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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.
Policy maker: As a policy maker, you are responsible for the development of a strategic data management framework and the coordination and implementation of research data management guidelines and practices.
Principal Investigator: As a Principal Investigator (PI), you may have recently acquired project funding. More and more funders require data management plans (DMP), stimulating the researcher to consider, from the beginning of a project, all relevant aspects of data management.
Researcher: Your research data is a major output from your research project, it supports your research conclusions, and guides yourself and others towards future research. Therefore, managing the data well throughout the project, and sharing it, is a crucial aspect of research.
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.
Trainer: As a trainer, you design and deliver training courses in research data management with a focus on bioinformatics data. Your audience is mainly people in biomedical sciences: PhD students, postdocs, researchers, technicians and PIs.
The VASCA FAIRification core team consisted of a local data steward, an external FAIR datasteward, and an EDC system specialist. Throughout the project, additional expertise wasconsulted, such as a clinician specialised in vascular anomalies, the Institutional Ethical ReviewBoard, FAIR software developers, and researchers. A full overview of the different kinds ofexpertise and which part of the FAIRification process they contributed to can be found in TableS1
Expert | Description | Metroline Steps |
|---|---|---|
Clinicians specialised in the domain | Has understanding/knowledge May have relevant expertise about:
| Has understanding/knowledge about: I changed to the latter, since they don’t necessarily always have the knowledge you’re looking for. Let’s decide on Friday. Domain Experts are individuals who possess deep knowledge and expertise in a particular domain or industry. They have a deep understanding of the intricacies, challenges, and nuances of their field. Their expertise comes from their years of experience and interactions within their specific domain. (copy-paste) |
Data manager | Has understanding/knowledge about:
| A data manager is a professional who oversees the development and use of data systems, ensuring effective data management, secure procedures, and data analysis. They enforce policies, establish data sharing rules, and troubleshoot data-related issues for organizations (copy-pasted). |
EDC system specialist | Has understanding/knowledge about:
| I’m not sure what job this is (something you could find on e.g. indeed) Part of Clinical Data Manager? If I look here in example 3 that seems to overlap? We could also write our own description, e.g.: |
FAIR data stewards | <See the other page>
| Maybe we can add FAIR and local data stewards as 1 entry here - data stewards or perhaps (FAIR) data stewards? We keep the list (add the “access policies” entry to make it complete?). It’s probably easier to discuss data stewards on the separate page, also given the distinction made in both Fieke’s link and rdmkit |
Health-RI expert team | Has understanding/knowledge about:
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Institutional Ethical Review Board | Has understanding/knowledge about:
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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?(copy-pasted) |
Patient advocate for the domain | Has understanding/knowledge about:
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Semantic data modelling specialists | Has understanding/knowledge about:
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Senior expert of standards for automated access protocols and privacy preservation | Has understanding/knowledge about:
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Senior healthcare interoperability expert | Has understanding/knowledge about:
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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:
| (copy-pasted) |
In the FAIRification objectives step the following expertise is mentioned:
Domain expert; provides context to the FAIRification efforts from the perspective of a domain
Data stewards; helps defining FAIR objectives to meet the project’s, funder’s, journal’s and/or institute’s requirements
FAIR experts,such as metadata/semantics specialists; helps specifying the metadata/modeling aspects of FAIR objectives
ELSI experts, help identifying the legal compliance and ethical aspects of your FAIR objectives.
[Generic]
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. )In list form, expertise required:
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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