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 reuse. 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 reuse.
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 make effective use of their data.
- The institution's 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 reuse.
- 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 online toolkits, and research data interviews
- metadata management: so that data records can be used for both internal and external purposes.
And are assessed across 5 levels of maturity: Initial; Development; Defined; Managed; Optimised.
It also has an in depth analysis of the Capability Maturity Model which can be used when developing an institutional Data Management Framework 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.
- Building Blocks: Laying the Foundation for a Research Data Management Program from OCLC. This is intended for those who are just beginning to offer data services to researchers at their universities.
- Part 1 assumes that very little, if anything, is in place, and that resources are limited. It seeks to guide the individual who has data management program responsibilities in directions that will lay a very basic foundation.
- Part 2 helps identify steps for building on that foundation as needs become evident and as resources allow