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Related Experiment Video

Updated: Mar 1, 2026

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

FAIR m-BIDS: Advancing brain data utilization through multimodal and FAIR principles.

Seyed Mohammad Mirhosseini^1,2, Hoda Naseri^1,2, Bahaareh Siahlou^2,3

¹Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.

Scientific Data

|February 27, 2026

Summary

This summary is machine-generated.

FAIR m-BIDS enhances multimodal brain imaging data sharing by assigning unique identifiers to individual data files. This allows seamless integration of diverse data types across studies, improving data findability and reusability for AI and neuroscience research.

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Area of Science:

Neuroscience
Data Science
Artificial Intelligence

Background:

Multimodal data is crucial for accurate AI and data science results.
Current Brain Imaging Data Structure (BIDS) limitations hinder multimodal data integration across studies.
Managing diverse data types from single subjects requires specialized structures.

Purpose of the Study:

Introduce FAIR m-BIDS (FAIR Multimodal Brain Imaging Data Structure) to overcome BIDS limitations.
Enable integration of multimodal data from the same individual across multiple studies.
Enhance FAIR principles (findability, accessibility, interoperability, reusability) for brain imaging data.

Main Methods:

Shift granularity from dataset level to individual data entities.
Assign a Global Unique Identifier Key (GUId-Key) to each brain data file.
Maintain compatibility with conventional BIDS standards.

Main Results:

FAIR m-BIDS allows selection and integration of data items from different modalities and studies.
Global identifiers facilitate tracking of anonymized subject data across datasets and modalities.
The new structure improves data findability, accessibility, interoperability, and reusability.

Conclusions:

FAIR m-BIDS provides a robust framework for managing and integrating multimodal brain imaging data.
The proposed structure supports advanced AI and neuroscience research by enabling flexible data utilization.
This advancement promotes more comprehensive and dependable research outcomes through enhanced data sharing and integration.