Background

Improving the systematic management of research data is expected to be a journey, because:

  • Adoption will occur at different rates in different institutions;

  • The goals and approaches will evolve as understanding improves; and

  • Different beneficiaries will have different interests in the gains provided.

Multiple meetings held by the RDCC have confirmed a growing interest in addressing the challenge. A strong agreement about the current paradox in research data management practice exists, as follows.

Research Data Management Plans (RDMPs, as researcher provided information) created at research initiation are not used to inform subsequent decision making.

The contrary would be expected.  The continuous improvement of a ‘container of information around data, about that data’, targeting the capture of value from that data over the course of its lifetime, must be able to inform the decision making required by that data’s life cycles.

RDMPs previously focussed on the data layer. It is anticipated that decisions at the collection layer can reduce the labour required to manage data and facilitate a more cohesive response across a variety of institutional approaches to the management of research data. To support meeting these macro-perspective challenges, an RDMP-2.0 should focus on collection layer management. 

An RDMP-2.0 process is envisaged, whereby research data management would transition from a plan (waterfall) based approach to a continuous improvement-based approach. 

The RDMP-2.0 process would:

  • Translate best practice improvements arising from institutional efforts into common national practice by coordinating aligned activities;

  • Realise FAIR principles for data management through the harmonisation of metadata and improving the coherence of the multiple systems supporting access to that data; and

  • Improve efficiency and compliance within support “pillars” (archives, data privacy, eResearch, ethics, IT, library, records, the research office and security):

    • By using a machine-driven approach which leverages common metadata to inform better data management decisions as data progresses through its life cycles; and

    • By providing actionable reporting relating cost to benefit.

Challenges

The means for making improvements would need to be developed. Some indicative concepts are set out below.


Concept

Many government policy, university and national infrastructure goals could be advanced. For example: improved research integrity; more certain access to data underpinning published research, university held prestige collections, the flow of data into science agency collections and support for the data life cycles associated with data in all of the NCRIS capabilities.

An overall summary of the themes clustering around the development of the RDMP-2.0 is depicted below.