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Research Data Management: Home

This library guide outlines the basics of data management and creating a data management plan.

Unisa and Research Data Management - note to users of this guide

Welcome to the Unisa Research Data Management LibGuide. This Guide will provide information on processes, procedures and policy with regards to research data management (RDM), as well as access to resources and tools that can support researchers in managing their data.

It will also serve as a space to pilot platforms for archiving the research data

Please send any suggestions and comments to the compiler of this guide. 

Online Research Data Management Courses

MANTRA Online Research Data Management Course

MANTRA is a free online course for those who manage digital data as part of their research project.

Please click on the following links for an Online open access course for researchers and librarians: 




Funding Requirements

In order to promote open access to research data, many funding agencies require research data produced as a funded project to be made publicly available. Many funding agencies have stipulated requirements for data sharing and a formal data management plan.

National Research Foundation (NRF) Funding Requirement 

From 01 March 2015, authors of research papers generated from research either fully or partially funded by NRF, when submitting and publishing in academic journals, should deposit their final peer-reviewed manuscripts that have been accepted by the journals, to the administering Institution Repository with an embargo period of no more than 12 months. Earlier Open Access may be provided should this be allowed by the publisher. If the paper is published in an Open Access journal or the publisher allows the deposit of the published version in PDF format, such version should be deposited into the administering Institutional Repository and Open Access should be provided as soon as possible.

In addition, the data supporting the publication should be deposited in an accredited Open Access repository, with the provision of a Digital Object Identifier for future citation and referencing.

The NRF encourages its stakeholder community, including NRF’s Business Units and National Research Facilities, to:

  • Formulate detailed policies on Open Access of publications and data from its funded research;
  • Establish Open Access repositories; and
  • Support public access to the repositories through web search and retrieval according to international standards and best practice.

The NRF requires its relevant Business Units and National Research Facilities to actively collaborate with relevant governmental departments and public higher education and research institutions to facilitate Open Access to publications generated from publicly funded research. The NRF requires its stakeholder community to actively seek collaboration with the international scientific community to facilitate the Open Access of publications generated from publicly funded research across the world.

Full Statement on Open Access to Research Publications from the National Research Foundation (NRF)-Funded Research

The following is a selection of the core funding agencies for the potential researcher to consider:

South Africa



The benefits of Research Data Management include the following:

  • Fulfills funding body grant requirements, e.g. NRF,  see further under the tab Funder Requirements
  • Fulfills publisher requirements
  • Ensures research integrity and replication
  • Ensures research data and records are accurate, complete, authentic and reliable
  • Increases your research efficiency
  • Saves time and resources in the long run
  • Enhances data security and minimise the risk of data loss
  • Prevents duplication by enabling others to use your data
  • Complies with practices conducted in industry and commerce
  • Protects your institution from reputational, financial & legal risk

Progression from Data to Information to Knowledge

It is important to distinguish between data and information.  The following sourced from Data vs Information explains this difference:  

" ... Data are the facts or details from which information is derived.  Individual pieces of data are rarely useful alone.  For data to become information, data needs to put in context. ... " 

In turn, information allows us to expand our knowledge.  

The infographics on the second and third tab illustrate this further.

The following "Infogineering Model" illustrates the progression from data to information to knowledge.

Research Data & Research Data Management

Research data may be broadly described as "... data that is collected, observed, or created, for purposes of analysis to produce original research results."  (What is "Research Data")

Research data may be generated for different purposes and through different processes and may be divided into the following categories.  Each category may require a different type of data management plan. 


  • captured in real-time
  • usually irreplaceable
  • examples include: sensor readings, survey results, telemetry, sample data, neurological images


  • data from lab equipment
  • often reproducible (this can be expensive)
  • examples include: gene sequences, magnetic field readings


  • data generated from test models
  • models and metadata where the input is more important than the output data
  • examples include: climate models, economic models.

Derived or compiled

  • reproducible (expensive)
  • examples include: text and data mining, 3D models

Reference or Canonical

  • a (static or organic) conglomeration or collection of smaller (peer-reviewed) datasets
  • most probably published or curated. 
  • examples include: gene sequence databanks, chemical structures, spatial data portals. 

These data can come in many forms such as, text, numerical, multimedia, models, software, discipline specific (i.e., FITS in astronomy, CIF in chemistry), or instrument specific.

Sourced from:  Boston University Libraries - Research Data Management 

Research data management, also referred to as Data Management  is the process of controlling the data generated during a research project. The outcome is a usually a publication in the form of an article, report, thesis, dissertation and the like. 

Cartoon credit – Auke Herrema

Any research project  will require some level of data management.  Funding agencies are increasingly requiring researchers and scholars to plan and execute good data management practices.

Managing data or data management is an integral part of the research process.

It can be challenging particularly when studies involve several researchers and/or when studies are conducted from multiple locations.

How data is managed depends on the types of data involved, how data is collected and stored, and how it is used - throughout the research lifecycle.

The outcome of a research project  depends in part on how well the raw data is managed.

Managing data helps the researcher to organize research files and data for easier access and analysis. It helps ensure the quality of the research. It supports the published results of the work and, in the long term, helps ensure accountability in data analysis.

Effective data management practices include:

  • Designating the responsibilities of every individual involved in the study.
  • Determining how data will be stored and backed up.
  • Implementing the data management plan.
  • Deciding how data will be dealt with through each modification of the study.

Sourced from:  Penn State University Libraries - What is data management 


Due to contractual and licensing agreements, access to some content may be restricted to the Unisa community.

Inclusion in this LibGuide does not imply University nor library endorsement of any ideas expressed.