fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience.

As data analysis pipelines grow more complex in brain imaging research, understanding how methodological choices affect results is essential for ensuring reproducibility and transparency. This is especially relevant for functional Near-Infrared Spectroscopy (fNIRS), a rapidly growing technique for assessing brain function in naturalistic settings and across the lifespan, yet one that still lacks standardized analysis approaches. In the fNIRS Reproducibility Study Hub (FRESH) initiative, we asked 38 research teams worldwide to independently analyze the same two fNIRS datasets.

A high-density diffuse optical tomography dataset of naturalistic viewing.

Traditional laboratory tasks offer tight experimental control but lack the richness of our everyday human experience. As a result many cognitive neuroscientists have been motivated to adopt experimental paradigms that are more natural, such as stories and movies. Here we describe data collected from 58 healthy adult participants (aged 18-76 years) who viewed 10 minutes of a movie (, 1966).