Metroline Step: Build the Team

STATUS: IN DEVELOPMENT

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. 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 ]

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, RDMkit, Netherlands eScience center and practical experience.

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	A Research Software Engineer (RSE) is a professional with in-depth knowledge of one or more research fields and expertise in software development and methodology. To address research issues and find solutions within their field of study, RSEs concentrate on creating and/or maintaining research software.
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.

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