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Research Data Management (RDM): Research Data Management (RDM)

UCT Libraries Research Data Services provide guidance and support for all aspects of the data lifecycle, from planning your data management strategy during the proposal phase through preserving your data at the conclusion of your project.

What is Research Data?

It is universally acknowledged that researchers are interested in data of all kinds, regardless of origin or type.

Here are some of the recognised definitions of research data:

"Research data, unlike other types of information, is collected, observed, or created, for purposes of analysis to produce original research results." Edinburgh University Data Library Research Data Management Handbook  

“Research data means data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media”. The Queensland University of Technology Management of Research Data Policy

“The recorded information (regardless of the form or the media in which they may exist) necessary to support or validate a research project’s observations, findings or outputs”. The University of Oxford Policy on Management of Research Data and Records



What is Research Data Management (RDM)

Research data management (or RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access).

There are a host of reasons why research data management is important:

  • Data, like journal articles and books, is a scholarly product.
  • Data (especially digital data) is fragile and easily lost.
  • There are growing research data requirements imposed by funders and publishers.
  • Research data management saves time and resources in the long run.
  • Good management helps to prevent errors and increases the quality of your analyses.
  • Well-managed and accessible data allows others to validate and replicate findings.
  • Research data management facilitates sharing of research data and, when shared, data can lead to valuable discoveries by others outside of the original research team.

Why Research Data Management?

Satisfy funder requirements

Increasingly, funding bodies mandate the submission of a Data Management Plan (DMP) to ensure that data can be preserved and shared.

A detailed DMP will help you:

  • meet the requirements of your funding agency

  • secure funding

  • address preservation, documentation and verification issues.

  • Plan for and comply with the FAIR principles (Findable, Accessible, Interoperable and Reusable) for data sharing.

Many funder provide their own DMP templates. If your research funder doesn't have a DMP creation system, you are funded by the NRF, or do not have a research funder, you can use UCT Libraries’ UCT DMP.

Organise and understand your data

By managing your data, you make it easier to understand the details and procedures relating to your data and data collection throughout the life cycle of the project.

Good data management makes research easier

Increase citations and get recognition

The data you collect are the basis of your research. By managing your data you increase your chances of being recognised and cited by others.

Well-managed data ensures that your research can be reused and validated by others.

Enable reproducibility and growth in research output

Well-managed data is:

  • Findable

  • Accessible

  • Interoperable

  • Reusable.

These four components of well-managed research data are known together as the FAIR Data principles. If your data is well managed, time and costs of future research efforts are greatly reduced. It makes it easier for you (or your students, or colleagues) to be able to reuse the data in future studies, and when appropriate shareable with the broader research community future research output and facilitate new discoveries. Note that while the FAIR principles are geared towards open data publication, data can be FAIR-compliant without being made open.