This course is part of a series of courses provided by Datability to offer the participant an efficient introduction to good data management practices based on the FAIR principles. The goal is to provide an understanding of the FAIR principles and some of the skills needed as a data steward. Some hands-on exercises will be included to enable participants to get a better grip of the steps needed to provide datasets that are suitable for discovery and reuse.
In this module we provide an introduction to the main topics required to become a FAIR data steward, including a thorough introduction to FAIR data management and the importance of preparing data and metadata for reuse. An essential part of FAIR relates to supporting the ability of artificial agents to interpret discovered information and act accordingly. We look at how vocabularies and certain semantic attributes can help facilitate this. There will be some practical examples and exercises throughout the course.
The main topics of the course are as follows:
- Introduction to FAIR Data Stewardship;
- Detailed explanation of the FAIR principles;
- Introduction to the FAIRification process on metadata and data
- Introduction to semantic interoperability
- Introduction to Semantic Web and Linked Data technologies
- Introduction to the tools to be used in the FAIRification process (FAIR Data Point)
Schedule:
Session 1: May 16, 13-16 CEST
Session 2: May 22, 13-16 CEST
Session 3: May 23, 13-16 CEST
Session 4: May 30, 13-16 CEST
Session 5: June 1, 13-16 CEST
Session 6: June 2, 13-16 CEST
The course is given online via Zoom.
Tutor: Luiz Bonino (University of Twente, GO-FAIR Leiden)
Programme:
Session 1:
Introduction to the training, goals, activities and participants
Introduction to FAIR, the movement, its motivation and latest developments
FAIR principles explained: detailed discussion of each of the principles, their original intentions and implementation consequences
Session 2:
The FAIRification process and planning
Practical session: FAIR evaluation and FAIRification planning: attendees will work in groups to evaluate the current FAIRness of their chosen datasets and planning the FAIRification to improve their FAIRness
Session 3:
Introduction to semantic interoperability
Introduction to Semantic Web and Linked Data
Session 4:
Introduction to metadata and metadata modelling
Practical session: semantic metadata modelling: attendees will work in groups to define a semantic metadata schema for their chosen datasets
Session 5:
Semantic data modelling
Practical session: semantic data modelling: attendees will work in groups to define a semantic data model for their chosen datasets
Session 6:
Metadata and data publication
Practical session: final FAIRness assessment and final discussions
Prerequisites
No particular requirements.
NOTE: The organiser reserves the right to cancel the event at the latest by May 8, 2023 if registrations have not exceeded 14 participants. In such case registrants will be refunded the registration fee to the same payment card they used when registrering.
Course material:
Slides from the lectures will be provided to the participants.
We can recommend the following as supplemental/optional material:
Barend Mons 2018. Data Stewardship for Open Science: Implementing FAIR Principles. 1st edition, CRC Press.
This event is offered and coordinated by Datability.
No exam or test will be offered as part of this course.
