Welcome to the SU research data management (RDM) guide. This guide will provide information on various RDM components, practices, procedures, and regulations that govern the management of research data at SU, as well as available resources and tools that can used by researchers to adequately manage their data. This guide further intends to provide support to the SU community with aim to ensure effective management of research data throughout the data lifecycle.
What is research data management (RDM)?
RDM involves many small set practices. The reason that SU researchers are encouraged to engage in these RDM practices is to ensure that they do not get stuck without their data when they need it or end up spending too much time trying to reconstruct their research data and analysis. RDM can be described as a process consisting of two components:
1. Planning the way research data will be managed during and after the research process; and
2. Controlling the collection, processing, analysis, sharing, dissemination, curation and reuse of research data
Researchers lacking proper knowledge and tools for managing research data generated through scientific research projects may find it frustrating to generate meaningful analysis for their research. For many researchers, data management frustrations are not something new, however, the good news is that SU Library and Information Service provides better methods for managing their digital datasets through conscious data management. Outlined below are some of reasons why good data management practices are essential: