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This month we are focusing on how to manage your data for future reusability and reproducability. Data reuse is an important facet of the research process that enables the verification of results, minimizes duplicate work, and builds on the work of others.

Preparing your data for reusability requires following data management best practices throughout the data collection process. Following the FAIR Principles will ensure your data is optimized for future reusability.

* Note: metadata refers to information resources that describe, explain, or otherwise make it easier to understand and use the data. In the bullets below, FAIR uses "(meta)data" to mean either metadata or data.


Findable

  • Dataset contains descriptive metadata
  • (Meta)data have a unique persistent identifier (e.g. DOI)
  • (Meta)data are registered and indexed for discoverability via web search engines


Accessible

  • (Meta)data are free and open to access
  • Metadata are accessible, even when data are no longer avaible


Interoperable

  • (Meta)data adhere to a standard schema with controlled vocabulary and are machine-readable
  • Datasets need to be properly cited and contain meaningful links between (meta)data


Reusable

  • (Meta)data are published with clear data usage terms and licensing

 

💬 Not sure where to start? Contact us! The services offered by the Research Data Service, including the Illinois Data Bank and data curation, follow the FAIR principles.

 
 
 

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