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description
‘Human resources are the most important part of the FAIRification process. Having a team with the right skillset will play an important role in achieving your FAIRification goals.’(FAIRopoly)
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, the step 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 Assembling the right team is essential to meet your goalsFAIR objectives.
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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 Step 1
Define the FAIRification Objectives you want to reach in your project. These objectives define which FAIR Metroline steps are relevant and each step suggests the expertise necessary.
Step 2
The table below gives an overview of many roles a professional can have in research data management. You In this table 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.
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Expert
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Description
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Metroline Steps
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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.
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FAIR Data Steward/Data manager
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the role, including nearly identical roles between brackets;
identical roles are not used on Metroline pages;
if you’re interested in pages that use an identical role (e.g. “data manager”) , look for pages with the main role (e.g. “data steward”);
note that the identical roles mentioned are not exhaustive.
a description of the role;
specific variants of a role, such as “a researcher with domain knowledge”;
in which steps (the variant of) a role is used.
The roles and descriptions in the table are adjusted from the EOSC Digital skills for FAIR and open science report and the NPOS Professionalising data stewardship in the Netherlands: competences, training and education report, Some roles not considered relevant were left out from the table and some that were deemed missing were added. With the with the exception of the researcher and citizen role, the mentioned roles are often summarised as (research) data support professionals.
Information Professionals
Librarians, archivists, and information scientists involved in organizing and preserving data assets.
Role | Description | Usage | Metroline steps |
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Researcher (Scientist) | A researcher obtains, processes, produces, deposits and shares research data. | Researcher with domain knowledge |
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Researcher with XYZ | |||
Data scientist | A data scientist is an expert on data processing, not necessarily from a specific discipline, who is capable of evaluating data quality, extracting relevant knowledge from data and representing such knowledge. | Data scientist | |
Research software engineer | A growing number of people in academia combine expertise in programming with an intricate understanding of research. These Research Software Engineers may start off as researchers who spend time developing software to progress their research or they may start off from a more conventional software-development background and be drawn to research by the challenge of using software to further research. For an elaborate overview of this role see the aforementioned NPOS report, chapter 4. | Research software engineer | |
Infrastructure professional (IT and Systems Administrators) | An infrastructure professional is an IT expert who manages and operates infrastructures and the necessary services for the storage, preservation and processing of data. | Infrastructure professional | |
Trainer (Educator) | A trainer is an expert who designs, organises, shapes content and manages and/or coordinates training activities, participating in the delivery of the training. | Trainer | |
Data curator | A data curator is an expert on the management and oversight of an organisation's entire data to ensure compliance with policy and/or regulatory obligations for longterm preservation and to provide higher-level users with high quality data that is easily accessible in a consistent manner. | Data curator | |
Data steward (Data librarian, Data manager) | A person responsible for keeping the quality, integrity, and access arrangements of data and metadata in a manner that is consistent with applicable law, institutional policy, and individual permissions. Data stewardship implies professional and careful treatment of data throughout all stages of a research process. A data steward aims at guaranteeing that data is appropriately treated at all stages of the research cycle (i.e., design, collection, processing, analysis, preservation, data sharing and reuse). 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.
FAIR data steward |
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Semantic expert (Metadata expert, interoperability expert) |
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Data steward with EDC knowledge | |||
Citizen | Citizens in this context are any kind of people having interest in one or several scientific disciplines (including, but not limited to, the open source community or commercial companies undertaking research), who want to get information or contribute to a citizen science initiative or other initiatives of general public interest, or have their own interest in learning and addressing a specific challenge which is not part of his/her professional activity. | Citizen with domain knowledge |
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Policy maker | Policy makers gather information through consultation and research, and reduce and extract from the information a policy, set of policies or a strategic framework which serve to promote a preferred course of action and could include financial support to research. | Policy maker | |
ELSI expert | ELSI experts provide guidance and answers to the ethical, legal and social implications of research. | ELSI expert |
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To members of the Writing group: if the necessary expertise cannot be found in the table above, check the one below. If you need one of the roles described there, let Sander/Mijke/Jolanda know.
If you still cannot find a suitable role, tell us what role you need and we can discuss where/how it should be added.
Expert | Description | Metroline Steps |
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Institutional Review Board (IRB) / Medical Ethics Review Committee (METC) | Evaluate research protocols and ensure the research complies with regulatory requirements and ethical standards. For research to which the WMO (Medical Research Involving Human Subjects Act) is applicable, evaluation must be done by an accredited METC or by the CCMO (Central Committee on Research Involving Human Subjects). |
IT and Systems Administrators
Professionals responsible for maintaining data infrastructure(s) and ensuring technical compatibility and accessibility for an organisation or department.
<On access policies applicable to the resource> | |
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)
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:
Global standards for data access
Senior healthcare interoperability expert
Has understanding/knowledge about:
Global standards applicable to the data resource interoperability
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.
Todo:
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:
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Domain expert; provides context to the FAIRification efforts from the perspective of a domain
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Data stewards; helps defining FAIR objectives to meet the project’s, funder’s, journal’s and/or institute’s requirements
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ELSI experts, help identifying the legal compliance and ethical aspects of your FAIR objectives.
[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.
Training
Relevant training will be added in the future.
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
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Expertise requirements for this step
To be able to define your team, you may need the experts described below.
Project manager. Knows the goals of the project and can help decide what team members are necessary to reach those goals.
HR. Involved when hiring new people.
Practical examples from the community
VASCERN (European Reference Network on Rare Multisystemic Vascular Diseases) describe the team used for the VASCA (Vascular Anomalies Registry) FAIRification in their De Novo paper, with a detailed description available in the paper’s supplementary material, table S1.
VASCA is a demonstrator project. More information can be found on its demonstrator page on the Health-RI website.
Training
Research Dream Team Toolkit: a resource for organising a workshop around the diversity of roles that contribute to
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[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
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research.
The Turing Way handbook contains several chapters with lessons and recommendations on research teams: E.g. Teamwork; Research Infrastructure Roles; Team manual, etc.
More relevant training will be added in the future if available.
Suggestions
Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.