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
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.
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.