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Data FAIRification requires different types of expertise and should therefore be carried out in a multidisciplinary team guided by FAIR data steward(s). The different sets of expertise are on i) the data to be FAIRified and how they are managed, ii) the domain and the aims of the data resource within it, iii) architectural features of the software that is (or will be) used for managing the data, iv) access policies applicable to the resource, v) the FAIRification process (guiding and monitoring it), vi) FAIR software services and their deployment, vii) data modelling, viii) global standards applicable to the data resource, and ix) global standards for data access. A good working approach is to organize a team that contains or has access to the required expertise. The core of such a team may be formed by data stewards, with at least expertise of the local environment and of the FAIRification process in general.
[RDMkit]
Perhaps: https://rdmkit.elixir-europe.org/dm_coordination
[Health-RI_FAIRification_Step_Report]
Expertise and Example Experts - Source: [De Novo]
| Expertise/Knowledge | Example Experts |
a | On the data to be FAIRified and how they are managed |
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b | On the domain and on what a data resource is used for |
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c | On architectural features of the software that is (or will be) used for managing the data |
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d | On access policies applicable to the resource |
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e | On the FAIRification process (guiding and monitoring it) |
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f | On FAIR software services and their deployment |
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g | On semantic data modelling |
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h | On global standards applicable to the data resource interoperability |
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i | On global standards for data access |
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FAIR Principles and Example Resources
# | FAIR Principle | Example resource |
Globally unique and persistent identifiers | DOI, ORCID, EUPID, | |
Metadata about data |
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Adding clearly and explicitly the identifier of the data they describe in the metadata |
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indexing or registering metadata and data in a searchable resource |
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metadata and data can be retrieved by their identifier via an protocol (making explicit the contact protocol to access the data) |
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open, free and universally implementable protocols |
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protocol that allows for authentication / authorization when necessary |
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metadata is there even when data is not available anymore (see F4) |
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Metadata and data use a proper language for knowledge representation (incl (1) commonly used controlled vocabularies, ontologies, thesauri (having resolvable globally unique and persistent identifiers, see F1) and and (2) a good data model (a well-defined framework to describe and structure (meta)data). |
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The controlled vocabulary used to describe datasets needs to be documented and resolvable using globally unique and persistent identifiers. This documentation needs to be easily findable and accessible by anyone who uses the dataset. |
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The goal is to create as many meaningful links as possible between (meta)data resources to enrich the contextual knowledge about the data. |
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Resource glossary
Tool/Standardl # can be used to #
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