Why is it useful?

Execution of DMP satisfying open science requirements

• SNSF expects all its funded researchers:
  
  
  • to store the research data they have worked on and produced during the course of their research work
  • to share these data with other researchers, unless they are bound by legal, ethical, copyright, confidentiality or other clauses
  • to deposit their data and metadata onto existing public repositories in formats that anyone can find, access and reuse without restriction, conformed with the Findable Accessible Interoperable Reusable (FAIR) principles
  
  
  Art 47 SNSF Funding Regulations and SNSF Open Research Data Policy

Advantages of adopting a standard

• Ease of sharing and reuse - No need to spend a considerable amount of time to understand data organization and adapt code to each researcher or lab.


• Automated processing pipelines can be built to analyze data


• Meta-analysis accross datasets become possible

How?

Keys towards FAIR neuroimaging data in Synapsy

• Standard to organize and describe neuroimaging data along with metadata: Brain Imaging Data Structure (BIDS) - https://bids.neuroimaging.io/


• Distributed data versioning and management system tailored to neuroimaging data: Datalad - https://www.datalad.org/


• Distributed storage of neuroimaging data accross the different institution servers with access controlled by PIs

The Road Map

Step 1: Understanding how datasets are organized in each lab

Step 2: Creation of BIDS datasets for raw data

Step 3: Adoption of Datalad

Step 4: Creation of dataset catalog

Step 5: Dataset publication

What is BIDS?

Specifications to organize and describe neuroimaging data that complies with the FAIR concept

https://doi.org/10.1038/sdata.2016.44

Comprehensible organization and naming with well-accepted formats

from https://bids-specification.readthedocs.io

Consistent image acquisition metadata

Consistent dataset documentation and experiment metadata

Why BIDS?

Neuroimaging data standard originating from MRI

• Arrived at the time researchers are incentized to share more their data
  
  Results from www.webofknowledge.com (Date: March 07, 2021)
• Focused on community, inter-operability, simplicity, extensibility and evolution
• Supported by the International Neuroinformatics Coordinating Facility (INCF) and encouraged by the COBIDAS report on Best Practices in Data Analysis and Sharing in Neuroimaging using MRI

Allow use to make Synapsy neuroimaging data FAIR and ready to be compliant with the SNSF requirements

Make data fully understandable by itself thanks to its consistent metadata and documentation

Facilitate data sharing between lab members and collaborators part of Synapsy

Make code interoperable between projects, lab members, and collaborators part of Synapsy

Very little effort to deposit neuroimaging Synapsy neuroimaging data to open databases

Databases such as OpenNeuro, LORIS, COINs, XNAT, SciTran and others accept and export datasets organized following BIDS

Benefit of dedicated and well documented tools

For dataset query

• In Python: https://github.com/bids-standard/pybids

  
  >>> from bids import BIDSLayout
  >>> layout = BIDSLayout('/home/data/ds000117')
  >>> layout.get_subjects()
  									



• In Matlab: https://github.com/bids-standard/bids-matlab

  
  >>> BIDS = bids.layout('/home/data/ds000117');
  >>> bids.query(BIDS, 'subjects')
  									

For analysis

A number of processing pipelines handling BIDS datasets (BIDS Apps) are available, ranging from quality control to preprocessing, connectome mapping, and statistical analysis

https://bids-apps.neuroimaging.io/apps/

How does BIDS apply for EEG?

BIDS extension for EEG

https://doi.org/10.1038/s41597-019-0104-8

An overview

Follow the general BIDS data structure originating from MRI with some modifications to accommodate EEG data types

Figure adapted from https://doi.org/10.1038/s41597-019-0104-8

EEG tools supporting BIDS