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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:

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 mostly based on De Novo and RDMkit.

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 (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.

Policy maker

Decision-makers responsible for research data management policies that promote FAIR data practices within an institute.

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 Engineer

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

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.

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:

  • 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.

[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

Plan is om deze sectie weg te laten en alles te verwerken de teksten

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

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