.org/Semantic_interoperability
Trans-language
interoperability
http://doi.org/10.5281/zenodo.3557381
Read more about the European Interoperability Framework (EIF)
https://ec.europa.eu/isa2/eif_en
Semantic artefacts adoption
22
Characteristics Indicators
Coverage in field widely approved and adopted used within community,
acknowledged mandate
Coverage of content sufficient amount of the
terminology
coverage,
completeness, coherence
structure corresponds to the
ontology of the domain
certification, quality, community
approval
Governance usable and fit the purpose compatibility, format, granularity,
workflow
actively maintained by a trusted,
authoritative party
curation, versioning, persistence
(re)usability open access and licence and
documented
http://doi.org/10.5281/zenodo.3557381
D2.2 FAIR Semantics: First recommendations
• To create semantic interoperability we need semantic artefacts that make semantics
machine readable
• Semantic artefacts can be considered a special type of dataset that the FAIR data
principles can be customized for
• To support a FAIR Ecosystem well the semantic artefacts should meet more specific
requirements
• The 17 preliminary technical recommendations presented in D2.2 are under discussion
among experts in RDAVSSIG and FAIRsFAIR organized activites
• Semantic artefacts are most easily made FAIR by creating them FAIR from the start
23
https://doi.org/10.5281/zenodo.3707985
Recommendations for best practice regarding FAIR semantics
BP-Rec. 1: Use a unique naming convention for concept/class and relations
BP-Rec. 2: Use an Ontology Naming Convention
BP-Rec. 3: Use defined ontology design patterns
BP-Rec. 4: Create mappings validated by domain experts
BP-Rec. 5: Define workflows between different formats
BP-Rec. 6: Harmonize the methodologies used to develop semantic artefacts
BP-Rec. 7: Interact with the designated community and manage user-centric development
BP-Rec. 8: Provide a structured definition for each concept
BP-Rec. 9:The underlying logic of semantic artefacts should be grounded on the domain it intends to
describe
BP-Rec. 10: Define a set of governance policies for the semantic artefacts
24
https://doi.org/10.5281/zenodo.3707985
Semantic vocabulary services in Finland
• The Finto service offers a good service for SKOS ontologies, suitable for
offering terminologies in a well structured way
ohttp://finto.fi/yso/fi/
• The HelsinkiTerm Bank for the Arts and the Sciences also offers platform,
though not FAIR
ohttps://tieteentermipankki.fi/wiki/Termipankki:Etusivu/en
• Species published as linked data
ohttps://laji.fi/
• See also Heldig
ohttps://www.helsinki.fi/en/helsinki-centre-for-digital-humanities/infrastructure
• Yhteentoimivia.suomi.fi
25
FAIR:The Landscape of Persistence and Interoperability 2019
26
FAIRness on a more generic level is not ready and clearly
defined yet, but key elements are clear.
The most popular, potentially most useful, and most
complex approaches on improving FAIRness of data are
based on technologies using Linked Data.
http://doi.org/10.5281/zenodo.3557381
FAIR:The Landscape of Persistence and Interoperability 2019
27
Semantic artefacts are important in building interoperability and good quality
(meta)data.
Crosswalks, mappings and semantic application profiles should be published
and registered in machine readable formats.
Reuse of semantic artefacts should be promoted by publishing application
profiles. Curated registries like the EOSC Hub, FAIRsharing and re3data.org are
important resources.
http://doi.org/10.5281/zenodo.3557381
FAIR:The Landscape of Persistence and Interoperability 2019
28
The development should be research rather than technology driven.
The landscape is diverse in all aspects. Differences inside domains are
often bigger than differences between domains.
Data citation and machine actionable solutions should be developed
in parallel. Community adoption and trust are the decisive factors.
http://doi.org/10.5281/zenodo.3557381
FAIR:The Landscape of Persistence and Interoperability 2019
29
Solutions should be user friendly, context sensitive and transparent
to the users.
PID and data type registries should promote reuse rather than bulk
creation of PIDs. To support interoperability, they should be
considered semantic artefacts and used mindfully.
http://doi.org/10.5281/zenodo.3557381
Steps towards more FAIR research data
There are many things to address when enabling creation of FAIR data
and a FAIR Ecosystem.They include both technical solutions and a wide
range of data management practices.
