Research Data Management Framework for Institutions

Australia now has a national framework for institutional research data management (RDM), which can help universities and research organisations create a data-rich institutional approach to research.

Research data management (RDM) is a complex challenge for institutions. It requires many stakeholders to work together. This challenge is becoming more pressing as:

  • the quantity of research data generated within institutions is increasing
  • institutions are moving towards increasingly open research practices, with a focus on data availability for both integrity assurance and reuse.

Institutions are also facing financial pressure to improve the efficiency of their RDM as storage costs are no longer decreasing fast enough to allow all research data to be stored indefinitely.

Introducing the RDM Framework for Institutions

To help institutions address the challenges around RDM, the ARDC has partnered with 25 Australian universities in the Institutional Underpinnings program to:

  • facilitate a more cohesive, collaborative approach to RDM across Australia’s universities and research institutions more generally.
  • support an overall uplift in their RDM capability in line with the responsibilities outlined under the Australian code for the responsible conduct of research.

This joint effort culminated in a national framework for institutional RDM. Highlighting 19 elements essential for RDM, 9 of which were identified for immediate action, the framework helps institutions:

  • design RDM policy, procedures, infrastructure and services
  • improve coordination of RDM within and between institutions.

The framework is intended specifically for Australian universities to use but may be useful to other research institutions. It provides an institutional-level perspective and was informed by key stakeholders across relevant business units at each university.

RDM Elements for Immediate Focus

The framework features 19 elements essential for RDM, 9 of which were identified by the universities participating in the program as presenting opportunities for immediate collaborative action. Expert working groups have made recommendations and calls-to-action for each of these 9 elements, summarised as follows:

This addresses institutional approaches to providing the infrastructure for managing research data during the life of the research project. 

Institutions have a responsibility under the Australian code for the responsible conduct of research to give researchers access to infrastructure to use throughout active RDM. This part of the framework deals specifically with selecting the active RDM infrastructure by an institution. Because active RDM takes place during the life of the research project, it has a large impact on a researcher’s ability to conduct their research. Effective active RDM solutions reduce the burden on researchers’ workload and prevent them from turning to non-endorsed solutions that reduce institutional oversight and expose both institution and researcher to risk.

This is the shifting of RDM practices within an institution towards more effective RDM.

This part of the framework includes both approaches to: 

  • changing institutional staff attitudes and practices
  • changing the institutional processes, guidance and incentive structures that motivate and support these attitudes and practices.

This lays out the principles that govern the institution’s approach to RDM.

Effective RDM policy gives an institution a structured approach to meeting its regulatory requirements and ensuring that the required roles, responsibilities, processes and procedures are in place for effective RDM.

This addresses institutional considerations for planning the management of data emerging from a research project. 

RDM planning ensures that researchers carefully consider the management of their research data, leading to better RDM practice within an institution. As they take the researcher through the planning process, institutions can introduce researchers to institutional infrastructure and processes. RDM planning may be documented by the researcher in a data management plan, which should help with institutional oversight of research data and can be used to inform the provision of RDM infrastructure and services.

This refers to the decisions about what to do with data at the end of a research project.

Institutions are responsible for large quantities of research data. They must retain data to: 

  • meet their regulatory requirements
  • back up the integrity of their research
  • maintain valuable data assets for future reuse.

Identifying the data that should be retained for these reasons and disposing of datasets that no longer need to be retained are complex challenges for institutions.

This can improve the visibility and impact of research.

The research sector is moving towards a more open model, where the data underlying research is made available in aid of research integrity, reproducibility, collaboration and innovation.

This is data that presents a risk to persons, groups, the environment or society at large if it is disclosed or mishandled. 

Special protections are required when managing sensitive research data within an institution. Institutions must therefore account for data sensitivity when putting in place RDM infrastructure and procedures.

This addresses institutional approaches to providing researchers with the essential knowledge to manage data effectively.

This is the process of reviewing data collections to assess their ongoing value and determine retention requirements. 

Effective appraisal requires sufficient information about: 

  • the data to be appraised
  • well-defined triggers for appraisal decisions
  • clarity within the institution about who has the responsibility to appraise data.

Other Important Elements for RDM

A further 10 elements were noted as priority areas but were not an immediate focus of the working groups. Nonetheless, considerations raised for these elements are also summarised in this framework:

  • data sharing and access
  • cybersecurity
  • data ownership
  • digital preservation
  • funding and sustainability
  • governance
  • identifiers and metadata
  • non-digital material
  • standards and guidelines
  • Indigenous data management.

Reusable Outputs from RDM Framework Projects

To validate, test and implement elements of the RDM Framework in the local contexts of the participating universities, a series of projects were created. The project generated outputs that can be reused by universities and research institutions. Learn more about the RDM framework projects and outputs.

Next Step

For more information, read the full Research Data Management Framework for Institutions:

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Last updated

20 February 2023



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