DIGITAL DATA COLLECTIONS FOR DISCOVERY-DRIVEN NEUROSCIENCE

The transition to open science has led to a growing volume of shared neuroscience data in different data repositories. While the amount and diversity of research data available is extensive, data reusability is often limited by a lack of appropriate metadata annotations according to the FAIR guiding principles (Wilkinson et al., 2016). The EBRAINS Research Infrastructure (RRID:SCR_019260) shares well-annotated datasets to facilitate data discovery and reuse across studies and modalities via the EBRAINS Knowledge Graph (KG; RRID:SCR_017612).

The EBRAINS KG currently indexes 1100+ heterogeneous datasets, annotated with standardised metadata following the openMINDS metadata framework (RRID:SCR_023173). We present two examples for building data collections via the KG Search functionality using a combination of multi-term text queries and faceted filtering to exemplify the potential of data driven workflows.

First, we explore the brain-wide distribution of calbindin- and parvalbumin-positive neurons in rodent brains, identifying a comprehensive data collection generated using the QUINT workflow (Yates et al., 2019). Related datasets are connected through metadata including species and techniques. Second, we collect BIDS-compliant macaque datasets, containing NIfTI files, enabling the combination of neuroimaging data from different laboratories.

Filtering and query options for experimental methods, content types and data organisation standards enable the selection of data for viewing and analysis using EBRAINS tools, highlighting their interoperability. These examples demonstrate how discovery-driven neuroscience is facilitated by FAIR annotated data across a range of studies.

Funded from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101147319 (EBRAINS 2.0).