The FAIR data principles are meant to foster collaboration, data sharing, promote transparency and advance the integrity of research by making research data:
| Findable | Research data should be easy to find and discover. This involves assigning unique and persistent identifiers (e.g., DOIs or Handles) to datasets, as well as providing comprehensive metadata and descriptive information. Appropriate keywords will also enhance the findability of the data. |
| Accessible | Data should be openly available and accessible to researchers and interested parties. This means removing any technical, legal, or financial barriers that might prevent individuals from accessing the data. Data accessibility can be achieved through open licenses and data repositories. |
| Interoperable | The data should be structured in a way that allows seamless integration with other datasets and tools. This involves using standardised data formats and well-defined data models that facilitate data exchange and interoperability between different research systems. An important part of Interoperability is to make data ‘machine readable’ which will increase the findability and reusability of that data, but machine readable data will more readily processed and analysed by different tools. |
| Reusable | Data should be presented in a manner that enables researchers to reuse it for various purposes. This requires clear and unambiguous data documentation, along with adherence to best practices for data quality and consistency. By making data reusable, researchers can build upon existing findings and conduct more robust and impactful studies. |
This has been a brief overview of the FAIR Principles. It is recommended that the Go FAIR website is consulted for further information and reccomendations on making data FAIR.
UGent Open Science Knowledge clip: FAIR data principles (4:54)
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