How to offer FAIR enabling services
The FAIR principles apply to metadata and data. Their implementation requires a set of data services and components that need to be presented in the broader ecosystem. These services themselves need to be discoverable, identifiable, or indexed and follow appropriate standards and protocols that enable interoperability and machine-to-machine communication. Furthermore, it requires a set of skills in data management according to FAIR principles.
A Data Steward should be the first person to whom researchers can turn for help. Specifically, in managing data, developing metadata and using repositories. A Data Steward helps to create mature working practices for Research Data Management (RDM) and according to FAIR principles.
FAIRsFAIR training events for Data Stewards is a good start to understand the existing competence frameworks for Data Stewards and other relevant professions which can be integrated on different education levels.
Metadata should be detailed and comprehensive, including descriptive information about the context and characteristics of the data. Rich metadata help a user/machine to understand how the data has been created, the fitness for other researchers’ demands, consistency with other datasets, and provide much of the content that the search engine will be able to index and discover.
In the following links you can find the most common catalogues and guidelines for developing metadata:
- RDA metadata standard
- Index of metadata standards
- The Data Documentation Initiative (DDI)
- Guide to writing “readme” style metadata
Persistent identifiers are essential to robust data management strategies and enable the creation of trusted digital connections. Persistent identifiers eliminate the ambiguity in the meaning of the published data by assigning a unique identifier to each object, contributor, or organization.
Persistent Identifiers, a core component of connected research – ARDC will help you to determine what, why and when to use persistent identifiers (PIDs).
The following are the most commonly used PIDs for research data and for people:
A Data Steward should ensure data interoperability with applications for analysis and processing as well as data integration with other data. In this section we collect information related to standards in making data interoperable.
- Ontology vs Controlled Vocabularies
- OpenAIRE: Research graph data model
- Linked Open Vocabularies (linkeddata.es)
- Re3data.org , FAIRsharing & FAIRassist: Explore what resources exist — and if they can be used, extended or added new
- (5-star Open Data): Why Machine-actionability is core to each of the FAIR principles
FAIR data tools
How FAIR is our data? This is the most question that Data Steward been asked by researchers and ICT management!
Some highlights to find out whether data is aligned with FAIR, FAIRness assessment tools and some FAIR use cases:
- FAIR Toolkit and FAIR use cases for Life Science Industry
- Enabling FAIR Data – FAQs – COPDESS
- Assessment: FAIR Data and Software
Licenses and copyrights help to clarify the “R” in the FAIR principles. As Data Steward should know the tools and guides to licensing data and be able to help researchers to identify the owner of data:
Tools that help choose the right license:
- License Selector (ufal.github.io)
- Choose a License (creativecommons.org)
- Choose an open source license | Choose a License
- CLARIN License Category Calculator | CLARIN ERIC
Making data FAIR requires repositories with sustainable governance and organizational frameworks, reliable infrastructure, and comprehensive policies whilst preserving them over time. More information about the TRUST principles can be found on the Research Data Alliance (RDA) website.
- Recommendations on certifying services required to enable FAIR within EOSC: An analysis of activities relevant to certification of the services required to enable FAIR research outputs within EOSC
- Repository reflections on the FAIRsFAIR Repository Support Programme Part 3: Advice for repositories considering certification | FAIRsFAIR
- Practical Guide to the International Alignment of Research Data Management: A reference document on criteria for the selection of trustworthy repositories.
- “As open as possible, as closed as necessary”.
- Even sensitive and private data can be FAIR! (FAIR for Sensitive Data).
The FAIR Cookbook contains ten recipes for the Life Science domain that provide practical support for researchers, data stewards, trainers, and developers on how to FAIRify data, assess FAIRness, which models, technologies, tools and standards, as well as the required skills to achieve and improve FAIRness. Also interesting for other disciplines!