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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].
[Health-RI_FAIRification_Step_Report] In this section we describe the needed expertise for making data more FAIR. In general, FAIRification work requires consultancy with:
Domain experts who know the domain-specific data - the meaning of the data, but also the provenance and relations to other data.
FAIR experts or project managers that conducted a FAIRification project before (who know how to interpret and implement the FAIR principles).
Next to that, depending on your FAIRification goals, you might need more specific experts. To help you identify which expertise is required and available (or not) in your team, we present below . 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 FAIRification process by expertise and by FAIR principle. For the items that you do not have the expertise, please contact your local data stewards or other data management services or Health RI to discuss a plan of action.[Jolanda Summary] 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].
To . This will help you identify which team members and expertise is are required and available (or missing) in your team, we present a list of common roles and resources involved in the FAIRification process listed by expertise and by FAIR principle. [Sander] As a FAIRification .
Since a FAIR data steward is essential for reaching the FAIRification goals, the step a full page separate step has been dedicated to this role. See “Metroline “Metroline Step: Have a FAIRification FAIR data steward on board” 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
[Fieke] The data steward profile is often described according to three roles (policy, research and infrastructure) and eight task areas (policy & strategy; compliance; FAIR data; Services; Infrastructure; Knowledge management; network; data archiving). A single data steward can be responsible for all task areas, but tasks can also be divided among central and embedded / domain data stewards. Each task area requires different competencies. The EMBL-EBI competency hub describes activities, ksa’s (knowledge, skills & abilities) and learning objective for each rol and task area.
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]
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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. 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 on useful information for getting started with data management specific to each role.
Roles:
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.
[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.
[RDMkit]
Perhaps: https://rdmkit.elixir-europe.org/dm_coordination
[Health-RI_FAIRification_Step_Report]
Expertise and Example Experts - Source: [De Novo]
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Expertise/Knowledge
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Example Experts
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a
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On the data to be FAIRified and how they are managed
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Local data steward
FAIR data steward
Data manager
EDC system specialist
Clinicians specialised in the domain
Patient advocate for the domain
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b
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On the domain and on what a data resource is used for
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Clinicians specialised in the domain
Patient advocate for the domain
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c
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On architectural features of the software that is (or will be) used for managing the data
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EDC system specialist
Software developer
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d
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On access policies applicable to the resource
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Local data steward
Clinicians specialised in the domain
Institutional Ethical Review Board
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e
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On the FAIRification process (guiding and monitoring it)
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Local data stewards
FAIR data stewards
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f
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On FAIR software services and their deployment
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EDC system specialist
Software developer
Health-RI expert team
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g
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On semantic data modelling
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Local and FAIR data steward
Semantic data modelling specialists
Clinicians specialised in the domain
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h
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On global standards applicable to the data resource interoperability
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Local and FAIR data stewards
EDC system specialist
Senior healthcare interoperability expert
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i
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On global standards for data access
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Local data and FAIR stewards
EDC system specialist
Senior expert of standards for automated access protocols and privacy preservation
FAIR Principles and Example Resources
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#
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FAIR Principle
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Example resource
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Globally unique and persistent identifiers
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DOI, ORCID, EUPID,
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Metadata about data
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DCAT (standard)
FAIR data point (former DTL metadata editor) (tool)
ISA Framework
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Adding clearly and explicitly the identifier of the data they describe in the metadata
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FAIRifier tool
FAIR data point
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indexing or registering metadata and data in a searchable resource
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FAIR data point
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metadata and data can be retrieved by their identifier via an protocol (making explicit the contact protocol to access the data)
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Http/ Ftp
In case of sensitive data, add to the metadata the contact info (email / telephone) of who to discuss data access with, and a clear protocol for such access request.
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open, free and universally implementable protocols
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Email / phone
Http / ftp / SMTP
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protocol that allows for authentication / authorization when necessary
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(set user rights, register users in repository)
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metadata is there even when data is not available anymore (see F4)
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FAIR data point
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Metadata and data use a proper language for knowledge representation (incl (1) commonly used controlled vocabularies, ontologies, thesauri (having resolvable globally unique and persistent identifiers, see F1) and and (2) a good data model (a well-defined framework to describe and structure (meta)data).
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RDF (ttl, rdfs, rdfxml, shex, shacl)
Dublin Core / DCAT
OWL
DAML+OIL
JSON LD
Semantic data models
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The controlled vocabulary used to describe datasets needs to be documented and resolvable using globally unique and persistent identifiers. This documentation needs to be easily findable and accessible by anyone who uses the dataset.
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FAIR data point
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The goal is to create as many meaningful links as possible between (meta)data resources to enrich the contextual knowledge about the data.
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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
This section should show the step applied in a real project. Links to demonstrator projects.
References & Further reading
Mijke Jetten, Marjan Grootveld, Annemie Mordant, Mascha Jansen, Margreet Bloemers, Margriet Miedema, & Celia W.G. van Gelder. (2021). Professionalising data stewardship in the Netherlands. Competences, training and education. Dutch roadmap towards national implementation of FAIR data stewardship (1.1). Zenodo. https://doi.org/10.5281/zenodo.4623713
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
Authors / Contributors
HRI FAIR TEAM (Jolanda, Bruna, Fieke, Sander)
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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 |
|---|---|---|
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.