These are things we need to look into also in Finland.We have good
competencies in semantics and data management planning.
We are engaged on an international level but should broaden our
engagement even more and harness our competencies more efficiently.
Cooperation and knowledge sharing on all levels are necessary.
30

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A Finnish perspective on FAIRsFAIR outputs

  • 1. FAIRsFAIR and implementing the FAIR principles - a Finnish perspective Jessica Parland-von Essen & Josefine Nordling 15.4.2020 https://www.fairdata.fi/koulutus/koulutuksen-tallenteet/
  • 2. Turning FAIR into Reality Final Report and Action Plan from the European Commission Expert Group on FAIR Data (2018) Step 1: Define – concepts for FAIR DigitalObjects and the ecosystem Rec. 1: Define FAIR for implementation Rec. 2: Implement a model for FAIR Digital Objects Rec. 3: Develop components of a FAIR ecosystem Step 2: Implement – culture, technology and skills for FAIR practice Rec. 4: Develop interoperability frameworks for FAIR sharing within disciplines and for interdisciplinary research Rec. 7: Support semantic technologies … 2 https://doi.org/doi:10.2777/1524
  • 3. Creating a FAIR Ecosystem 3
  • 4. FAIR Ecosystem Components 4 http://doi.org/10.5281/zenodo.3565428 TFiR https://doi.org/doi:10.2777/1524
  • 5. INFRAEOSC-5 Task Forces ESFRI Clusters ‘Horizontals’EOSC Governance Board FAIR (5c) (5a) INFRAEOSC-5 Cross Project Collaboration Board Regional Nodes / Thematic Projects (5b) Other FAIR initiatives EOSC Executive Board
  • 7. D3.3 FAIR Policy Enhancement Recommendations Based on D3.1 FAIR Policy Landscape Analysis https://doi.org/10.5281/zenodo.3558173, some examples: • Building on the work of other initiatives (FAIRsharing, EOSCpilot, RDA), agree on a common set of FAIR policy elements and work with stakeholders to employ them to describe their policies.The emphasis should be on describing those policy elements that may be considered ‘rules’ rather than simply suggested good practice to support machine-actionability. • Standardised exceptions for not sharing data should be developed and promoted in associated policy guidance. • Provide mechanisms to enable searching for data by license type in repositories. • Support researchers to assess the potential risks, benefits and associated costs to enable the sharing of FAIR data as they draft their DMP. • RDM support should place an emphasis on selecting which data to make and keep FAIR as well as advising on where data should be deposited7 https://doi.org/10.5281/zenodo.3686901
  • 8. D3.2 FAIR Data PracticeAnalysis • Landscape review oESFRI clusters • Examples of good practices oGood community metadata oData management planning templates oMandatory DMP training oPublished DMPs in catalogue oTools for assessing data quality and FAIRness 8 https://doi.org/10.5281/zenodo.3581353 National Archives of Finland. Photo: Helsinki City Museum https://finna.fi/Record/hkm.HKMS000005:00000zfk
  • 11. FAIR compliance in services? • M2.7 Assessment Report on 'FAIRness of Services' ohttps://doi.org/10.5281/zenodo.3688762 • There are many frameworks to assess FAIRness of datasets oRDA maturity model oFAIR assist oSee further examples in report • CoreTrust Seal, Rules of participation and FitSM examples of tools for assessing services, but they don’t address alignment with the FAIR principles per se • Sufficient metadata, PID management and API are elements in providing FAIR support • A service can enable, respect or reduce the FAIRness of a digital object 11
  • 12. D4.1 Recommendations on Requirements for FAIR Datasets in Certified Repositories • Trusted Digital Repositories are an important element of the FAIR Ecosystem, as they offer long-term stewardship to keep the data objects FAIR • The CoreTrustSeal offers a certification process for assessing repositories • Most of the FAIR data principles are either explicitly or implicitly covered by the CoreTrustSeal requirements • Assessment during different stages of the RDM lifecycle. Examples oFor the researcher manual evaluation https://satifyd.dans.knaw.nl/ oFor the repository FsF metrics to support CTS evaluation 12 https://doi.org/10.5281/zenodo.3678716
  • 13. 13 The goal is an API that exposes FAIR data – read more about the project https://fairsfair.eu/application-results-open-call-data-repositories How a repository can support the FAIR data principles
