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. 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 and descriptions in the table are adjusted from 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 from the table 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 (Scientist) | A researcher obtains, processes, produces, deposits and shares research data. | Researcher with domain knowledge |
|
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. | Data scientist | |
Research software engineer | A growing number of people in academia combine expertise in programming with an intricate understanding of research. These Research Software Engineers may start off as researchers who spend time developing software to progress their research or they may start off from a more conventional software-development background and be drawn to research by the challenge of using software to further research. For an elaborate overview of this role see the aforementioned NPOS report, chapter 4. | Research software engineer | |
Infrastructure professional (IT and Systems Administrators) | An infrastructure professional is an IT expert who manages and operates infrastructures and the necessary services for the storage, preservation and processing of data. | Infrastructure professional | |
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. | Trainer | |
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 curator | |
Data steward (Data librarian, Data manager) | A person responsible for keeping the quality, integrity, and access arrangements of data and metadata in a manner that is consistent with applicable law, institutional policy, and individual permissions. Data stewardship implies professional and careful treatment of data throughout all stages of a research process. A data steward aims at guaranteeing that data is appropriately treated at all stages of the research cycle (i.e., design, collection, processing, analysis, preservation, data sharing and reuse). Details on this role in the team are described in a separate step Have a FAIR data steward on board. | FAIR data steward |
|
Semantic expert (Metadata expert, interoperability expert) |
| ||
Data steward with EDC knowledge | |||
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 |
|
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. | Policy maker | |
ELSI expert | ELSI experts provide guidance and answers to the ethical, legal and social implications of research. | ELSI expert |
|
To be decided:
Expert | Description | Metroline Steps |
|---|---|---|
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> | |
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). |
A FAIR data steward is a data steward with specialistic FAIR skills/knowledge
[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.
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