Recognised & Endorsed

Purpose:

Lack of interoperability between tools/e-infrastructures presents a significant barrier to streamlining processes throughout the research lifecycle. These gaps prevent the comprehensive collection and incorporation of research data and metadata into the research record captured during the active research phase. Furthermore, it limits the scope for passing this data and metadata on to data repositories, thus undermining FAIR data principles and reproducibility.

The FAIR for Machine Learning (FAIR4ML) Interest Group will enable community members to discuss the various aspects of FAIR as applied to Machine Learning, looking both at domain specific and domain-agnostic use cases, and creating task forces and working groups as needed for specific guidance documents, recommendations, definitions and technical specification to that effect. The overall aim is to foster collaborations among researchers and developers who are interested in making machine learning (data, models, workflows, etc.) FAIR, along with those who contribute to the infrastructure and policies that support this.

The Neuroimaging Data WG fulfils the RDA’s mission to build the social and technical bridges that enable open sharing and re-use of data in the domain of neuroimaging. The WG envisions a neuroimaging research landscape in which knowledge is generated in a reproducible fashion (in terms of data, analysis and computation) and coupled with the ability to reuse and extend these studies by others in the community.