Data Management Framework
Good research data management practices ensure that researchers and institutions are able to meet their obligations to funders, improve the efficiency of research, and make data available for sharing, validation and re-use. To support these goals, it is imperative that research data management is done properly from the outset; through the stages of planning, collection, analysis, publication, archiving and later re-use.
The Data Management Framework is underpinned by a number of principles:
- Data management is essential to support the evolving global data-intensive research environment
- Data management is an essential part of doing good research and supporting the research community of which each researcher is a part
- Data management will help each researcher in making effective use of their data
- The individual institutional data management framework is in accordance with the Australian Code for the Responsible Conduct of Research and other external legal and regulatory frameworks
- The institution will support all aspects of the data lifecycle, through creation and collection, storage, manipulation, sharing and collaboration, publishing, archiving and re-use
- Effective data management is best achieved through teamwork and collaboration between researchers, research offices, information specialists and technical support staff.
Institutional Data Management Framework
There are many things to consider when assessing what is required for data management at an institution.
- The Data Management Framework outlines the basic elements required within an institutional context to support effective data management. The elements are set out in four separate categories:
- Institutional policy and procedures
- IT Infrastructure: the hardware, software and other facilities which underpin data-related activities
- Support services: people and other means of providing advice and support, such as web-pages, research data interviews
- Metadata management: so that data records can be used for both internal and external purposes
- The Capability Maturity Model shows five levels of attainment or maturity which institutions may achieve in managing their research data. Organisations can use it as a guide to assessing their current level of attainment and identifying areas where they may wish to concentrate in the future. In this way the model can serve as a form of gap analysis.
- UK Community Capability Model for Data-Intensive Research. The ultimate aim of this UKOLN-Microsoft ResearchConnections project is to provide a framework that is useful for researchers and funders in modelling a range of disciplinary and community behaviours with respect to the adoption, usage, development and exploitation of cyber-infrastructure for data-intensive research.
- Creating a Data management framework - ANDS Guide
- Capability maturity model - ANDS Guide
- Data curation continuum - ANDS Guide
- What is research data? - ANDS Guide
- Digital object identifiers - ANDS Guide
- Costs and Benefits of Data Provision
- Full list of ANDS Guides