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Working Groups/Special Interest Groups

Special Interest Groups are composed of users and developers from across the INCF network working collaboratively to develop, refine, and/or implement community standards. Working Groups are composed of SIG members working on short-term funded projects that aim to achieve a concrete deliverable.


The aim of this working group is the harmonization of Common Data Elements (CDEs) for data discovery and metadata annotation.
This SIG aims to bring together experimentalists within the glial community with computational modellers, with objective to provide a forum to foster detailed interactions and advance astro-centric brain models.
This SIG will be a community dedicated to the creation and application of neuroinformatics technologies to address clinical and wellness challenges in aging
This SIG will coordinate common efforts for defining and describing cell types across neuroscience, to reduce duplicate efforts and to improve interoperability and reuse of cell type-specific data collected across groups.
This SIG aims to coordinate community efforts for the development of open, use case driven and shared validatable data models (schemas, vocabularies) to enable the FAIR principles (Findable, Accessible, Interoperable and Reusable) for basic, computational and clinical neuroscience (meta)data.
The SIG aim is to collect, compile, synthesize and distribute information from task forces working on separate projects but with reproducibility in neuroimaging as an overarching theme.
This SIG will coordinate interactions among researchers who are interested in reproducible research issues and open science. We will promote policies that support reproducibility, encourage better training in this area, and organize information about resources to make them more visible to the neuroscience community.
This SIG deals with the various tools and formats for creating and sharing representations of biological neuronal networks, and will work towards ensuring these are as interoperable and usable as possible for computational neuroscientists.
This SIG aims to develop standards and best practices for quality control of neuroimaging data, including standardized protocols, easy to use tools and comprehensive manuals