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:
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. Assembling the right team is essential to meet your FAIR objectives.
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 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 is based on De Novo, RDMkit, Netherlands eScience center and practical experience.
Expert | Description | Metroline Steps |
|---|---|---|
Domain expert | Domain experts have deep knowledge and expertise in a particular domain. They have a deep understanding of the intricacies, challenges, and nuances of their field. | FAIRification objective |
FAIR Data Steward/Data manager | Individuals responsible for managing and curating research or healthcare data within organisations 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. They are in charge of setting up user access, data validation checks and electronic case report forms in the EDC system. They offer technical help to researchers and ensure data integrity and regulatory compliance. | |
Information Professionals | Librarians, archivists, and information scientists involved in organising 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. | |
Decision-makers responsible for research data management policies that promote FAIR data practices within an institute. | ||
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. | |
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 data modelling specialsit is primarily responsible for designing and implementing semantic data models. These models are a representation of knowledge and concepts in a structured format that a computer can understand. They use tools like RDF, OWL and SPARQL. | |
Senior expert of standards for automated access protocols and privacy preservation | Has expertise with standards for protocols for secure and automated access to sensitive data while preserving privacy. They guarantee adherence to pertinent guidelines and laws, such the GDPR. | |
Senior healthcare interoperability expert | A healthcare interoperability expert is responsible for ensuring smooth communication and data interchange between various healthcare systems and applications, including electronic health records and medical devices. In compliance with legal requirements, they create, put into practice, and uphold interoperability standards and protocols that facilitate the safe and effective interchange of patient data. | |
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 → done?
Verify Expertise already mentioned in step exists here
Hier nog naar kijken: https://research-dream-team-toolkit.readthedocs.io/en/latest/scenario.html
Zie onderaan
Even more roles can be found on the website of the dream team toolkit.
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.
Pre-FAIR assessment:
Data stewards. Specialist who can help filling out the surveys and questionnaires.
Research Software Engineers. Specialists who can help running some of the specialised software.
Apply data semantics
Data specialist: can help with understanding of data structure,
Domain expert: can help with understanding of data elements.
FAIR experts
It is the responsibility of a FAIR (Findable, Accessible, Interoperable, and Reusable) specialist to ensure digital assets and data follow the FAIR principles. Tasks involve developing and implementing plans to enhance data discoverability via metadata enhancement and standardised identifiers, ensuring accessibility by instituting appropriate data storage and access methods and fostering interoperability by adopting shared data standards and formats.
ELSI experts
Assessing and handling the ELSI (Ethical, Legal, and Social Implications) components of research in domains like genetics and medicine falls under the purview of an ELSI specialist. They offer direction on how to handle difficult ethical dilemmas involving data sharing, privacy, informed consent, and possible societal repercussions of research findings.
Table from the dreamteam source
Areas of expertise | Roles |
|---|---|
Privacy and security |
|
Ethics |
|
Commercialisation / valorsation |
|
Citizen science / societal engegement |
|
Communication, education and outreach |
|
Data and software |
|
Project administration |
|
Project funding |
|
Infrastructure and instrumentation |
|
Expertise requirements for this step
To be able to define your team, you need to know the goals and steps for your project.
Project manager
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.
Tools and resources on this page
Add the tools and resources mentioned on this page. This should be a list of usable content and does not include textual resources such as journal references.
Training
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.
Further reading
Plan is om deze sectie weg te laten en alles te verwerken de teksten
Resource below is about organising a workshop. Could be more relevant for one of Fieke’s resources somewhere?
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