# reusability

> FAIR data principle

**Wikidata**: [Q115999420](https://www.wikidata.org/wiki/Q115999420)  
**Source**: https://4ort.xyz/entity/reusability-q115999420

## Summary
Reusability is the fourth FAIR Data Principle, established in 2016, that requires data and metadata to be sufficiently well-described and documented so that they can be reused in future research by humans and machines. It mandates that data be released with clear usage licenses, provenance information, and standards-compliant metadata to enable replication and integration with other datasets.

## Key Facts
- Inception year: 2016 as part of the FAIR Data Principles framework
- Position: Fourth of four FAIR principles (Findable, Accessible, Interoperable, Reusable)
- Exact match URI: https://w3id.org/fair/principles/terms/R
- Part of: FAIR Data Principles (P1545: 4)
- Multilingual aliases: reusable, 再利用性, リユーザビリティ, 再利用できる, リユーザブル
- Official description source: "The FAIR Guiding Principles for scientific data management and stewardship"
- Described at: https://w3id.org/fair/principles/latest/R (English language)

## FAQs
### Q: What does reusability mean in the FAIR context?
A: Reusability means that data and metadata must be richly described with accurate attributes so they can be replicated and combined with other datasets. This includes providing provenance, usage licenses, and standards-compliant metadata.

### Q: Is reusability only about making data available?
A: No. Reusability requires more than availability—it demands comprehensive documentation, clear licensing, provenance tracking, and adherence to domain standards so future researchers can understand and reuse the data correctly.

### Q: How does reusability relate to the other FAIR principles?
A: Reusability builds on the first three FAIR principles: data must first be findable, accessible, and interoperable before it can be effectively reused. Reusability adds the requirement for rich metadata and clear usage terms.

## Why It Matters
Reusability transforms data from single-use artifacts into long-term scientific assets. By requiring comprehensive metadata, provenance documentation, and clear licensing, reusability enables researchers to validate, replicate, and build upon existing work without needing to contact original authors or repeat experiments. This principle addresses the reproducibility crisis in science by ensuring that data remain interpretable and usable years after collection. Reusability accelerates scientific discovery by allowing datasets to be combined in novel ways, enabling meta-analyses, and supporting machine learning applications that require large, well-documented datasets. It reduces research costs by eliminating redundant data collection and enables new forms of interdisciplinary research that were previously impossible due to incompatible or undocumented datasets.

## Notable For
- One of only four core principles that define modern scientific data management standards
- Requires both human-readable and machine-actionable metadata for full automation
- Explicitly mandates usage licenses—a unique requirement among data management frameworks
- Addresses the complete data lifecycle from creation to long-term preservation
- Forms the foundation for data-driven AI and machine learning in research

## Body
### Definition and Scope
Reusability as defined by the FAIR principles requires that data and metadata be sufficiently well-described to enable replication and integration with other datasets. This principle goes beyond simple data availability to require comprehensive documentation that supports both human interpretation and machine processing.

### Technical Requirements
The reusability principle mandates several specific requirements:
- Rich metadata that accurately describes all data attributes
- Clear usage licenses that specify how data can be reused
- Provenance information documenting the data's origin and processing history
- Adherence to domain-relevant community standards
- Machine-readable formats that enable automated processing

### Implementation Context
Reusability was introduced in 2016 as part of the FAIR framework to address growing concerns about data loss and irreproducibility in scientific research. The principle recognizes that data value increases exponentially when properly documented and licensed for reuse, enabling new forms of collaborative and computational research.

## References

1. [Source](https://www.go-fair.org/fair-principles)