IN-DEPTH METADATA ANNOTATION WORKFLOW FOR FAIR SCIENCE AND MACHINE-ACTIONABLE ANALYSES OF COMPLEX EXPERIMENTAL NEUROSCIENCE DATA

Neuroscience is in a pivotal period in the establishment of new standards in data sharing. Cutting-edge techniques are increasing quantities of increasingly-complex data, which poses major challenges for data sharing and reuse. Prominent among these challenges is a lack of well-described, in-depth metadata, needed to understand, analyse, or integrate shared research data. This is a major barrier to data reuse and to application of artificial intelligence(AI) tools to accelerate research. Therefore, there is a critical need to enrich existing and future data with standardized, in-depth metadata, in accordance with the FAIR principles¹.
To meet this need, we have developed a streamlined workflow for annotating data with machine-actionable metadata, accessible to AI through linked data systems. This approach relies on technique-specific in-depth metadata relationship diagrams for electrophysiology, neuroimaging and microscopy data.
While applicable to any data management system, we have implemented this workflow for the EBRAINS² Knowledge Graph³ using the openMINDS⁴ metadata framework, and demonstrated how the in-depth metadata enable machine-actionable reuse, including model building, and data analysis.
Here we present the workflow, focusing on its user-friendly interface for in-depth metadata annotation of previously published datasets. We also show how this approach can be adapted to other data sharing services.
In-depth metadata annotation workflows are crucial to deliver machine-actionable metadata for complex experimental neuroscience data. This is essential for data reuse through novel analyses, meta-analysis, model building, etc., fostering new discoveries and accelerated by new technologies such as AI.
[1] Wilkinson.,et al 2016 (DOI:10.1038/sdata.2016.18)
[2] https://doi.org/10.25504/FAIRsharing.XO6ppp
[3] EBRAINS Knowledge Graph (RRID:SCR_017612)
[4] https://doi.org/10.25504/FAIRsharing.6ac6aa