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Towards FAIR Data Management

  • 26 October - 10 November 2022
  • -
  • Methodology
  • 1.0 ECTS

FAIR stands for “Findable, Accessible, Interoperable, Reusable” and is becoming increasingly important for sharing data, especially in research. We discuss the incentives and best practices of FAIR data management. Why should the data be shared in a FAIR way? What stands behind this concept on the technology side? How does FAIR data look like, how to use, and how to create it? Which relevant platforms and tools exist and how to use them? This course will address these questions by providing relevant information and possibilities to get skills to work with and implement FAIR data in practice.

We will work with hands-on, and you as a participant are welcome to bring your own use case, e.g. a dataset which you want to FAIRify. We will make the following steps (in the class and as homework):

  • Status quo: Assessment of the FAIRness level of the data (using existing metrics e.g. FAIR data maturity model of Research Data Alliance) in the use case.
  • Objectives in FAIRness: Where would it be (most) desirable to be for this use case in terms of data FAIRness? Why?
  • Roadblocks: What are the main obstacles now in reaching the desirable level of FAIRness? How to overcome them?
  • Recommended actions (both technical and non-technical actions): What should be done to the data in the use case to reach the desirable objective in FAIRness? How should this be done? With which methods and tools?
  • Success criteria: How can it be ensured/checked that the recommended actions are successful? Eventually, we will be creating new FAIR datasets or be raising level of FAIRness of existing datasets, employing relevant state-of-the-art tools.

The course is open to participants from all disciplines, and is a combination of lectures and hands-on sessions. A brief introduction to FAIR data will be provided, however, most of the course focuses on technical and non-technical aspects of FAIR data management. Time is reserved to discuss and work with datasets used and produced by the participants.

  • PE&RC, Wageningen University