Improving the efficiency of your research
Good research data management practices ensure that researchers and institutions are able to meet their obligations to funders, improve the efficiency of research, and ensure data is findable, accessible, interoperable, trusted, and reusable. Research data management is a joint responsibility between researchers and institutions. That is why it’s important to understand what institutions need to consider when formulating data management frameworks and strategies.
You can read more about the importance of data management or find out how researchers can manage their data in the most effective way possible below.
What to consider when using data management frameworks and strategies
Fundamental to good research data management, policies and procedures are required tho address the ownership of research materials and data, their storage, their retention beyond the end of the project, and appropriate access to them by the research community.
Australian funding agencies have guidelines and requirements relating to research data management that address planning, dissemination and sharing, accessibility and reuse and storage.
Data with good metadata attached at the point of capture can expedite data sharing, publishing and citation.
A new version of a dataset may be created when an existing dataset is reprocessed, corrected or appended with additional data. Versioning is one means by which to track changes associated with ‘dynamic’ data that is not static over time.
Research data is increasingly seen as part of the corpus of scholarly publications. Publishing data means that the data is discoverable, adequately documented, publicly available, and citable to virtually everyone.
Outreach programs designed to encourage student and community contribution, creating new skills and roles to meet the growing demand for a data savvy workforce.
Using data visualisations in your collections has the potential to increase the reuse, discovery and connectivity of your research data.
The process of documenting where a piece of data comes from and methodology by which it is produced. Answering the questions why and how the data was produced, where and when and by whom.
Your data management toolkit
We've put together a list of handy guides and tools to help you ensure you're always up to date on best practices for data management.