The Research Data Culture Conversation

A group of research intensive universities have engaged in a conversation over the last few years aiming to understand the nature of a sustainable and effective research data culture, under the banner of the Research Data Culture Conversation.

SOME KEY PROPERTIES OF THE SITUATION ARE AS FOLLOWS:

Institutions will increasingly coordinate and invest in large-scale infrastructure to support the growth in the creation and use of research data and to ensure policy compliance on retention, access and disposal.
The efficient treatment of data depends on information and metadata not readily and systematically available, and that increasing the availability of such metadata depends on the appropriate involvement of researchers.
Research data challenges span areas of interest and activities traditionally owned by multiple research support pillars, including the library, records, archive and IT functions of universities, and more recently eResearch functions.
Opportunities to gain efficiencies, economies of scale and quality improvements, all require coordinated action by these pillars in relation to each researcher's data as well as research data at a macro scale.
Improved alignment is also needed between institutions, research funders and other third parties, such as those created by national infrastructure investment in data.

Those universities that are facing the problem at scale, want to explore with all universities and other prominent holders of research data, how to accelerate a change in practice and to enable research communities as a whole to ‘buy in’ to rationalisations in the treatment of their research data.

A simplified summary was developed during the conversation to date, in which the main institutional data challenges are seen to have both Yin and Yang dimensions. The diagram summarises that key finding:

Data management goals and practice need to encompass Preservation, Sharing and Re-use as well as Resourcing, Sensitivity and End-of-Life, if research institutions and research communities are to more effectively manage research data.

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