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Research Data Management Toolkit

This guide provides information about research data management and the Research Data JCU platform

Introduction: Data Wrangling

Data "wrangling" -- also known as data cleaning -- may be required at different stages of the Research Data Management Lifecycle. This process identifies and corrects errors or makes formatting more consistent. It is often required to prepare data for analysis and/or visualization.

Data also needs to be cleaned before archiving it to ensure it is preserved correctly, that it is not misinterpreted by other users, and to facilitate interoperability (one of the FAIR Data Principles). See the list of Resources for some useful tools and tutorials.  

Making It Easier to Re-Use Your Data

White et al. (2013) published an excellent paper "Nine simple ways to make it easier to (re)use your data". in Ideas in Ecology and Evolution. The authors noted that much of the shared data in ecology and evolutionary biology is not easily reused because they don't follow best practices in terms of data structure, metadata and licences.Their nine specific recommendations are:

  1. Share your data.
  2. Provide metadata.
  3. Provide an unprocessed form of the data.
  4. Use standard data formats.
  5. Use good null values.
  6. Make it easy to combine your data with other datasets
  7. Perform basic quality control.
  8. Use an established repository.
  9. Use an established and liberal license 

Their advice is on point and highly readable - and it includes some specifics on data wrangling.

Source: White, E., Baldridge, E., Brym, Z., Locey, K.,McGlinn, D. & and Supp, S. (2013) Nine simple ways to make it easier to (re)use your data. Ideas in Ecology and Evolution, 6(2):1-10 doi:10.4033/iee.2013.6b.6.f

Data Wrangling Resources

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