| Status | ||||
|---|---|---|---|---|
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A description of the main tasks usually handled by each role.
A collection of research data management responsibilities for each role.
Links to RDMkit guidelines and advice (where applicable) on useful information for getting started with data management specific to each role.
Roles:
Data Steward/Data manager: Individuals responsible for managing and curating research or healthcare data within organizations or projects. Job title and exact activities and responsibilities vary between organisations. In the Metroline steps we will refer to this role as “data steward”. Details on this role in the team are described in a separate step “Have a FAIR data steward on board”.
Policy maker: Decision-maker involved in shaping data management policies that promote FAIR data practices.
Principal Investigator: leads a clinical trial or research project. Responsible for following the data management requirements and guidelines of the organisation and/or funder. Decisions regarding data management are documented in the DMP (data management plan).
Researcher /scientist: Professionals involved in collecting, analyzing, and sharing data as part of a clinical trial, research project or other scientific endeavors.
Information Professionals: Librarians, archivists, and information scientists involved in organizing and preserving data assets.
IT and Systems Administrators: Professionals responsible for maintaining data infrastructure and ensuring technical compatibility and accessibility for an organisation or department.
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: Professionals involved in training and educating others, such as PhD students, postdocs, researchers, technicians and PIs. In case of FAIR related training this includes practices for managing and sharing data.
The table below is based on De Novo and RDMkit. <linkjes>
Expert | Description | Metroline Steps | ||
|---|---|---|---|---|
Clinicians specialised in the domain | 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 specialist | 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) May have relevant expertise about:
| ||
FAIR Data Steward/Data manager | Individuals responsible for managing and curating research or healthcare data within organizations or projects. Job title and exact activities and responsibilities vary between organisations. In the Metroline steps we will refer to this role as “data steward”. Details on this role in the team are described in a separate step “Have a FAIR data steward on board”. Has understanding/knowledge ofMay have relevant expertise 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 | 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:
| Should HRI expert team be in here? | ||
Information Professionals | Librarians, archivists, and information scientists involved in organizing and preserving data assets. | |||
IT and Systems Administrators | Professionals responsible for maintaining data infrastructure and ensuring technical compatibility and accessibility for an organisation or department. | |||
Policy maker / Institutional Ethical Review Board | Decision-maker involved in shaping data management policies that promote FAIR data practices. Has understanding/knowledge about:
| |||
Patient advocate for the domain | Has understanding/knowledge about:
| Semantic data modelling specialists | ||
Senior expert of standards for automated access protocols and privacy preservation | Senior healthcare interoperability expert | Has understanding/knowledge about:
Software developer = Principal Investigator | Leads a clinical trial or research project. Responsible for following the data management requirements and guidelines of the organisation and/or funder. Decisions regarding data management are documented in the DMP (data management plan). | |
Researcher /scientist | Professionals involved in collecting, analyzing, and sharing data as part of a clinical trial, research project or other scientific endeavors. | |||
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. (copy-pasted) 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:
| |||
Professionals involved in training and educating others, such as PhD students, postdocs, researchers, technicians and PIs. In case of FAIR related training this includes practices for managing and sharing data. |
Potential Todo:
Add [Generic] expertise list to each of the Experts in the table (if possible)
Rewrite some descriptions that are copy-pasted
...
Verify Expertise already mentioned in step exists here
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.
→ 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
[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
...
Local data steward
FAIR data steward
Data manager
EDC system specialist
Clinicians specialised in the domain
Patient advocate for the domain
...
b
...
On the domain and on what a data resource is used for
...
Clinicians specialised in the domain
Patient advocate for the domain
...
c
...
On architectural features of the software that is (or will be) used for managing the data
...
EDC system specialist
Software developer
...
d
...
On access policies applicable to the resource
...
Local data steward
Clinicians specialised in the domain
Institutional Ethical Review Board
...
e
...
On the FAIRification process (guiding and monitoring it)
...
Local data stewards
FAIR data stewards
...
f
...
On FAIR software services and their deployment
...
EDC system specialist
Software developer
Health-RI expert team
...
g
...
On semantic data modelling
...
Local and FAIR data steward
Semantic data modelling specialists
Clinicians specialised in the domain
...
h
...
On global standards applicable to the data resource interoperability
...
Local and FAIR data stewards
EDC system specialist
Senior healthcare interoperability expert
...
i
...
On global standards for data access
...
Local data and FAIR stewards
EDC system specialist
Senior expert of standards for automated access protocols and privacy preservation
Resource glossary
Tool/Standard # can be used to #
Goal Modelling (see link) is a standard that can be used to represent goals that are connected to each other and it helps defining clear FAIRification objectives for both research question and process perspectives.
FAIR data point (see link) is a tool guarantees many FAIR principles and can be used to describe metadata completely in accordance to the DCAT standard, you can create and publish metadata in the FAIR data point which is a searchable and indexable resource (see fair data index, every fair data point is indexed in the fair data index),
DCAT (see link) is a standard to describe metadata of, from detail to general levels: distribution, dataset, catalogue
RDF (see link) extensible knowledge representation model is a way to describe and structure datasets
Smart Guidance (see link) is a tool that defines the specific steps for RD registries data FAIRification
Semantic data model for (e.g. Data model for set of common data elements for rare disease registration, Data model for Omics data, data model for WHO Rapid COVID CRF, Data models from EBI in the ‘documentation’ links on this page http://www.ebi.ac.uk/rdf/)
Practical Examples from the Community
...
The VASCA FAIRification core team consisted of a local data steward, an external FAIR data steward, and an EDC system specialist. Throughout the project, additional expertise was consulted, such as a clinician specialised in vascular anomalies, the Institutional Ethical Review Board, FAIR software developers, and researchers. A full overview of the different kinds of expertise and which part of the FAIRification process they contributed to can be found in TableS1
Links to demonstrator projects.
References & Further reading
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Salome Scholtens, Mijke Jetten, Jasmin Böhmer, Christine Staiger, Inge Slouwerhof, Marije van der Geest, & Celia W.G. van Gelder. (2022). Final report: Towards FAIR data steward as profession for the lifesciences. Report of a ZonMw funded collaborative approach built on existing expertise (Versie 4). Zenodo. https://doi.org/10.5281/zenodo.7225070
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