Helping researchers make sense of the complexity around data storage, description, re-use and archiving.
The research data landscape is evolving. International publishers and funders are now mandating for best practice in data planning, description, storage, and sharing. However commercial sensitivities still need to be understood and managed. This presentation covers general best-practice principles of management, storage and sharing of research data. It includes practical tips for improving data management practices that can be implemented immediately regardless of the type of data. By attending students will feel better prepared to respond to university, employer, funder and/or publisher data requirements.
Students will be able to:
- Think critically about best practice in the management, storage and sharing of research data, relating it to their discipline and research practices.
- Share and discuss personal data management experiences.
- Examine their current practices within conversations around F.A.I.R. data principles.
- Use the University of Otago Data Management Planning (DMP) tool.
The workshop is designed to be discipline agnostic. We believe the basics of good data management are universal regardless of discipline, level of study, or format of data. However, if you require a more tailored approach please see "Tailored Support" below.
Individual researchers can approach their Subject Librarian to talk about Research Data Management. Depending on the complexity of the question the Subject Librarian may refer a researcher to the Research Support Unit for specialised help.
The RSU is available to provide advice on defining and implementing your RDM strategy. The RSU can run advocacy and awareness raising programmes in your department or unit, training on creating Research Data Management plans, and support the publishing of data. When delivering tailored training and advice, the RSU works in partnership with a nominated researcher from the department or unit who provides discipline specific information on data generated, workflows and common software and instruments. This is vitally important to ensuring the training is relevant.