Metroline Step: Build the Team

STATUS: IN DEVELOPMENT

Short Description 

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 listed by expertise and by FAIR principle. 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, 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. Hence, assembling the right team is essential to meet your goals.  

Expertise requirements for this step 

TODO

How to 

[Mijke: Another RDMkit page on this: https://rdmkit.elixir-europe.org/dm_coordination ]

[Sander] Would it make sense that, if we mention roles in this section in other pages, these roles are actually specified in this page’s How to? We could even create hyperlinks to this page.

RDMkit has a nice section about Roles in Data Management (with more details than I copied below) [Mijke coordinated/wrote most of it this]

In this section, information is organised based on the different 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.

Roles:

• Data Steward/Data manager: Individuals responsible for managing and curating research or healthcare data within organizations 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”.

• Policy maker: Decision-maker involved in shaping data management policies that promote FAIR data practices.

• 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, analyzing, and sharing data as part of a clinical trial, research project or other scientific endeavors.

• Information Professionals: Librarians, archivists, and information scientists involved in organizing and preserving data assets.

• IT and Systems Administrators: Professionals responsible for maintaining data infrastructure and ensuring technical compatibility and accessibility for an organisation or department.

• Research Software Engineer: Research software engineers (RSE) in the life sciences design, develop and maintain software systems that help researchers manage their software and data. The RSE’s software tools and infrastructure are critical in enabling scientific research to be conducted effectively.

• 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.
  

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.

[Generic] 

Data FAIRification requires different types of expertise and should therefore be carried out in a multidisciplinary team guided by FAIR data steward(s). The different sets of expertise are on i) the data to be FAIRified and how they are managed, ii) the domain and the aims of the data resource within it, iii) architectural features of the software that is (or will be) used for managing the data, iv) access policies applicable to the resource, v) the FAIRification process (guiding and monitoring it), vi) FAIR software services and their deployment, vii) data modelling, viii) global standards applicable to the data resource, and ix) global standards for data access. A good working approach is to organize a team that contains or has access to the required expertise. The core of such a team may be formed by data stewards, with at least expertise of the local environment and of the FAIRification process in general. 

→ In list form, expertise required:

1. The data to be FAIRified and how they are managed,

2. the domain and the aims of the data resource within it

3. architectural features of the software that is (or will be) used for managing the data

4. access policies applicable to the resource

5. the FAIRification process (guiding and monitoring it),

6. FAIR software services and their deployment,

7. data modelling,

8. global standards applicable to the data resource

9. global standards for data access.

[RDMkit]

Perhaps: https://rdmkit.elixir-europe.org/dm_coordination

[Health-RI_FAIRification_Step_Report]

Expertise and Example Experts - Source: [De Novo]

Resource glossary

Tool/Standard # can be used to #

• Goal Modelling (see link) is a standard that can be used to represent goals that are connected to each other and it helps defining clear FAIRification objectives for both research question and process perspectives. 

• FAIR data point (see link) is a tool guarantees many FAIR principles and can be used to describe metadata completely in accordance to the  DCAT standard, you can create and publish metadata in the FAIR data point which is a searchable and indexable resource (see fair data index, every fair data point is indexed in the fair data index), 

• DCAT (see link) is a standard to describe metadata of, from detail to general levels: distribution, dataset, catalogue

• RDF (see link) extensible knowledge representation model is a way to describe and structure datasets

• Smart Guidance (see link) is a tool that defines the specific steps for RD registries data FAIRification

Semantic data model for  (e.g. Data  model for set of common data elements for rare disease registration, Data model for Omics data, data model for WHO Rapid COVID CRF, Data models from EBI in the ‘documentation’ links on this page http://www.ebi.ac.uk/rdf/)

Practical Examples from the Community 

Example team:

The VASCA FAIRification core team consisted of a local data steward, an external FAIR data steward, and an EDC system specialist. Throughout the project, additional expertise was consulted, such as a clinician specialised in vascular anomalies, the Institutional Ethical Review Board, FAIR software developers, and researchers. A full overview of the different kinds of expertise and which part of the FAIRification process they contributed to can be found in TableS1

Links to demonstrator projects. 

References & Further reading

Mijke Jetten, Marjan Grootveld, Annemie Mordant, Mascha Jansen, Margreet Bloemers, Margriet Miedema, & Celia W.G. van Gelder. (2021). Professionalising data stewardship in the Netherlands. Competences, training and education. Dutch roadmap towards national implementation of FAIR data stewardship (1.1). Zenodo. https://doi.org/10.5281/zenodo.4623713

Salome Scholtens, Mijke Jetten, Jasmin Böhmer, Christine Staiger, Inge Slouwerhof, Marije van der Geest, & Celia W.G. van Gelder. (2022). Final report: Towards FAIR data steward as profession for the lifesciences. Report of a ZonMw funded collaborative approach built on existing expertise (Versie 4). Zenodo. https://doi.org/10.5281/zenodo.7225070

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

[FAIRopoly] https://www.ejprarediseases.org/fairopoly/  

[FAIRinAction] https://www.nature.com/articles/s41597-023-02167-2 

[Generic] https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification  

Contributors 

Dena Tahvildari; Sander de Ridder; Jolanda Strubel; Bruna dos Santos Vieira; Mijke Jetten; Fieke Schoots; Ines De Oliveira Coelho Henriques; Shuxin Zhang; Alberto Cámara; César Bernabé; Joeri van der Velde