  • 14. D2.3 Set of FAIR data repositories features • Metadata for digital objects: o The repository should provide metadata in different formats, which can be harvested by different search engines (I) o Metadata should be provided as RDF, including JSON-LD. Based on these machines can provide human-friendly presentations/visualisations by resolving the URIs and retrieving the human-readable labels (I) o Providing metadata at the level of files, variables, attributes, individual cells, granularity to be decided by the repository (I) o Gather provenance metadata on digital objects and files upon upload (IR) o Provide masks and ways to quickly upload metadata (I) o Demand fine-grained metadata from data providers (FI) o Implement community standards (FI) o Automatic ontology suggestions and lookup (IF) o Landing pages should be machine-interpretable or implement content negotiation, have metadata in different formats (FI) o HTTP header should contain technical metadata about the DO (FI) 14 https://doi.org/10.5281/zenodo.3631528 • Machine-readable and interpretable metadata about repository itself (I) • Expose (Meta) Data Model (in machine-readable form) (I) • PID policies • PID for each digital object or file (I) • Use global persistent identifiers (I) • The target of PID should be inferable by machines from PID metadata itself, employ PID information types or Linked Data type (I) • Data object and file requirements • Connect compute infrastructures and data repositories (to avoid commuting data) (I) • Subsetting of data (I) • Technical support for predefined file formats (including complex data formats like netCDF), with a preference for open file formats (FI) • Machine-readable license (R) • The repository should provide a search interface or be linked to aggregating services that enable findability (F)
  • 15. What FAIRsFAIR metrics could be like 15 FsF-F1-01D Universally Unique Identifier FsF-F1-02D Persistent Identifier FsF-F2-01M Descriptive Metadata FsF-F3-01M Inclusion of Data Identifier in Metadata FsF-F4-01M Searchable Metadata FsF-A1-01M Data Access Level FsF-A2-01M Metadata Preservation FsF-I1-01M Semantic Representation of Metadata FsF-I3-01M Qualified References to Related Entities FsF-R1-01M Community-Driven Metadata FsF-R1-02M Data Content Description FsF-R1.1-01M Data Usage Licence FsF-R1.3-01D Standard File Format https://doi.org/10.5281/zenodo.3678716
  • 17. 17 FINDABLE ACCESSIBLE INTEROPERABLE RE-USABLE Mons, Barend & Neylon, Cameron & Velterop, Jan & Dumontier, Michel & Bonino da Silva Santos, Luiz Olavo & Wilkinson, Mark. (2017). Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use. 37. 1-8. https://doi.org/doi:10.3233/ISU-170824
  • 18. Data catalog Resolver PID Data file License Configuration file Read me Persistent identifiers are necessary for research and a key element in FAIR data
  • 19. Persistent identifiers and research data in Finland • URN, DOI and handle are common PIDs for research data • The DataCite Finland Consortium offers access to minting DOI • CSC is also a member in the ePIC Consortium (handle identifiers) • CSC PID Policy ohttps://research.csc.fi/pid-policy PID info https://wiki.eduuni.fi/display/C scPidVerkosto/PID-verkosto Open Science WG https://wiki.eduuni.fi/x/GYv6B PID Forum https://www.pidforum.org/ EOSC PID Policy https://doi.org/10.5281/zenodo.35 74203
  • 20. D2.1 Report on FAIR requirements for persistence and interoperability 2019 • FAIR technologies and methods o Semantic interoperability o Semantic artefacts o PIDs and PID services • FAIR in the context of the data life cycle o Repositories o Evolving datasets and data citation • ESFRI RI’s o Metadata, persistent identifiers, semantic artefacts in different domain infrastrucutres 20 http://doi.org/10.5281/zenodo.3557381
  • 21. Semantic Interoperability 19/04/202021 Interoperability is the ability of computer systems to transmit data with unambiguous, shared meaning. Semantic interoperability is a requirement to enable machine computable logic, inferencing, knowledge discovery, and data federation between information systems. Semantic interoperability is achieved when the information transferred has, in its communicated form, all of the meaning required for the receiving system to interpret it correctly, even when the algorithms used by the receiving system are unknown to the sending system. Syntactic interoperability is a prerequisite to semantic interoperability. CASRAI. https://dictionary.casrai.org/Semantic_interoperability Trans-language interoperability http://doi.org/10.5281/zenodo.3557381 Read more about the European Interoperability Framework (EIF) https://ec.europa.eu/isa2/eif_en
