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Short description
In this service you are introduced to existing communities that are of interest for data stewards and researchers. For data stewards there are profession specific communities. Next to that there are communities for specific health-related domains. Furthermore, we give you a guideline on how to set up a new community or improve an existing community.
Why is
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this step important
Research funding organisations, like ZonMw, recommend researchers and data stewards to collaborate and join or start a community within the research domain of their project. Their roles and expertise are complementary, as researchers provide knowledge about their research domain, while data stewards provide expertise on data FAIRification.
Such a community is fruitful to exchange knowledge and learn from each other, and to make improve the interoperability of data within the domain by making joint decisions on FAIRification: writing FIPsFAIR Implementation Profiles, decide on controlled vocabularies and metadata schemas.
How to
Step 1
Data stewardship plays an essential role in meeting the requirements of funding organisations. Therefore, the role of the data steward is an important one. For more information, see Metroline Step: Have a FAIR data steward on board.
FAIR Data data stewardship is a relatively new profession that emerged to support researchers in data handling before, during and after a research project. Data stewards are trained to make data more valuable by making them reusable for future research (and other purposes) after the project ends. They can also advise researchers on providing access to their data. Increasingly, data stewards introduce new practices to make data as FAIR (Findable, Accessible, Interoperable, Reusable) as possible and to advise researchers on data sharing. With the support of data stewards, data gain more value, as they become reusable and available for future research after a research project ends. To get a general idea on the backgrounds and experiences of professionals working with FAIR in the health domain, visit the Health-RI Data champion portfolio.However, because it is a new profession, there is no consensus on what exactly a data steward’s responsibilities and tasks are. Work has been initiated on a national level on defining the roles relevant for data stewardship, funded by ZonMw and co-funded by the National Programme Open Sciences (NPOS) and ELIXIR-NL. One of the concrete outcomes is the development of the NPOS/ELIXIR Data Stewardship Competency Framework.
Step 2
Joining and forming communities is an important tool to strengthen the data steward profession by sharing experience and knowledge.
How to
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There are already active communities out there, some specifically for the profession of data steward and for a range of health-related domains. In the next sections we give you some examples of communities you could join.
Data Stewards and data management communities
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You can engage with your fellow data stewards via these communities:
Data Stewardship Interest Group (DSIG): a domain agnostic, international community hub for data stewardship that enables informal and inclusive knowledge and experience exchange that meets online monthly. Their way of working is shared in detail in this article. Every two weeks, together with the TDCC (Thematic Digital competency centres), they “put the spotlight on” a data steward and other data professionals.
Health-RI Data Stewards community (DSC): national community hub for health and life sciences data stewards that facilitates sharing experiences and collaboration. National community hub for health and life sciences data stewards that facilitates sharing experiences and collaboration.
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Institutional communities such as the Leiden University Research Data Management Community ELIXIR RDM Community
Domain specific communities
In health research and innovation, many researchers and data professionals from different specific domains already come together to share their experiences and collaborate, for instance by defining FAIR Implementation Profiles (FIPs) for the domain.
Here are a few examples of domain specific communities:
Also, just ask around, your colleagues will possibly know of communities related to your research domain.
How to
Build a new or improve an existing community
Step 3
For researchers who receive a research grant, it is recommended to join or start a project specific community involving both researchers with knowledge on the relevant research domain, and data stewards with FAIR data-expertise. An example of such a community is the “ZonMw COVID programme”: a data champions group consisting of the project’s PIs and data stewards. This community was facilitated by the Data Stewards Interest Group (DSIG) and GO FAIR Foundation. The community collaborated during the programme, shared experiences, learned from each other and used their joint knowledge to produce and use FAIR COVID data by bringing together their project metadata with domain specific machine actionable metadata schemes, exposed in the Health-RI portal.
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If you would like to dive deeper, the following resources will help you on your way:
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Community Canvas is a framework that will help you build and run a community. It has three big sections which are equally important,
Identity: Strong communities have a clear and explicit sense of who they are, why they exist and what they stand for.
Experience: what does your community offer to its members (shared experiences and content, but also rituals, traditions, rules and roles)
Structure: operational elements of running a community. These are often neglected, and consistency is key
A nice starting point is the Community Canvas “Minimal Viable community” sheet, which covers the most important questions for these sections .
The Turing way’s “Guide to planning a community”
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. The Turing way is an open source, open collaboration, and community-driven project that aims to make data science accessible, comprehensible and effective for everyone. They have an extensive “Guide to planning a community” which provides a checklist with extensive additional information on how to run a collaborative project.
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Expertise requirements for this step
Describes the expertise that may be necessary for this step. Should be based on the expertise described in the
Practical examples from the community
Examples of how this step is applied in a project (link to demonstrator projects).
Training
Further reading
Community Building for FAIR Data - Leiden community building workshop
Community building via the Data Stewards Interest Group (DSIG)
Engaging Researchers with Research Data Management: the cookbook
Manifesto for community management by the CMC (Community Managers Club)
Authors / Contributors
Jolanda Strubel
Pauline L’Hénaff
Fieke Schoots
Margreet Bloemers
Ellen Carbo
Mijke Jetten
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Suggestions
Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.