Speaker
Description
The FAIR DO Cookbook helps researchers to learn about FAIR Digital Objects (FAIR DOs) practically. It is guided by existing implementations and expert guidelines from HMC.
The FAIR (Findability, Accessibility, Interoperability, Reusability) principles aim to support and facilitate the reuse of data. They require the use of structured metadata and other important aspects in research data management. But for automation, machines do not only need to read this information, but to understand and act on it. The concept of FAIR Digital Objects (FAIR DO) aims to be a common layer for all (FAIR) data to achieve this machine-actionability. It offers an intentionally limited set of typed information to enable the machine to make decisions on the data objects:
- Can I (the machine) access it? Will access need human intervention?
- What kind of object is this? Can I open and read it? In which context was it created?
- What metadata exists for this object, and can I interpret it?
- Which actions can I perform on this object?
If a machine can answer such questions, this will further increase FAIRness as it will make it easy and reliable to build services (like research graphs or search indices) and tools for a wider range of data.
As the FAIR Data Commons group, we deal with the implementation of FAIR DOs for the Helmholtz Association. Therefore, we created the FAIR DO Cookbook to collect recipes and best practices. Issues, open questions, or suggestions can be reported in its GitHub Repository.
This research has been supported by the Helmholtz Metadata Collaboration (HMC) Platform, the German National Research Data Infrastructure (NFDI) and the German Research Foundation (DFG).
In addition please add keywords.
FAIR Digital Object, Recipes, Guidelines
| Please assign your poster to one of the following keywords. | Processes/Policies |
|---|---|
| Please assign yourself (presenting author) to one of the stakeholders. | Data Infrastructure Provider |
