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Research Data Management

This guide provides information on processes, procedures and policies with Regards to Research Data Management (RDM) and lists some tools and resources that support RDM.

What is Research Data Management?

Research data management (RDM) involves organizing, documenting, storing, archiving, and sharing both digital and physical research data. Data management is essential across the entire research data life cycle. RDM aims to ensure the reliability of research results and encourages new and innovative research based on existing information. RDM is integrated into the research process to enhance efficiency. This LibGuide provides guidance on research data management planning, including strategies for data storage, protection, archiving, and other related resources. The Data Management Plan outlines the execution of these activities throughout the research project.

 

While research projects have their own lifespan, research data can have a life beyond  its'  original project. Managing your research data during collection, storage, analysis, publication and making provision for access to it after the conclusion of your project is crucial to the scientific community. 

F.A.I.R. Data Principles

Sharing data ensures that other researchers can access and use your data for further study. The FAIR data principles address the sharing of data by providing the following guidelines:

Findable: The research data record need to be discoverable by other researchers. Applying the appropriate description using general or subject specific metadata allows researchers to discover your data.

Accessible: Your data needs to be stored for the long term in an recognised storage facility such as a data repository. The data needs to be freely accessible and downloadable and useable.

Interoperable: Data and metadata needs to be written in a format that is accessible and can be interpreted by researchers and integrated with other data for analysis and processing. 

Re-useable: The goal is the optimum re-use of data. Data needs to be fully described in as much detail as possible including its provenance and using community or subject specific standards.

F.A.I.R. principles apply to three aspects; the data (digital object), the metadata (a description of that object) and the infrastructure / repository where the data is stored and from which it is shared or accessed.

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