A FAIR Workflow Guide for Researchers in Human Cognitive Neuroscience

In the ecosystem of open science, three key entities—knowledge, scientists, and data—face major challenges. First, the replication crisis across disciplines raises concerns about the fairness of generating scientific knowledge, which we term "unfair to science." Second, the push for scientists to publish vast amounts of data without ensuring its discoverability creates an "unfair to data '' situation. Third, even when data is shared, open scientists often lack recognition and incentives within the academic hiring system, making it "unfair for open scientists." To address these challenges, this project offers a proof-of-principle solution. We outline an initial roadmap aimed at realizing a fair vision for each problem. To make science fair: we advocate for an open research lifecycle from pilot testing to final experimentation that safeguards reproducibility. To make data fair: we present a domain-specific metadata template for human cognitive neuroscience using controlled, machine-readable vocabularies and ontologies which ensures data discoverability. To promote fairness for open scientists and to credit open science efforts, we introduce a dashboard that quantitatively visualizes various research outputs and author contributions, including pre-registration, data collection, code development, and funding acquisition. This dashboard can expose the nuance of the scientific process, and the diverse contributions, and in the future could form the basis for a FAIR metric that acknowledges open science practices within academia. Our roadmap focuses on human cognitive neuroscience but can be generalized across other disciplines.