FAIR is a set of guiding principles for those involved in data management and organization. Outlined in Scientific Data, these principles provide a strategy to ensuring data is findable, accessible, interoperable and reusable. Check out the chart below for more on how to make your data FAIR. Applying these principles are key to writing data management and sharing plans.
| FAIR Principle | Definition | Measure to Ensure Compliance | |
| Findable |
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- Use standardized metadata formats (e.g., Dublin Core). - Assign persistent identifiers (e.g., DOI) to datasets. - Ensure metadata includes essential details (e.g., title, creator, date). |
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| Accessible | Data should be easily accessible with clear usage licenses. |
- Provide open access to data where possible. - Ensure data access protocols are clear and well-documented. - Use standard protocols and APIs for access (e.g., RESTful APIs) |
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| Interoperable |
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- Use standard data formats (e.g., CSV, JSON, XML). - Apply controlled vocabularies and ontologies for data elements. - Implement APIs or web services for data integration. |
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| Reusable | Data should be well-described and ready for reuse in different contexts. |
- Provide comprehensive metadata including provenance and context. - Clarify usage rights and licenses. - Ensure data is sufficiently documented for understanding and reuse. Use readme files when appropriate |
