Yaroslav O. Halchenko

Nipoppy standardizes neuroimaging data workflows to enable reproducible and comparable multi-center data sharing.

The paper presents a large-scale multimodal fMRI dataset combining naturalistic stimuli with diverse cognitive tasks to enable comprehensive cognitive neuroscience research.

This paper extends the BIDS standard to include eye-tracking data and metadata, improving organization and transparency for gaze position and pupil recordings.

The paper shows that behavioral models of action purpose and social content better predict neural representational geometry than other models in the action observation network.

Neurodesk provides a portable and reproducible neuroimaging analysis platform using software containers to enable accessible and flexible data analysis pipelines.

The paper introduces BABS, a Python package for reproducible and scalable neuroimaging data processing using BIDS Apps and HPC systems.

The paper introduces the Individualized Neural Tuning (INT) model, which provides a fine-grained, reliable, and generalizable measure of brain functional organization across individuals.

This paper describes OME-Zarr, a cloud-optimized bioimaging file format developed through community collaboration to improve scalability and data access.

TemplateFlow provides a FAIR framework for sharing and managing multi-scale, multi-species brain models to improve reproducibility in neuroimaging.

The paper provides a guide to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem, its tools, and resources for analyzing brain cell types across species.

OpenNeuro is an open archive for sharing brain imaging data following FAIR principles, hosting over 600 datasets from 20,000 participants across species and modalities, with over 150 publications citing its data.

The paper generates a multimodal cell census and atlas of the mammalian primary motor cortex, revealing a unified molecular genetic landscape and cross-species taxonomy of cell types.

The paper introduces a benchmark dataset of fMRI data from human subjects listening to naturalistic stories for evaluating language and narrative comprehension models.

This paper introduces fMRIPrep, a tool for preprocessing task-based fMRI data, and demonstrates its integration into a workflow for statistical analysis.

This study presents fMRI data from participants viewing naturalistic animal videos in two task contexts, showing how task demands affect neural representations of complex stimuli.

This study uses fMRI and multivariate pattern analysis to show that familiarity and face identity are represented in overlapping and distinct regions of the core and extended face processing systems.

The paper discusses the importance of sharing computational code in neuroscience and highlights challenges in current practices.

The paper investigates how attention affects the discriminability of distributed neural representations for behavior and taxonomy in humans.

The paper introduces BIDS, a standard for organizing MRI datasets to improve data sharing and processing.