STATUS: IN DEVELOPMENT
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:
FAIRopoly – FAIRification Guidance for ERN Patient Registries
FAIR in action - a flexible framework to guide FAIRification
In this step we present a list of common roles and resources involved in the FAIRification process listed by expertise and by FAIR principle. This will help you identify which team members and expertise are required and available (or missing) in your team.
Since a FAIR data steward is essential for reaching the FAIRification goals, a separate step has been dedicated to this role. See “Metroline Step: Have a FAIR data steward on board” for details on this crucial role.
Why is this step important
FAIRification is a complicated process and requires expertise from a variety of fields. Hence, assembling the right team is essential to meet your goals.
Expertise requirements for this step
TODO
How to
[Mijke: Another RDMkit page on this: https://rdmkit.elixir-europe.org/dm_coordination ]
[Sander] Would it make sense that, if we mention roles in this section in other pages, these roles are actually specified in this page’s How to? We could even create hyperlinks to this page.
RDMkit has a nice section about Roles in Data Management (with more details than I copied below) [Mijke coordinated/wrote most of it this]
In this section, information is organised based on the different roles a professional can have in research data management. You will find:
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.
The table below is based on De Novo and RDMkit. <linkjes>
Expert | Description | Metroline Steps |
|---|---|---|
Domain specialist | Domain specialists have deep knowledge and expertise in a particular domain. They have a deep understanding of the intricacies, challenges, and nuances of their field. | |
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. | |
EDC system specialist | Individual who has experience with and knowledge of Electronic Data Capture (EDC) systems, such as Castor EDC, REDCAP or OpenClinica. | |
Information Professionals | Librarians, archivists, and information scientists involved in organizing and preserving data assets. | |
Institutional Review Board | The IRB is concerned with protecting the rights of human subjects and is charged with the responsibility of reviewing research to which the Medical Research Involving Human Subjects Act is applicable involving human participants. Mooie zin van maken a committee at an institution that applies research ethics by reviewing the methods proposed for research involving human subjects, to ensure that the projects are ethical. The main goal of IRB reviews is to ensure that study participants are not harmed (or that harms are minimal and outweighed by research benefits https://www.amsterdamumc.org/en/research-support/ethical-review.htm | |
IT and Systems Administrators | Professionals responsible for maintaining data infrastructure(s) and ensuring technical compatibility and accessibility for an organisation or department. | |
Decision-makers responsible for research data management policies that promote FAIR data practices within an institute. | ||
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. | |
TODO Research software engineers (RSE) design, develop and maintain software systems that help researchers reach their scientific goals. You are responsible for the implementation of IT infrastructure solutions and the access to data and software. 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) | Mijke, since you were involved in RDMkit, do you know what they mean here? It’s called a “software engineer”, but we don’t see them engineering software / writing scripts in the description given by RDMkit. | |
Semantic data modelling specialists | A semantic modeler is a specialist responsible for creating and managing semantic models. These models are a representation of knowledge and concepts in a structured format that a computer can understand. | |
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]
→ 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
Practical Examples from the Community
Example team:
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
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
Toolkit for building your dream team: “a resource intended to make it as easy as possible to organise a workshop aimed at raising awareness of and facilitating discussion around the diversity of roles that contribute to research”. […] “[t]he knowledge sector is now looking towards a team-based approach bringing together more overtly diverse team members with specific skills in funding, research design, data analysis, data management, software development, research ethics, political relationships, dealing with business, interdisciplinarity, communications etc.” https://research-dream-team-toolkit.readthedocs.io/en/latest/index.html
[FAIRopoly] https://www.ejprarediseases.org/fairopoly/
[FAIRinAction] https://www.nature.com/articles/s41597-023-02167-2
[Generic] https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification
Contributors
Dena Tahvildari; Sander de Ridder; Jolanda Strubel; Bruna dos Santos Vieira; Mijke Jetten; Fieke Schoots; Ines De Oliveira Coelho Henriques; Shuxin Zhang; Alberto Cámara; César Bernabé; Joeri van der Velde