Alignment with the framework page.
1. Get the 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
2. Model the domain
2.1. Identify data types
Data type identification informs the selection of appropriate data standards, ontologies and target repositories
3. Select the identifier scheme
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
4. Apply data standards
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
5. Choose data vocabularies
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
6. Transform data for inter-operability
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
7. Host your data
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. Share your data
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