FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards.

ABSTRACT: Modern AI supported research holds many promises for basic and applied science. However, the application of AI methods is often limited because most labs cannot, on their own, acquire large and diverse datasets, which are best for training these methods. Data sharing and open science initiatives promise some relief to the problem, but only if the data are provided in a usable way. The FAIR principles state very general requirements for useful data sharing: they should be findable, accessible, interoperable, and reusable. This article will focus on two challenges to implement the FAIR framework for human neuroscience data. On the one hand, human data can fall under special legal protection. The legal frameworks regulating how and what data can be openly shared differ greatly across countrie

SUBMITTER: Reer A 

PROVIDER: S-EPMC10065194 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature