RDA Groups

Introduction

The Earth, space, and environmental science communities are developing, through multiple international efforts, both general and domain-specific leading practices for data management, infrastructure development, vocabularies, and common data/digital services. This Interest Group will work towards coordinating and harmonizing these efforts to reduce possible duplication, increase efficiency, share use cases, and promote partnerships and adoption in the community.

A strategic element to maintaining a competitive advantage in research is by focussing energies in areas of innovation, which is facilitated by sharing of resources and support of inter-disciplinary collaborations at the national and international levels. The primary impediment to this resource sharing and collaboration is the lack of an effective FIM (Federated Identity Management) ecosystem.

The coordination of data infrastructure on various levels (country, continent, discipline, sector) is on the increase. So called “Open Science Commons” or “Data commons” provide a shared virtual space or platform that provides a marketplace for data and services.

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 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. 

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

FAIR research software skills and training curriculums need to be identified and incorporated into broader efforts to facilitate increased support and recognition for the qualified personnel needed to achieve open science.

Extensive work has been, and continues to be done on data interoperability at the technical and information domains. However, a large portion of the challenges in building interoperable information infrastructures are the result of the interplay between organisations, institutions, economics, and individuals.

Software source code plays a critical role in all fields of modern research, where source code is written and developed to address a variety of needs, like cleaning, processing and visualising data.

Software source code is a necessary component for research reproducibility and reusability. 

The vision of the Research Data Alliance (RDA) is that “researchers and innovators openly sharing data across technologies, disciplines, and countries to address the grand challenges of society.” The Mission of RDA is that it “builds the social and technical bridges that enable open sharing of data.”

Increasingly researchers who are not co-located are seeking to work dynamically together at various scales from the local to global using the internet to share data, models, workflows, best practice, publications, management and administration of their research etc. The Virtual Research Environments Interest Group (VRE-IG) seeks to build the required technical bridges, skills and social communities that enable global sharing and processing of data across technologies, disciplines and countries through the creation of shared online virtual environments. As these individual VREs grow, inevitably they need to also connect with major national research infrastructures.

The goal of the VRE-IG is to identify the technical issues to and - where known - share solutions that enable online access to data required to address issues that can range from local challenges (which are also potentially of direct relevance to researchers in other geographical areas or other research domains), to the research grand challenges currently being faced by society on global issues, e.g., societal impacts of climate change; sustainable cities; and environmentally sensitive utilisation of the scarce resources of our planet.