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. 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. In this table you will find:

the 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 in the table are based on 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 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
Researcher	A researcher obtains, processes, produces, deposits and shares research data,	Researcher with domain knowledge	Define FAIRification objectives Apply data semantics
Researcher		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.
Research software engineer	A research software engineer is an ICT expert who designs, implements, maintains and/or integrates services and software to enable FAIR and open science, ensuring the fulfillment of software quality, reproducibility and sustainability.
Infrastructure professional (IT and Systems Administrators)	An infrastructure professional is an ICT expert who manages and operates infrastructures and the necessary services for the storage, preservation and processing of data.
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.
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 steward (Data librarian, Data manager)	A data steward is an expert on the preparation and treatment of data including data selection, storage, preservation, annotation provenance and other metadata maintenance, and dissemination. Data librarians are professional library staff who are experts on RDM, using research data as a resource or supporting researchers dealing with data (description, archiving and dissemination). Other closely related roles will also be considered under this category. Details on this role in the team are described in a separate step Have a FAIR data steward on board.	FAIR data steward	Define FAIRification objectives Pre-FAIR assessment Apply data semantics
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	Define FAIRification objectives Apply data semantics
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.

ELSI expert	<official description? ELSI Servicedesk?> ELSI experts provide guidance and answers to the ethical, legal and social implications of research on personalised medicine and next generation sequencing Guidance and answers to the ethical, legal and social implications of research on personalised medicine and next generation sequencing Life science professionals, policymakers and patients are faced with ethical, legal and social questions around Personalized Medicine research. The ELSI Servicedesk website and ELSI experts answer frequently asked questions. On the helpdesk one of the ELSI experts will answer questions.

The table is based on information from The de novo FAIRification process of a registry for vascular anomalies, 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.	Define FAIRification objectives Apply data semantics
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 “(FAIR) data steward”. Details on this role in the team are described in a separate step Have a FAIR data steward on board.	Define FAIRification objectives Pre-FAIR assessment Apply data semantics
IT and Systems Administrators	Professionals responsible for maintaining data infrastructure(s) and ensuring technical compatibility and accessibility for an organisation or department.	Potentially: Transform and expose FAIR (meta)data
Policy maker	Decision-makers responsible for research data management policies that promote FAIR data practices within an institute.	Potentially: Obtain informed consent
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.	Pre-FAIR assessment
Semantic data modelling specialists	A semantic data modelling specialist 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.	(Define FAIRification objectives: FAIR experts??)
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.	Potentially: Get training

Expert	Description	Metroline Steps
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.	Potentially relevant in the future if we e.g. add a new step to the Metroline which involves archiving of the data
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>
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, analysing, 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.	Pre-FAIR assessment
Semantic data modelling specialists	A semantic data modelling specialist 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.	(Define FAIRification objectives: FAIR experts??)
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.	<this sounds super niche, maybe remove?>
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.	Potentially: Query (use) over resources
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.

A FAIR data steward is a data steward with specialistic FAIR skills/knowledge

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.

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.