  • 22. Semantic artefacts adoption 22 Characteristics Indicators Coverage in field widely approved and adopted used within community, acknowledged mandate Coverage of content sufficient amount of the terminology coverage, completeness, coherence structure corresponds to the ontology of the domain certification, quality, community approval Governance usable and fit the purpose compatibility, format, granularity, workflow actively maintained by a trusted, authoritative party curation, versioning, persistence (re)usability open access and licence and documented http://doi.org/10.5281/zenodo.3557381
  • 23. D2.2 FAIR Semantics: First recommendations • To create semantic interoperability we need semantic artefacts that make semantics machine readable • Semantic artefacts can be considered a special type of dataset that the FAIR data principles can be customized for • To support a FAIR Ecosystem well the semantic artefacts should meet more specific requirements • The 17 preliminary technical recommendations presented in D2.2 are under discussion among experts in RDAVSSIG and FAIRsFAIR organized activites • Semantic artefacts are most easily made FAIR by creating them FAIR from the start 23 https://doi.org/10.5281/zenodo.3707985
  • 24. Recommendations for best practice regarding FAIR semantics BP-Rec. 1: Use a unique naming convention for concept/class and relations BP-Rec. 2: Use an Ontology Naming Convention BP-Rec. 3: Use defined ontology design patterns BP-Rec. 4: Create mappings validated by domain experts BP-Rec. 5: Define workflows between different formats BP-Rec. 6: Harmonize the methodologies used to develop semantic artefacts BP-Rec. 7: Interact with the designated community and manage user-centric development BP-Rec. 8: Provide a structured definition for each concept BP-Rec. 9:The underlying logic of semantic artefacts should be grounded on the domain it intends to describe BP-Rec. 10: Define a set of governance policies for the semantic artefacts 24 https://doi.org/10.5281/zenodo.3707985
  • 25. Semantic vocabulary services in Finland • The Finto service offers a good service for SKOS ontologies, suitable for offering terminologies in a well structured way ohttp://finto.fi/yso/fi/ • The HelsinkiTerm Bank for the Arts and the Sciences also offers platform, though not FAIR ohttps://tieteentermipankki.fi/wiki/Termipankki:Etusivu/en • Species published as linked data ohttps://laji.fi/ • See also Heldig ohttps://www.helsinki.fi/en/helsinki-centre-for-digital-humanities/infrastructure • Yhteentoimivia.suomi.fi 25
  • 26. FAIR:The Landscape of Persistence and Interoperability 2019 26 FAIRness on a more generic level is not ready and clearly defined yet, but key elements are clear. The most popular, potentially most useful, and most complex approaches on improving FAIRness of data are based on technologies using Linked Data. http://doi.org/10.5281/zenodo.3557381
  • 27. FAIR:The Landscape of Persistence and Interoperability 2019 27 Semantic artefacts are important in building interoperability and good quality (meta)data. Crosswalks, mappings and semantic application profiles should be published and registered in machine readable formats. Reuse of semantic artefacts should be promoted by publishing application profiles. Curated registries like the EOSC Hub, FAIRsharing and re3data.org are important resources. http://doi.org/10.5281/zenodo.3557381
  • 28. FAIR:The Landscape of Persistence and Interoperability 2019 28 The development should be research rather than technology driven. The landscape is diverse in all aspects. Differences inside domains are often bigger than differences between domains. Data citation and machine actionable solutions should be developed in parallel. Community adoption and trust are the decisive factors. http://doi.org/10.5281/zenodo.3557381
  • 29. FAIR:The Landscape of Persistence and Interoperability 2019 29 Solutions should be user friendly, context sensitive and transparent to the users. PID and data type registries should promote reuse rather than bulk creation of PIDs. To support interoperability, they should be considered semantic artefacts and used mindfully. http://doi.org/10.5281/zenodo.3557381
  • 30. Steps towards more FAIR research data There are many things to address when enabling creation of FAIR data and a FAIR Ecosystem.They include both technical solutions and a wide range of data management practices. These are things we need to look into also in Finland.We have good competencies in semantics and data management planning. We are engaged on an international level but should broaden our engagement even more and harness our competencies more efficiently. Cooperation and knowledge sharing on all levels are necessary. 30