Within the life sciences more and more data is generated and research often requires sharing and combining data.
Personalized medicine is a prominent example where data from different disciplines need to be combined to determine the best treatment for an individual. Think about combining vastly different data from health records, pharmaceutical treatment history, research findings and dietary intake, to name a few.
To facilitate combining and reusing data, data should be FAIR or increasingly FAIR. This enhances its value. If data is FAIR, the computer, you and others know what it means -even years later.
The FAIR principles have been embraced by both the European Commission and the G20 and act as a guidance for ‘handling data’.
However, researchers themselves are often not sufficiently equipped or knowledgeable, or have the time to handle large amounts of data and make data FAIR.
This is when data stewards come in, they are professionally trained to ‘handle data’ and make data FAIR.
Beyond proper collection, annotation, and archival, data stewardship includes the ‘long-term care’ of research data, with the goal that they can be found and re-used in downstream studies.
Research Data Management
Adequate research data management (RDM) is crucial to ‘care for data’. In order to do this, researchers are urged to construct a proper data management plan (DMP) before the start of their research projects.
However, it is often not possible to write a complete DMP before the start of a research project. It should therefore be updated during the project to add the latest decisions and procedural changes. This process is sometimes called ‘active data management planning’. At the end of the project, the final data management plan will be part of the documentation of the publishable data sets.
Research data management and data stewardship are terms that are often used interchangeably.
DTL uses the term data stewardship to emphasize the ‘long-term’ ambitions that go well beyond the duration of the research project.