RDA Groups

Building on the work of the Data Rescue IG, the Data Conservation IG will both expand scope to all types of data (both digital and analog), and focus on decision-making topics including:

• How are data sets prioritized for rescue or conservation?
• How are risk factors defined, assessed, and/or categorized?
• How is funding prioritized and/or secured?

More information will be added as the group moves forward. 

Good Research Data Management (RDM) is a key component of research integrity and reproducible research. Consequently, funding bodies, governments, research institutions and other agencies have emphasised the value and importance of good data management. However, discussions about data management and sharing are sometimes limited to librarians, data professionals, or researchers who are already passionate about data stewardship and open science.

The proposed Global Water Information Interest Group (GWIIG) will serve as a platform leading to formation of RDA working group; as a venue for communication among RDA WGs and IGs focused on both technical eInfrastructure aspects (e.g. brokering; metadata and catalogs, dynamic data citation) and on related research domains (e.g. geosciences, agriculture, biodiversity); and as a coordination mechanism across a wide group of water information stakeholders, in particular those not previously involved in the OGC/WMO water standardization work.

Preparing for the 16th Plenary

The IG on Surveying Open Data Practices draws attention to the growth of global and national-level surveys as a lens to characterize how researchers’ practices and perceptions toward open data shape the knowledge ecology in different jurisdictions and disciplines.

The proposed Interest Group on “Health Data” (HD-IG) is a long-term initiative in the framework of RDA. It follows a rather successful BoF Session during the 6^th RDA Plenary Meeting in Paris, which was attended by over 35 researchers and professionals from diverse backgrounds, who discussed several relevant issues, expressed significant interest in forming the proposed Interest Group, and helped shape its focus as presented here.

The Linguistics Data Interest Group plans to identify, prioritize, and get to work on data challenges across the Linguistics domain. As a first step, this new group will focus P10 time on developing the discipline-wide adoption of common standards for data citation and attribution, and to improve research data management training in the discipline.

Mission

This is the webpage of the RDA Interest Group on professionalising data stewardship. The idea to create an interest group was raised in a BoF session during the 14th Plenary (Helsinki), and a first informal group of interested people was established. At the 15th Virtual Plenary, as second BoF session was organised.

This IG starter out as a Working Group, to explore issues related to scientific research data sets that contains human subject information, as well as related datasets that have the potential to be combined in a way that can expose private information. The goal of the group is to develop a framework for how researchers and repositories should appropriately manage human-subject datasets, to develop a metadata set to describe the privacy-related aspects of research datasets, and to build awareness of the privacy implications of research data sharing.

Context and purpose

Engineering comprises a vast span of sub-disciplines including for example chemical, civil, electrical, and mechanical engineering. Traditionally, engineering disciplines apply scientific knowledge and empirical evidence to finding (innovative) solutions in areas that range from design, creating and building to production of software and innovative materials. Therefore, research in engineering disciplines is highly multi-disciplinary and works in close collaboration with industry.

Sensitive data is increasingly used for research purposes. However, there are a number of barriers which need to be overcome before sensitive data can be fully operationalized in the research context. Chief among these is that not all sensitive data is alike, with significant disciplinary variation in how sensitive data is defined, linked, managed, stored, and reused. Additionally, common approaches to data optimization are not always appropriate for sensitive data, such as the relationship between the FAIR and CARE principles for data management. Nonetheless, sensitive data has significant

Update: In the immediate much of our disucssions are happening on this Slack channel, to join see this link