The FAIRification Template.
The FAIRification Template operationalizes the FAIRification Process by outlining a set of clear, distinct steps for the implementation stage within the FAIRification Cycle. It comprises eight steps (covering) grouped in 3 main dimensions :
- hosting capabilities (e.g., data access, data retrieval, versioning, etc.)
- data representation and format (e.g., applying data standards and aligning vocabularies, etc.)
- data content (e.g., identifier minting and annotation with controlled vocabularies, etc)
The Template supports users as needed by offering concrete tasks commonly encountered across FAIRification efforts. While the Template presents the different steps in a recommended logical sequence (e.g. data cannot be transformed to an interoperable data model if no such model exists or if the data type is not properly understood), not all steps will be required for, nor relevant to each FAIRification scenario.
Hosting Environment Capabilities
What capabilities of the hosting environment are provided to enable and support the use of FAIR data
Hosting capabilities.
H. Hosting environment capabilities
What capabilities of the hosting environment that enables and supports the use of FAIR data.1.1 Data access
Considerations relating to how data is accessed, eg through APIs, via controlled access
1.2 Data retrieval
Considerations relating to data retrieval, eg query language, results representation and exporting capabilities
7.1 Data hosting
Considerations around data hosting infrastructure such as markup and search engine optimisation
7.2 Data versioning
Considerations around data versioning
7.3 Data transfer
Considerations around data transfer such as file formats, repository types and checksumming
8.1 Data licensing
Data licensing considerations such as data which license is most appropriate for a given scenario
8.2 Data anonymisation
Data anonymisation considerations
8.3 Data release
Data release considerations such as when to release a dataset and where to release it
Content and Context
What is reported in the data object and the metadata object
The FAIRification Template - Content & Context capabilities.
C. Content & Context
How the data object & metadata object are represented and formatted.2.1 Identify data types
Data type identification informs the selection of appropriate data standards, ontologies and target repositories
3.1 Identifier minting
How to create unique, persistent and resolvable identifiers
3.2 Reusing community identifiers
How to reuse existing identifiers in a dataset
5.1 Selecting data vocabularies
How to select the most appropriate vocabularies to annotate a dataset
5.2 Developing data vocabularies
How to develop new vocabularies from scratch
5.3 Annotating with data vocabularies
How to annotate data and metadata with terms from vocabularies
5.4 Managing vocabularies
How to manage vocabularies and ontologies
Representation and Format
How the data object and metadata object are represented and formatted
The FAIRification Template - Representation and Format capabilities.
R. Representation and format
What is reported in the Dataset (data) & the Dataset Descriptor (metadata)4.1 Reusing existing data standards
How to reuse existing data standards
4.2 Developing data standards
How to develop a new data standard if no appropriate standards exist
4.3 Applying data standards
How to apply data standards to datasets, especially retroactively
4.4 Validating against data standards
How to use validation to ensure that a dataset is compliant with a data standard
6.1 Identifier mapping
How to map between different types of equivalent identifiers
6.2 Vocabulary alignment
How to map between different equivalent vocabulary terms
6.3 Data model mapping
How to map equivalent concepts from different data models