Towards FAIR Data Management

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Towards FAIR Data Management

To be announced


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.

Course set-up

The course is spread across three days in two consecutive weeks. Each day, the morning is spent in class, followed by an afternoon of homework/self-study.

  • Day 1: Getting an understanding of the concept of FAIR data: technical foundations, challenges and opportunities, motivations for FAIR data use, research data infrastructures.
  • Day 2: Introduction of the datasets, research challenges and benchmarks: semantic annotations, can one say if and when the data is FAIR?
  • Day 3: Making data FAIR: creation and publication of own FAIR datasets, overview and selection of appropriate tools and platforms, interlinking of datasets, upgrading research practices towards broader and better use of FAIR data. What needs to be done so that more FAIR data gets produced and employed.
Useful links
  • Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ... & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), 1-9.
  • FAIR Data Maturity Model Working Group (2020). FAIR Data Maturity Model: specification and guidelines. Research Data Alliance.
  • Jaradeh, M. Y., Oelen, A., Farfar, K. E., Prinz, M., D'Souza, J., Kismihók, G., ... & Auer, S. (2019, September). Open research knowledge graph: next generation infrastructure for semantic scholarly knowledge. In Proceedings of the 10th International Conference on Knowledge Capture (pp. 243-246).
  • FAIR DATA STATION:; contact: Dr. Jasper Koehorst
  • Easy Questionnaire Tool: (in Comfocus H2020 project), (login); contact: Robbert Robbemond
  • SciKGTeX - A LATEX Package to Semantically Annotate Contributions in Scientific Publications:; contact: Christof Bless
  • Dynaccurate Semantic Interoperability solution:; contact: Dermot Doyle
General information
Target Group The course is aimed at PhD candidates, postdocs, and other academics who want to gain knowledge about FAIR data and improve FAIRness of their data. It is desirable that participants are familiar with basic research data management (e.g. by having attended the Research Data Management (RDM) course given by Wageningen Graduate Schools and WUR Library). Knowledge of Web and/or Linked Data technology is a plus.
Group Size Min. 15, max. 20 participants
Course duration 3 days; mornings with lectures and discussion, afternoons self study/homework
Language of instruction English
Frequency of recurrence Depending on interest
Number of credits 1.0 ECTS
Lecturers Dr. Anna Fensel (WDCC and CHL, Wageningen University), with further involvement of lecturers that have own tools for making the data FAIR (see the section “Useful Links”)
Prior knowledge No prior knowledge is assumed
Location To be determined
More information

Drs. M van Heist (PE&RC)
Phone: +31 (0)317 489131 or (0) 6 28521546

Registration of interest

At this moment, this course is not scheduled yet. However, if you register your interest in this activity below, we will inform you as soon as the course is scheduled and registration of participation is opened.