WGs Producing deliverables (~6-12 months after RDA endorsement)

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06 Jul 2022
WG

Artificial Intelligence and Data Visitation (AIDV) WG

Status: 
Recognised & Endorsed

Mission The mission of the AIDV-WG is to contribute to building the ethical, legal, social, and technical frameworks and bridges enabling the open sharing and re-use of data in the framework of Open Science.

 

Vision The vision of the AIDV-WG is to bring together expertise across disciplines and regions to ensure ameliorate the use of AI and DV in research and innovation across technologies and sectors to address the grand challenges of society.

 

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27 Jun 2022
WG

Neuroimaging Data WG

Status: 
Recognised & Endorsed
Chair (s): 

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.

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03 Dec 2021
WG

Data Repository Attributes WG

The Data Repository Attributes Working Group seeks to produce a list of common attributes that describe a research data repository and to provide examples of the current approaches that different data repositories are taking to express and expose these attributes. The working group will produce two documentary outputs over the course of 18 months and four Research Data Alliance (RDA) plenary meetings; they are: 

1) a list of common descriptive attributes of a data repository with 

     a) a definition of each attribute, 

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20 Oct 2020
WG

Data Granularity WG

Status: 
Recognised & Endorsed

Welcome to the RDA Data Granularity Working Group

 

Purpose

The efficient and effective reuse of data requires that users, be they humans or machines, be able to find and access data at fine levels of granularity.  The WG will explore key questions and collect and share valuable information for how to best support data granularity, providing guidance to help data professionals to determine the best level of granularity for user discovery, access, interoperability and citability. 

 

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