FAIR++ Scoring
The FAIR++ Scoring tab provides a detailed assessment of a Data Product across multiple FAIR++ dimensions.
It provides an overall Data Product score, highlights areas for improvement, and provides a detailed breakdown of individual scoring dimensions.
FAIR++ Data Product Score
The FAIR++ Data Product Score represents the weighted overall score of the Data Product across the available FAIR++ dimensions.
The score is accompanied by an overall status, such as:
- Good
- Fair
- Poor
The status provides a quick indication of the Data Product's overall FAIR++ maturity.
Top 5 Proposed Improvements
The Top 5 Proposed Improvements section provides dynamically generated recommendations based on the Data Product's FAIR++ assessment.
These recommendations may vary between Data Products depending on their individual scores, metadata, configuration, governance practices, security controls, quality characteristics, and other available assessment information.
The recommendations highlight areas where improvements could have the greatest impact on the Data Product's FAIR++ assessment.
Examples of recommendations may include improvements related to:
- Data discoverability and metadata
- Access management and authorization
- Data quality and validation
- Security and audit controls
- Documentation and usage guidance
- Interoperability and integration
- Reusability and version management
- Ethics, compliance, and governance
The specific recommendations displayed are determined dynamically for each Data Product.
The recommendations can be used as actionable guidance for improving the Data Product.
FAIR++ Scoring Breakdown
The FAIR++ Scoring Breakdown provides a detailed assessment of the Data Product across five key dimensions. Each dimension contributes to the overall FAIR++ score.
| Dimension | What It Measures |
|---|---|
| Findable | How easily the Data Product can be discovered, located, and understood by authorized users. |
| Accessible | How easily authorized users can access the Data Product and its underlying data. |
| Interoperable | Compatibility with standards and the ability to work with other data systems and technologies. |
| Reusable | The potential for the Data Product to be reused across different use cases, teams, or applications. |
| Ethics / Compliance | Alignment with applicable ethical principles, organizational policies, regulatory requirements, and compliance standards. |
Each dimension receives an individual score, which contributes to the overall FAIR++ assessment of the Data Product.
