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Short
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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)
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The table below gives an overview of many roles a professional can have in research data management. You 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 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.
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Expert
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Description
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Metroline Steps
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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.
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Define FAIRification objectives
Apply data semantics
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FAIR Data Steward / data manager
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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.
Information Professionals
Librarians, archivists, and information scientists involved in organising and preserving data assets.
Role | Description | Usage | Metroline steps |
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Researcher (Scientist) | A researcher obtains, processes, produces, deposits and shares research data. | Researcher with domain knowledge |
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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 |
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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
Decision-makers responsible for research data management policies that promote FAIR data practices within an institute.
Potentially: Obtain informed consent
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??)
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.
Semantic expert (Metadata expert, interoperability expert) |
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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 |
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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 |
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To members of the Writing group: if the necessary expertise cannot be found in the table above, check the one below. If you need one of the roles described there, let Sander/Mijke/Jolanda know.
If you still cannot find a suitable role, tell us what role you need and we can discuss where/how it should be added.
Expert | Description | Metroline Steps | |||
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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. | ||
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, analysing, 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. |
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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. |
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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. |
| 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:
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Rewrite some descriptions that are copy-pasted → done?
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Expertise
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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…
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Played a role in interpretation of the data elements
In the FAIRification objectives step the following expertise is mentioned:
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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.
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…
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may need the experts described below.
Project manager. Knows the goals of the project and can help decide what team members are necessary to reach those goals.
HR. Involved when hiring new people.
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
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Training
Research Dream Team Toolkit: a resource for organising a workshop around the diversity of roles that contribute to research.
The Turing Way handbook contains several chapters with lessons and recommendations on research teams: E.g. Teamwork; Research Infrastructure Roles; Team manual, etc.
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