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Short 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 [FAIRopoly]. The composition of the team depends on the exact goals and different skills may be necessary in different phases of the of the process [FAIRinAction]. The core of the team may be formed by one or more data stewards with expertise of the FAIRification process in general and knowledge of the local environment [Generic]. The team may, furthermore, contain (part-time) advisors with, for example domain expertise [FAIRopoly], as well as data managers, software developers, research scientists, project managers and legal support [FAIRinAction].
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. 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. 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.
Expertise requirements for this step
This section could describe the expertise required. Perhaps the Build Your Team step could then be an aggregation of all the “Expertise requirements for this step” steps that someone needs to fulfil his/her FAIRification goals.
How to
This section should help complete the step. It’s crucial that this is practical, doable and scalable.
Depending on the type of step, this can, for example, be a reference to one or more (doable) recipes, or perhaps some form of checklist? The recipes/best-practices presented should be based on experts from the field.
This should probably be a subpage so as not to have too much text on this page.
References, if relevant, to FAIRCookbook, RDMKit, GOFAIR?
Sub headers if relevant for specific domains?
[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.
Practical Examples from the Community
This section should show the step applied in a real project. Links to demonstrator projects.
References & Further reading
[De Novo] https://ojrd.biomedcentral.com/articles/10.1186/s13023-021-02004-y
[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
Authors / Contributors
Experts whom you can contact for further information
How to
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. In this table you will find:
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.
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. | 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). <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). |
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 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.