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
Institutional data management frameworks
Institutional policies and procedures
Fundamental to good research data management, policies and procedures are required to 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 management plans
A Data Management Plan (DMP) typically outlines what research data will be created during the course of a research project, plans for sharing and preserving the data, and any restrictions that may need to be applied.
Careful thought about files at the beginning of a research project can save a lot of time, resources and money. Our handy guides delve into best practice around file formats and file naming conventions, making sure you get it right from the start.
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.
Data reuse, licensing and copyright
There are many reasons for sharing and enabling reuse of data, including promoting innovation and potential new data uses. How do you find and access data to reuse, and what can you do with it once you’ve got it?
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.