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| Status | ||||
|---|---|---|---|---|
|
Short Description
To be able to reach your FAIRification goals, having a team with the right skillset is important. The composition of the team depends on the exact goals and different skills may be necessary in different phases of the of the process. See, for example:
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Expert | Description | Metroline Steps |
|---|---|---|
Clinicians specialised in the domain | Has understanding/knowledge about:
| |
Data manager | Has understanding/knowledge about:
| |
EDC system specialist | Has understanding/knowledge about:
| |
FAIR data stewards | <See the other page>
| |
Health-RI expert team | Has understanding/knowledge about:
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Institutional Ethical Review Board | Has understanding/knowledge about:
| |
Local data stewards = Data Steward | Data stewardship is a relatively new profession and a catch-all term for numerous support functions, roles and activities. It implies professional and careful treatment of data throughout all stages of a research process. Has understanding/knowledge about:
| Even nakijken: weet een data steward iets van standards/semantic modeling? Kan je dat verwachten? |
Patient advocate for the domain | Has understanding/knowledge about:
| |
Semantic data modelling specialists | Has understanding/knowledge about:
| |
Senior expert of standards for automated access protocols and privacy preservation | Has understanding/knowledge about:
| |
Senior healthcare interoperability expert | Has understanding/knowledge about:
| |
Software developer = Research Software Engineer | Research software engineers (RSE) in the life sciences design, develop and maintain software systems that help researchers manage their software and data. The RSE’s software tools and infrastructure are critical in enabling scientific research to be conducted effectively. Has understanding/knowledge about:
|
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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.
→ teams needs expertise in these areas (these are the exact same as used by De Nove table; I’m guessing de novo used the general paper. That’s great though. )
The data to be FAIRified and how they are managed,
the domain and the aims of the data resource within it
architectural features of the software that is (or will be) used for managing the data
access policies applicable to the resource
the FAIRification process (guiding and monitoring it),
FAIR software services and their deployment,
data modelling,
global standards applicable to the data resource
global standards for data access.
[RDMkit]
Perhaps: https://rdmkit.elixir-europe.org/dm_coordination
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