FAIR Principles & Digital Objects: The role of METADATA

The advancement of digital science thrives on the timely sharing and accessibility of digital data, and every day research activities provide unlimited amounts of data that could have an impact on achieving the objectives of a number of seamless research processes and their quality

With all the information and data available, it is important that your data are valuable, properly described and managed. By taking care of these matters at an early stage, your research will stand out and foster new promising integrative scientific approaches and studies based on FAIR Data.

Image source: CGIAR

FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) aim to help you create, share and re-use quality, valuable, and responsible data. Only by leveraging and applying the evidence about FAIR data impact we can help data live up to its long-term potential.

FAIR DATA PRINCIPLES: BACKGROUND & EXPLANATION

FAIR PRINCIPLES & DIGITAL OBJECTS: THE ROLE OF METADATA

Metadata – or ‘data about data’ – is arguably one of the most powerful tools available in scholarly communications. Good metadata enables discoverability and access, and (potentially) eliminates errors.

Interoperable metadata linkages and promoting data citations could provide an efficient model or a framework enabling access, management and re-use of any Digital Object (D.O.) – for example, a data set or journal paper - in a long-term.

"Implementing FAIR requires a model for #FAIRData Objects which have a PID linked to different types of essential metadata, including provenance and licencing. Use of community standards and sharing of code is fundamental for interoperability and reuse." (Twitter)

According to the FAIR Principles, to be(come):

 

D.O./DATA

(META)DATA

F

FINDABLE,

D.O./data
should have...

... sufficiently rich metadata and a unique and persistent identifier using a standardized communications protocol. 

Metadata should clearly and explicitly include the identifier of the data it describes

[check the FAO Meaningful Bibliographic Metadata (M2B) recommendations: IDENTIFIER]

A

ACCESSIBLE,

D.O./data
should have...

... metadata understandable to humans and machines. This metadata is registered or indexed in a searchable resource and deposited in a trusted repository.

(Meta)data should be supported by open, free, and universally implementable protocols which allow for an authentication and authorization procedure, where necessary.

Metadata is accessible, even when the data is no longer available

I

INTEROPERABLE,

D.O./data
should have...

... metadata that use a formal, accessible, shared, and broadly applicable language for knowledge representation

[check the FAO LODE-BD: encoding strategies for producing meaningful Linked Open Data (LOD)-enabled bibliographical data]. 

(Meta)data should meet domain relevant community standards

[check: FAIRsharingAgriSemantic Map of standards,  Basel Register of Thesauri, Ontologies & Classifications].

(Meta)data includes qualified references to other (meta)data.

R

REUSABLE,

D.O./data
should have...

... metadata with clear usage licenses and provide accurate information on provenance.

(Meta)data is richly described with a plurality of accurate and relevant attributes, and meets domain-relevant community standards.

THE NEED FOR MACHINE ACTIONABLE METADATA

Machine-actionable metadata (supported by Machine-actionable Data Management Plans) are core to the FAIR Principles. 

GO FAIR and RDA (Research Data Alliance) members have launched the 'Metadata for Machines' workshop series (M4M) to assess the state of metadata practices in data-related communities and stimulate the creation and re-use of FAIR metadata standards and machine-ready metadata templates (definitions of metadata categories).

Collectively, the M4M series of workshops result in recommendations about metadata and an Open repository of machine-ready, easy to use and interoperable FAIR metadata templates and components. Anyone can access this ’sea’ of metadata templates/components, re-use them as they see fit, and deploy them using metadata editors and other data capture tools (Learn more from: GO FAIR, Making it easy for humans to make metadata for machines).

HOW TO MAKE DATA BE(COME) FAIR TOGETHER?

'If you want to go fast, go alone. If you want to go far, go together!' (African Proverb)...

PLAN

- check, e.g. - DSW: Data Stewardship Wizard

CREATE

-check, e.g. - FAIRifier

 

PUBLISH

- check, e.g. - FAIR Data Point

 

FIND

- check, e.g. - FAIR Data: Many paths lead to the EOSC

ANNOTATE

- check, e.g. - ORKA (Open Reusable Knowledge Annotator) and other tools in FAIR Data tools indexed in Dutch Techcentre for Life Sciences

EVALUATE

- check, e.g. - FAIR Metrics Framework;

- check, e.g. - FAIR self-assessment tool

METADATA

- check, e.g. - RDA METADATA Catalog

- check, e.g. - CEDAR ­(CENTER for EXPANDED ANNOTATION and RETRIEVAL)

STANDARDS

- check, e.g. - FAIRsharing.org

 

Joint implementation based on FAIR Principles for research data, algorithms, processes, software etc. (check: Research Data Alliance outputs and their adoption stories and adoption use-cases):

  • stimulates cooperation, convergence and global (data) interoperability;

Discover RDA IGAD ‘Landscaping the Use of Semantics to Enhance the Interoperability of Agricultural Data' … and keep coping with FAIRifying challenge!

  • prevents further fragmentation and enhance opportunity to help shape the Internet of common/shared FAIR Data and Services; 
  • pushes 'increasingly speaking with one voice' (check, e.g. GO FAIR Implementation Networks). 

FAIR DATA-RELATED

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