Skip to main content

FAIR workflows for neuroscience research

Difficulty level

An exciting development we are witnessing in neuroscience research is the increase in large-scale collaboration. But on the other hand, the field faces significant reproducibility problems which introduces great uncertainty in the interpretation of study results. More specifically, consciousness research inherits this same challenge, while also facing further limitations of the contrastive method put forward almost a decade ago. Such complications have impeded the search for the neural correlate of consciousness, and thereby put to question the validity of the theories of consciousness that were built on those findings. To address the question of what anatomical structures and physiological processes in the human brain give rise to consciousness, would require countless studies, and critically, the aggregation of data across them. Yet, the lack of infrastructure to aggregate results in a consequential way, poses great challenges for researchers to fully understand the extent of a research study - including the experimental context, the methodology, analysis, stability of the results and data. Development of FAIR workflows will address that need, unleashing the possibility to better understand the ‘hard problem’ of consciousness. This session was co-chaired by Helena Cousijn and Lucia Melloni.

Speakers: Helena Sousijn, Xiaoli Chen, Maria Praetzellis, Zefan Zheng, Tanya Brown


Topics covered in this lesson
  • Collaborative neuroscience research
  • FAIR principles / PID infrastructure
  • Metadata standards and templates for data and project
  • Implementing FAIR Workflows project