Data
Learning and adopting open research practices like version control, reproducible code, and code/data sharing make you not only a better researcher but a more efficient oneDr David Wilkinson, PhD Graduate 2020, The University of Melbourne
Considerations for making data open should happen early on, especially when working with sensitive datasets. The best approach to this is collecting de-identified data and integrating open data into your research data management planning. More on this is in the online, self-paced modules, Managing Data @ Melbourne.
By making your data open you;
- increase the transparency and verifiability of your research;
- generate another research output which, like research publications, can be cited;
- facilitate new research opportunities through data reuse; and
- meet funder and publisher requirements
Making data open can and must be done ethically and appropriately. There are various factors and options to consider, especially when working with sensitive datasets. It’s best to start thinking about these things early on. Some possible approaches include;
- integrating open data into your research data management planning;
- collecting as little sensitive information as possible, and only if it is strictly necessary for your research;
- considering whether anonymised or aggregated versions of your dataset can be shared openly;
- building consent to share your data into your participant consent forms; and
- considering different models of data sharing, such as mediated or restricted access, if fully open data is not suitable for your dataset
More on this is in the online, self-paced modules, Managing Data @Melbourne.
Further support
For more information on making your data as open as possible though as closed as necessary see the Open Research Library Guide
The Digital Stewardship (Research) team at Melbourne provides support, services, training, advice, and examples of good practice in data stewardship, digital preservation and research data management planning.