[James Wilson]

Collaborative session notes: https://docs.google.com/document/d/1Gb_WwPLnxWHEHle0LyEzilh-IBQrQbMQ_Uyrt83P9fk/edit?usp=sharing 

James A J Wilson, Introduction to the RDARI Interest Group (5 minutes)

Daniel Mohr and Björn Brötz, “Continuous Integration (CI) for research data — Experiments with adapting concepts from software development to research data management: an in-house repository” (10 minutes)

Fenella France and Andrew Forsberg, “Reining in Cultural Heritage Data APIs for Better Accessibility” (10 minutes)

Magdalene Cyra, “Comprehensible datasets – how an ELN infrastructure can support researchers” (10 minutes)

Holger Angenent, “Democratising FAIRification: Handling Collections Level Tools to Coalface Researchers”(10 minutes)

Lars Vilhuber,  “Improving reproducibility in the social sciences: Challenges” (10 minutes)

Group discussion (25 minutes)

Next steps and Way Forward (10 minutes)

Are you willing to hold your session at multiple times to accommodate various time zones?
No

Avoid conflict with the following group (1)
FAIR Data Maturity Model WG

Brief introduction describing the activities and scope of the group
The Research Data Architectures in Research Institutions Interest Group is primarily concerned with technical architectures for managing research data within universities and other multi-disciplinary research institutions. It provides insight into the approaches being taken to the development and operation of such architectures and their success or otherwise in enabling good practice.
An institution’s research data management infrastructure consists of more than just a data repository and discovery mechanisms. It includes the underlying storage technologies, the networking, hardware, system interfaces, authentication mechanisms, data brokers, monitoring platforms, semantic interoperability tools, long-term preservation services, high-performance and high-throughput computing facilities, data science platforms, and potentially many other technologies that process data and control the flows of data and metadata between systems. 
Seamless data interoperability and movement between different systems both local and in national or disciplinary services is a particular challenge at present, given the need to provide researchers with a smooth and efficient user experience – a key requirement for any research data service. Governance and policies, project management environments, and communications platforms are also vital elements in shaping and informing IT architectures, as is the management of business information associated with research.
This IG seeks to understand the various architectures used by institutions globally, identify pain points within those architectures, and learn from those who have overcome or avoided those pain points.
The general approach of this IG is to encourage discussion about architectures and enable interested parties to collaborate and learn from one another. Many institutions are at present planning and working towards overarching data management architectures, and there is a legitimate concern that without such a forum as is provided by this IG institutions will relive the same experiences and repeat the same mistakes as their peers.

Group chair serving as contact person
James Wilson

Meeting objectives
The session will consider approaches that institutions are taking to help researchers get their data and/or metadata in a shape that will help others access it, understand it, and build the confidence to re-use it in their own work.
We are interested in institutional enhancements to research workflows, rather than discipline-specific practices, and we are not concerned here with data repositories per se, which are well covered by other RDA groups.
We will hear a number of case studies of innovative practices being tried at a variety of research-supporting institutions and have time to discuss as a group how the principles involved could be applied to support researchers more broadly. Topics may include how institutions can improve research workflows, support data or metadata standardization, capture and communicate provenance information, enable data interchange, and help researchers prepare their data for openness.

Please indicate the breakout slot (s) that would suit your meeting
Breakout 1, Breakout 2, Breakout 3, Breakout 4, Breakout 5, Breakout 7, Breakout 8

Privacy Policy
1

Discussion