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Research Data Management: Home


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

Research Data Services Librarian

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Sizwe Ngcobo
Stellenbosch University Library
Private Bag X5036
South Africa
+27(0) 21 808 9978

Research data management Lifecycle

Why research data management is needed

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:

  • Research that is funded by the institutions flows back to the public through research data sharing initiatives.
  • Good RDM practices enable researchers to efficiently manage their research projects throughout the research process.
  • RDM is one of the main instruments used by research funders to issue mandates.
  • RDM practices ensure that researchers comply with both legal and ethical requirements.
  • Engaging in RDM practices enables reproducibility and reusability of research findings
  • Good RDM practices enables seamless validation of previously published research
  • RDM reduces duplication of research efforts, along with associated costs.

RDM Goals

Research data management is guaranteed to make meaningful difference in scientific research. SU researchers are encouraged to be cognisant of the following goals of research data management:

  • Assurance that research data are capable of supporting analysis.
  • Assurance that all operations performed on research data are traceable.
  • Facilitating use of research data by people other than those involved in their original collection, generation, management and analyse.

Definition of Research Data

Official Definition at Stellenbosch University:

Recorded information, obtained during a research process, regardless of form or the media on which it may be recorded. The term includes computer software (computer programmes, databases and documentation thereof), and records of scientific or technical nature. The term does not include information incidental to research administration such as financial, administrative, cost or pricing, or management information. In practice research data include both intangible data (statistics, findings, conclusions) and tangible data. Tangible data include, but are not limited to notes, printouts, electronic storage, photographs, slides, negatives, films, scans, images, autoradiograms, electro-physical recordings, gels, blots, spectra, cell lines, reagents, modified organisms, specimens, consent forms, case report forms, collected organisms and other materials that are relevant to the research project.

Stellenbosch University Research Data Management Regulations