Monday, 2 September 2013

What is research data?

Trying to define what is research data is something of a challenge and there appears to be no clear consensus on a definition. What we do know is that research data means different things to different people in different contexts and that the definition varies depending on your subject discipline and research funder.

However, it is worth exploring some of these different definitions of research data. The University of Sheffield Research Data Management Policy defines 'data', as:
"...observational data, experimental data and data derived from analysis, independent of format."
The Engineering and Physical Sciences Research Council (EPSRC) define research data as:
"...recorded factual material commonly retained by and accepted in the scientific community as necessary to validate research findings; although the majority of such data is created in digital format, all research data is included irrespective of the format in which it is created."
What about the visual arts? How do they define research data? The distinctive and varied nature of research data in the visual arts was explored in depth by KAPTUR, a Jisc Managing Research Data Project led by the Visual Arts Data Service.  They define research data as:
"Research data can be described as data which arises out of, and evidences, research... Examples of visual arts research data may include sketchbooks, log books, sets of images, video recordings, trials, prototypes, ceramic glaze recipes, found objects, and correspondence."
Earlier this year I attend an event where Laura Molloy from the University of Glasgow provided one of the more succinct definitions of research data I have come across. She defined it as:

“The material underpinning a research assertion”

Research Data types 

Can include all of the following:

  • sketchbooks 
  • video recordings 
  • correspondence 
  • log books
  • test responses 
  • slides, artefacts, specimens, samples 
  • audiotapes, photographs, films 
  • models, algorithms, scripts 
  • questionnaires, transcripts, codebooks 
  • methodologies and workflows 
  • standard operating procedures and protocols
In our next posting we will start to reflect on why research data management (rdm) is important and outline the benefits of good practice in research data management. 

Further reading:

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