The “FAIR” data guiding principles aim to make research data easier to share and reuse, for greater scientific impact. FAIR stands for making data simple to Find, easy to Access, Interoperable across systems, and ready for future Reuse. FAIR is community-driven, encourages collaboration, sparks innovation, strengthens research integrity, and reduces participant burden, all while advancing more open and impactful science.

Here’s what you can expect:

· A basic introduction to the FAIR principles.

· A case example of implementing FAIR principles in practice.

· A discussion on how making data FAIR can benefit your projects and the broader scientific community.

By the end of this presentation, participants will be able to describe the FAIR principles, identify benefits and barriers to their implementation, analyze their own data practices in light of FAIR, and collaboratively generate potential solutions to overcome identified challenges.

About the Presenter - Justina Pociūnaitė-Ott

Justina Pociūnaitė-Ott is part of the Global Collaboration for Traumatic Stress (GCTS) FAIR data workgroup. During her postdoctoral fellowship at the University of Twente (The Netherlands), she has been working on intensive longitudinal on daily experiences of grief, investigating how people’s coping with bereavement fluctuates over time and how it is associated with their immediate environment. Because this kind of data is often challenging to acquire, the importance of sharing and reusing it became evident.

Contact the host of Making Research FAIR: Insights and Applications in Clinical Psychology