Research Data Management
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What is research data?

That which is collected, observed, or created for purposes of analysing to produce original research results.

 

- Definition from The University of Nottingham Research Data Management policy

Good practice in research data management is important for several reasons: 

  • it is beneficial to researchers and institutions, ensuring both are able to meet the expectations of the research funders. 
  • it can improve research efficiency and facilitates the availability of research for sharing, validation and re-use.           
 

Classification of research data

The Massachusetts Institute of Technology (MIT) has classified data into four types:

  • observational: data captured in real-time, usually irreplaceable, examples include neuroimages, survey data, sensor data, and sample data.
  • experimental: data from laboratory equipment, often reproducible, examples include gene sequences, and chromatograms.
  • simulation: data generated from test models where model and metadata (inputs) are more important than output data, examples include climate models and economic models.
  • derived or compiled: data that is reproducible, examples includes compiled database, 3D models, text and data mining.

Research data types

Research data types may 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

Research @ The University of Nottingham

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Lenton Lane
Nottingham, NG7 2NR

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