Data analysis is the process of turning data into information or to answer questions. It is common for the data collection, capture and  analysis processes to involve academics and researchers working together. Collaboration both expands the question set that can be asked of data as well as potentially augmenting the data used to answer a particular question

Analysis could involve a variety of steps:

  • Data entry/capture, digitisation, transcription, translation
  • Data checking, validation, cleaning up or sanitising data
  • Deriving secondary data sets
  • Interpreting data
  • Anonymising data where necessary
  • Creating metadata which means describing your data so your future self or others knows where it came from, what it describes and how it could be used.‘


Much analysis in the digital age results in the creation of new files that contain new data. Effective collaboration creates a broader base of specialist knowledge being applied to research questions, allowing for more complex, efficient and effective analysis to be performed. Working collaboratively with other researchers requires the sharing of such files back and forth (and at the same time) as new discoveries are made. Managing the flow of data in this process becomes crucial so that the project is developing efficiently and without duplication or loss of valuable knowledge.


Collaboration has never been easier than it is today, but this has brought its own complexities. UCT supports working with institutional instances of Microsoft Teams and with Google Drive . For a really sophisticated, research-focussed collaboration tool - an ‘RDM dashboard’ check out the UCT Open Science Framework (OSF) , and learn more about how to use it here .

We also have a Geographic Information Systems lab with GIS specialists that can be consulted by researchers working with spatial data.

Data analysis methods vary vastly over the spectrum of a Universities research outputs. At DLS we offer certain pointers around tools that help with analysis, including data visualisation. There are a variety of tools that enable collaborative work and support the tracking of versions of files as well as controlled access. They also help with the creation of valuable metadata to describe the workflow, ensuring that every step of the process is captured properly. Cloud based computing, with shared data stores and processing capabilities, has led to effective collaborations, even if teams are geographically scattered. DLS partners with eResearch  to provide such services to the UCT community.

Explore the RDM life cycle

Plan& design

Plan & Design

Preparing for your research project: this includes identifying what items (like data storage) need to be budgeted for and costing those, how data will be shared. All of this starts with a data management plan (DMP).
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Share & publish

Share & Publish

Publish both your data and your research output openly through one of the many general or discipline-specific platforms available to support open science.
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Manage, store & preserve

Manage & Store

Find out how best to manage, store and preserve your data to make sure you, and other researchers, can come back to it for reuse down the line.
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