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FAIR Data: Reusable

The FAIR Data Principles are a set of community developed guiding principles for making data Findable, Accessible, Interoperable, and Reusable.

Lots of documentation is needed to support data interpretation and reuse. The data should conform to community norms and be clearly licensed so others know what kinds of reuse are permitted.

  • Your data will have a clear and accessible data usage license.
  • Your data and metadata will meet relevant disciplinary standards.

Licensing your Data

If you publish your data in a data repository of your choice, a licence agreement will be applied to your data. A licence agreement is a legal arrangement between the creator/depositor of the data set and the data repository, signifying what a user is allowed to do with the data, stating clear re-use rights .

To make re-use as likely as possible we advise you to choose a licence which:

  • Makes data available to the widest audience possible;
  • Makes the widest range of uses possible.

In making your data available you will need to use a data licence to help others understand what they are allowed to do with your data.

Documentation to Support Data Interpretation & Reuse

  1. Do not panic. Much documentation is simply good research practice, so you are probably already doing much of it.
  2. Start early! Careful planning of your documentation at the beginning of your project helps you save time and effort. Do not leave the documentation for the very end of your project. Remember to include procedures for documentation in your data management planning.
  3. Think about the information that is needed in order to understand the data. What will other researchers and re-users need in order to understand your data?
  4. Create a separate documentation file for the data that includes the basic information about the data. You can also create similar files for each data set. Remember to organise your files so that there is a connection between the documentation file and the data sets.
  5. Plan where to deposit the data after the completion of the project. The repository probably follows a specific metadata standard that you can adopt.
  6. Document consistently throughout the project. Data documentation gives contextual information about your dataset(s). It specifies the aims and objectives of the original project and harbours explanatory material including the data source, data collection methodology and process, dataset structure and technical information. Rich and structured information helps you to identify a dataset and make choices about its content and usability.

Find a Metadata Standard for your Discipline