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:

• 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.

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.

• Patient advocate is also mentioned in De novo…

  • Played a role in interpretation of the data elements

In the FAIRification objectives step the following expertise is mentioned:

• 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.

Apply data semantics

• Data specialist: can help with understanding of data structure,

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.

[HANDS]

In short, the responsibilities of the key players are:

• Principle Investigator or research leader (=Principal Data Steward): responsible for research, hence for the data stewardship in a particular research project, but can delegate certain data stewardship tasks such as data management and FAIRification to dedicated data stewards (see below).

• Researcher involved in a project: responsible for the execution of good data stewardship.

• UMC Board: end responsible for research data stewardship within the institute.

• UMC: responsible for offering all researchers support in data stewardship, such as policies, training, tools, technical solutions and organisational support, for instance by appointing institutional data stewards and project data stewards.

• Institutional data stewards and project data stewards: offer advice on good data stewardship and certain data stewardship tasks may be delegated to these data stewards.

What are the responsibilities of the Principle Data Steward?

You:

• are accountable and responsible for your research data;

• are in control of the complete research data flow;

• collaborate with patient organisations throughout your research;

• reuse existing data when possible;

• protect research quality and reproducibility;

• protect the privacy and safety of study subjects;

• apply the FAIR Principles as much as possible;

• think ahead about rights of third parties, proprietary data and intellectual property rights;

• share your data responsibly.

What are the responsibilities of my UMC?

Your institution has a duty of care when it comes to data stewardship. Your UMC is accountable for having adequate policies (e.g., a Data Governance Policy), facilities and expertise around data management and data stewardship. It is your UMC's responsibility that you as a researcher are informed about these policies, facilities and expertise.

Your institute has:

• professionals that provide the procedures and technical systems for data stewardship (e.g., institutional/operational data stewards, data managers, IT-specialists, statisticians, protein sequence experts);

• institute managers, who govern and facilitate the professionals;

• supervisory bodies such as medical-ethical review committees and privacy officers;

• data collections from patients and citizens.

Responsibilities of the managers at your UMC:

• facilities for data stewardship (e.g., data protection, storage, interoperability);

• financial means for data stewardship and expert employees;

• organisation, policy, standard procedures, practical measures, etc.;

• training the employees who work with data.

Responsibilities of professionals that support data stewardship are

• to provide, advise and support the use of terminologies, IT-standards and e-infrastructure that promote data sharing, data integration, etc.;

• data curation and archiving.

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 → this one is actually not in our table…

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

• Research Dream Team Toolkit: a resource for organising a workshop around the diversity of roles that contribute to research.

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.

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