The shift towards open science enables the exploration of an increasing variety of neuroscience datasets. The diversity of data formats, ways to organize and describe data, however, pose a significant challenge for reproducing and reusing scientific outputs. To address this, we developed an efficient data curation workflow for sharing FAIR and integrated neuroscience data. This workflow is well documented and continuously refined to meet researchers' needs.The aim of the data curation process is to build the best possible metadata representation of the data for discoverability and reuse. Accordingly, our workflow involves steps to organize, describe, and annotate data in a standardized manner. We have developed tools and methodology to equip the data with relevant metadata in compliance with openMINDS (RRID:SCR_023173), an open-source metadata framework. The standardized metadata representation makes it possible to publish the data in the context of other available research data and experimental or computational methods via the EBRAINS Knowledge Graph ( This improves the findability and accessibility of the data, and enables further processing options. To ensure a good understanding of the data and facilitate its reuse, a human-readable data descriptor outlining data acquisition methods, file organization, and software is added. We demonstrate the most recent version of our workflow using examples of rodent and human neuroscience data published on ebrains.eu. Our results are openly accessible and fully integrated into the EBRAINS Data and Knowledge services.Funded by the European Union’s Research and Innovation Program Horizon Europe Grant Agreement No. 101147319 (EBRAINS 2.0)